Beautified MATLAB code (Unix newline convention + Emacs indentation), except: AIM, swz, particle
git-svn-id: https://www.dynare.org/svn/dynare/trunk@3250 ac1d8469-bf42-47a9-8791-bf33cf982152time-shift
sebastien
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e6f1a53e60
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502e3e1df8
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function [dr,aimcode,rts]=dynAIMsolver1(jacobia_,M_,dr)
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function [dr,aimcode,rts]=dynAIMsolver1(jacobia_,M_,dr)
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% function [dr,aimcode]=dynAIMsolver1(jacobia_,M_,dr)
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% function [dr,aimcode]=dynAIMsolver1(jacobia_,M_,dr)
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% Maps Dynare jacobia to AIM 1st order model solver designed and developed by Gary ANderson
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% Maps Dynare jacobia to AIM 1st order model solver designed and developed by Gary ANderson
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% and derives the solution for gy=dr.hgx and gu=dr.hgu from the AIM outputs
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% and derives the solution for gy=dr.hgx and gu=dr.hgu from the AIM outputs
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% AIM System is given as a sum:
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% AIM System is given as a sum:
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% i.e. for i=-$...+& SUM(Hi*xt+i)= £*zt, t = 0, . . . ,?
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% i.e. for i=-$...+& SUM(Hi*xt+i)= £*zt, t = 0, . . . ,?
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% and its input as single array of matrices: [H-$... Hi ... H+&]
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% and its input as single array of matrices: [H-$... Hi ... H+&]
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% and its solution as xt=SUM( Bi*xt+i) + @*£*zt for i=-$...-1
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% and its solution as xt=SUM( Bi*xt+i) + @*£*zt for i=-$...-1
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% with the output in form bb=[B-$... Bi ... B-1] and @=inv(Ho+H1*B-1)
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% with the output in form bb=[B-$... Bi ... B-1] and @=inv(Ho+H1*B-1)
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% Dynare jacobian = [fy'-$... fy'i ... fy'+& fu']
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% Dynare jacobian = [fy'-$... fy'i ... fy'+& fu']
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% where [fy'-$... fy'i ... fy'+&]=[H-$... Hi ... H+&] and fu'= £
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% where [fy'-$... fy'i ... fy'+&]=[H-$... Hi ... H+&] and fu'= £
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%
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%
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% INPUTS
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% INPUTS
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% jacobia_ [matrix] 1st order derivative of the model
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% jacobia_ [matrix] 1st order derivative of the model
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% dr [matlab structure] Decision rules for stochastic simulations.
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% dr [matlab structure] Decision rules for stochastic simulations.
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% M_ [matlab structure] Definition of the model.
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% M_ [matlab structure] Definition of the model.
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%
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%
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% OUTPUTS
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% OUTPUTS
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% dr [matlab structure] Decision rules for stochastic simulations.
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% dr [matlab structure] Decision rules for stochastic simulations.
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% aimcode [integer] 1: the model defines variables uniquely
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% aimcode [integer] 1: the model defines variables uniquely
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% aimcode is resolved in AIMerr as
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% aimcode is resolved in AIMerr as
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% (c==1) e='Aim: unique solution.';
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% (c==1) e='Aim: unique solution.';
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% (c==2) e='Aim: roots not correctly computed by real_schur.';
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% (c==2) e='Aim: roots not correctly computed by real_schur.';
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% (c==3) e='Aim: too many big roots.';
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% (c==3) e='Aim: too many big roots.';
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% (c==35) e='Aim: too many big roots, and q(:,right) is singular.';
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% (c==35) e='Aim: too many big roots, and q(:,right) is singular.';
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% (c==4) e='Aim: too few big roots.';
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% (c==4) e='Aim: too few big roots.';
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% (c==45) e='Aim: too few big roots, and q(:,right) is singular.';
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% (c==45) e='Aim: too few big roots, and q(:,right) is singular.';
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% (c==5) e='Aim: q(:,right) is singular.';
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% (c==5) e='Aim: q(:,right) is singular.';
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% (c==61) e='Aim: too many exact shiftrights.';
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% (c==61) e='Aim: too many exact shiftrights.';
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% (c==62) e='Aim: too many numeric shiftrights.';
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% (c==62) e='Aim: too many numeric shiftrights.';
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% else e='Aimerr: return code not properly specified';
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% else e='Aimerr: return code not properly specified';
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%
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%
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% SPECIAL REQUIREMENTS
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% SPECIAL REQUIREMENTS
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% Dynare use:
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% Dynare use:
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% 1) the lognormal block in DR1 is being invoked for some models and changing
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% 1) the lognormal block in DR1 is being invoked for some models and changing
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% values of ghx and ghy. We need to return the AIM output
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% values of ghx and ghy. We need to return the AIM output
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% values before that block and run the block with the current returned values
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% values before that block and run the block with the current returned values
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% of gy (i.e. dr.ghx) and gu (dr.ghu) if it is needed even when the AIM is used
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% of gy (i.e. dr.ghx) and gu (dr.ghu) if it is needed even when the AIM is used
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% (it does not depend on mjdgges output).
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% (it does not depend on mjdgges output).
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%
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%
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% 2) passing in aa={Q'|1}*jacobia_ can produce ~ one order closer
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% 2) passing in aa={Q'|1}*jacobia_ can produce ~ one order closer
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% results to the Dynare solutiion then when if plain jacobia_ is passed,
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% results to the Dynare solutiion then when if plain jacobia_ is passed,
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% i.e. diff < e-14 for aa and diff < *e-13 for jacobia_ if Q' is used.
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% i.e. diff < e-14 for aa and diff < *e-13 for jacobia_ if Q' is used.
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%
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%
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% GP July 2008
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% GP July 2008
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% Copyright (C) 2008 Dynare Team
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% Copyright (C) 2008 Dynare Team
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%
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%
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% This file is part of Dynare.
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% This file is part of Dynare.
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%
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%
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% Dynare is free software: you can redistribute it and/or modify
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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% (at your option) any later version.
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%
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%
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% Dynare is distributed in the hope that it will be useful,
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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% GNU General Public License for more details.
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%
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%
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% You should have received a copy of the GNU General Public License
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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aimcode=-1;
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aimcode=-1;
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neq= size(jacobia_,1); % no of equations
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neq= size(jacobia_,1); % no of equations
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lags=M_.maximum_endo_lag; % no of lags and leads
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lags=M_.maximum_endo_lag; % no of lags and leads
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leads=M_.maximum_endo_lead;
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leads=M_.maximum_endo_lead;
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klen=(leads+lags+1); % total lenght
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klen=(leads+lags+1); % total lenght
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theAIM_H=zeros(neq, neq*klen); % alloc space
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theAIM_H=zeros(neq, neq*klen); % alloc space
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lli=M_.lead_lag_incidence';
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lli=M_.lead_lag_incidence';
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% "sparse" the compact jacobia into AIM H aray of matrices
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% "sparse" the compact jacobia into AIM H aray of matrices
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% without exogenous shoc
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% without exogenous shoc
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theAIM_H(:,find(lli(:)))=jacobia_(:,nonzeros(lli(:)));
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theAIM_H(:,find(lli(:)))=jacobia_(:,nonzeros(lli(:)));
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condn = 1.e-10;%SPAmalg uses this in zero tests
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condn = 1.e-10;%SPAmalg uses this in zero tests
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uprbnd = 1 + 1.e-6;%allow unit roots
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uprbnd = 1 + 1.e-6;%allow unit roots
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% forward only models - AIM must have at least 1 lead and 1 lag.
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% forward only models - AIM must have at least 1 lead and 1 lag.
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if lags ==0
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if lags ==0
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theAIM_H =[zeros(neq) theAIM_H];
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theAIM_H =[zeros(neq) theAIM_H];
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lags=1;
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lags=1;
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klen=(leads+lags+1);
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klen=(leads+lags+1);
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end
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end
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% backward looking only models
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% backward looking only models
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if leads ==0
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if leads ==0
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theAIM_H =[theAIM_H zeros(neq)];
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theAIM_H =[theAIM_H zeros(neq)];
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leads=1;
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leads=1;
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klen=(leads+lags+1);
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klen=(leads+lags+1);
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end
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end
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%disp('gensysToAMA:running ama');
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%disp('gensysToAMA:running ama');
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try % try to run AIM
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try % try to run AIM
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[bb,rts,ia,nexact,nnumeric,lgroots,aimcode] =...
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[bb,rts,ia,nexact,nnumeric,lgroots,aimcode] =...
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SPAmalg(theAIM_H,neq, lags,leads,condn,uprbnd);
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SPAmalg(theAIM_H,neq, lags,leads,condn,uprbnd);
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catch
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catch
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err = lasterror;
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err = lasterror;
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disp(['Dynare AIM Solver error:' sprintf('%s; ID:%s',err.message, err.identifier)]);
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disp(['Dynare AIM Solver error:' sprintf('%s; ID:%s',err.message, err.identifier)]);
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rethrow(lasterror);
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rethrow(lasterror);
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end
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end
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if aimcode==1 %if OK
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if aimcode==1 %if OK
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col_order=[];
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col_order=[];
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for i =1:lags
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for i =1:lags
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col_order=[((i-1)*neq)+dr.order_var' col_order];
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col_order=[((i-1)*neq)+dr.order_var' col_order];
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end
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end
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bb_ord= bb(dr.order_var,col_order); % derive ordered gy
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bb_ord= bb(dr.order_var,col_order); % derive ordered gy
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% variables are present in the state space at the lag at which they
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% variables are present in the state space at the lag at which they
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% appear and at all smaller lags. The are ordered from smaller to
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% appear and at all smaller lags. The are ordered from smaller to
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% higher lag (reversed order of M_.lead_lag_incidence rows for lagged
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% higher lag (reversed order of M_.lead_lag_incidence rows for lagged
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% variables)
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% variables)
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i_lagged_vars = flipud(cumsum(M_.lead_lag_incidence(1:M_.maximum_lag,dr.order_var),1))';
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i_lagged_vars = flipud(cumsum(M_.lead_lag_incidence(1:M_.maximum_lag,dr.order_var),1))';
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dr.ghx= bb_ord(:,find(i_lagged_vars(:))); % get columns reported in
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dr.ghx= bb_ord(:,find(i_lagged_vars(:))); % get columns reported in
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% Dynare solution
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% Dynare solution
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if M_.exo_nbr % if there are exogenous shocks then derive gu for the shocks:
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if M_.exo_nbr % if there are exogenous shocks then derive gu for the shocks:
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% get H0 and H+1=HM
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% get H0 and H+1=HM
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% theH0= theAIM_H (:,M_.maximum_endo_lag*neq+1: (M_.maximum_endo_lag+1)*neq);
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% theH0= theAIM_H (:,M_.maximum_endo_lag*neq+1: (M_.maximum_endo_lag+1)*neq);
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%theH0= theAIM_H (:,lags*neq+1: (lags+1)*neq);
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%theH0= theAIM_H (:,lags*neq+1: (lags+1)*neq);
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% theHP= theAIM_H (:,(M_.maximum_endo_lag+1)*neq+1: (M_.maximum_endo_lag+2)*neq);
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% theHP= theAIM_H (:,(M_.maximum_endo_lag+1)*neq+1: (M_.maximum_endo_lag+2)*neq);
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%theHP= theAIM_H (:,(lags+1)*neq+1: (lags+2)*neq);
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%theHP= theAIM_H (:,(lags+1)*neq+1: (lags+2)*neq);
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theAIM_Psi= - jacobia_(:, size(nonzeros(lli(:)))+1:end);%
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theAIM_Psi= - jacobia_(:, size(nonzeros(lli(:)))+1:end);%
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%? = inv(H0 + H1B1)
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%? = inv(H0 + H1B1)
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%phi= (theH0+theHP*sparse(bb(:,(lags-1)*neq+1:end)))\eye( neq);
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%phi= (theH0+theHP*sparse(bb(:,(lags-1)*neq+1:end)))\eye( neq);
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%AIM_ghu=phi*theAIM_Psi;
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%AIM_ghu=phi*theAIM_Psi;
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%dr.ghu =AIM_ghu(dr.order_var,:); % order gu
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%dr.ghu =AIM_ghu(dr.order_var,:); % order gu
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% Using AIM SPObstruct
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% Using AIM SPObstruct
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scof = SPObstruct(theAIM_H,bb,neq,lags,leads);
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scof = SPObstruct(theAIM_H,bb,neq,lags,leads);
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scof1= scof(:,(lags)*neq+1:end);
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scof1= scof(:,(lags)*neq+1:end);
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scof1= scof1(:,dr.order_var);
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scof1= scof1(:,dr.order_var);
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dr.ghu =scof1\theAIM_Psi;
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dr.ghu =scof1\theAIM_Psi;
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else
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else
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dr.ghu = [];
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dr.ghu = [];
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end
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end
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else
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else
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err=SPAimerr(aimcode);
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err=SPAimerr(aimcode);
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%warning('Error in AIM: aimcode=%d, erro=%s', aimcode, err);;
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%warning('Error in AIM: aimcode=%d, erro=%s', aimcode, err);;
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disp(['Error in AIM: aimcode=' sprintf('%d : %s',aimcode, err)]);
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disp(['Error in AIM: aimcode=' sprintf('%d : %s',aimcode, err)]);
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if aimcode < 1 || aimcode > 5 % too big exception, use mjdgges
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if aimcode < 1 || aimcode > 5 % too big exception, use mjdgges
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error('Error in AIM: aimcode=%d ; %s', aimcode, err);
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error('Error in AIM: aimcode=%d ; %s', aimcode, err);
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end
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end
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% if aimcode > 5
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% if aimcode > 5
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% disp(['Error in AIM: aimcode=' sprintf('%d : %s',aimcode, err)]);
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% disp(['Error in AIM: aimcode=' sprintf('%d : %s',aimcode, err)]);
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% aimcode=5;
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% aimcode=5;
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% end
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% end
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end
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end
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@ -1,215 +1,215 @@
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function [ErrorCode] = AnalyseComputationalEnviroment(DataInput)
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function [ErrorCode] = AnalyseComputationalEnviroment(DataInput)
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% Input/Output description:
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% Input/Output description:
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%
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%
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% DataInput is the strcture option_.parallel, with the follow fields:
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% DataInput is the strcture option_.parallel, with the follow fields:
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%
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%
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% Local Define the computation place: 1 is on local machine, 0 remote
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% Local Define the computation place: 1 is on local machine, 0 remote
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% PcName Intuitive: contain the computer name.
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% PcName Intuitive: contain the computer name.
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% NumCPU Intuitive: contain the CPU number.
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% NumCPU Intuitive: contain the CPU number.
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% user Intuitive: contain the use name for the PcName.
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% user Intuitive: contain the use name for the PcName.
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% passwd Intuitive: contain the password for the user name in PcName.
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% passwd Intuitive: contain the password for the user name in PcName.
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% RemoteDrive Drive used for Local/Remote computation (data exchange, etc) must be contain 'RemoteFolder'.
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% RemoteDrive Drive used for Local/Remote computation (data exchange, etc) must be contain 'RemoteFolder'.
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% RemoteFolder Folder in RemoteDrive used for Local/Remote computation.
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% RemoteFolder Folder in RemoteDrive used for Local/Remote computation.
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%
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%
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% This information is typed by the user using the *.mod file,
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% This information is typed by the user using the *.mod file,
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% the goal of this function is to check if it correct.
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% the goal of this function is to check if it correct.
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%
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%
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%
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%
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% The variable ErrorCode is initialized at 0. If there are non problems with
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% The variable ErrorCode is initialized at 0. If there are non problems with
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% Local, PcName connections,... in general with parallel software execution,
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% Local, PcName connections,... in general with parallel software execution,
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% the ErrorCode is unchanged, in the others cases 1, 2 , ... The values
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% the ErrorCode is unchanged, in the others cases 1, 2 , ... The values
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% table is below.
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% table is below.
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%
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%
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%
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%
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% Table for ErrorCode Values.
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% Table for ErrorCode Values.
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%
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%
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% ErrorCode -> 0 Initial Value -> No Error Detected!!!
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% ErrorCode -> 0 Initial Value -> No Error Detected!!!
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% ErrorCode -> > 1 When an error happens. The value 1, 2, 3... are
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% ErrorCode -> > 1 When an error happens. The value 1, 2, 3... are
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% used to specify the kind of error.
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% used to specify the kind of error.
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%
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%
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% Value 1: The variable 'Local' has a bad value!
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% Value 1: The variable 'Local' has a bad value!
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%
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%
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% Value 2: The variable 'NumCPU' has a bad value. Parallel Dynare
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% Value 2: The variable 'NumCPU' has a bad value. Parallel Dynare
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% require an input data like [s:d] with s<=d, in this case we
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% require an input data like [s:d] with s<=d, in this case we
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% have s>d!
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% have s>d!
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% 2.1 The user asks to use more CPU of those available.
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% 2.1 The user asks to use more CPU of those available.
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% 2.2 There are CPU not used!
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% 2.2 There are CPU not used!
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%
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%
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% Value 3: The remote computer is unreachable!!!
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% Value 3: The remote computer is unreachable!!!
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%
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%
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% Value 4: The user name and/or password is/are incorrect on the
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% Value 4: The user name and/or password is/are incorrect on the
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% remote computer!
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% remote computer!
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%
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%
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% Value 5: It is impossible write/read file on remote computer.
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% Value 5: It is impossible write/read file on remote computer.
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%
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%
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% Then at the point call of this function it is possible react in a best way, in accord
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% Then at the point call of this function it is possible react in a best way, in accord
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% with the ErrorCode.
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% with the ErrorCode.
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% Copyright (C) 2009 Dynare Team
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% Copyright (C) 2009 Dynare Team
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%
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%
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% This file is part of Dynare.
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% This file is part of Dynare.
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%
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%
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% Dynare is free software: you can redistribute it and/or modify
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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% (at your option) any later version.
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%
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%
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% Dynare is distributed in the hope that it will be useful,
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
ErrorCode=0;
|
ErrorCode=0;
|
||||||
|
|
||||||
|
|
||||||
% The function is composed by two main blocks, determined by the 'Local'
|
% The function is composed by two main blocks, determined by the 'Local'
|
||||||
% variable.
|
% variable.
|
||||||
|
|
||||||
if ((DataInput.Local == 0) |(DataInput.Local == 1))
|
if ((DataInput.Local == 0) |(DataInput.Local == 1))
|
||||||
% Continue it is Ok!
|
% Continue it is Ok!
|
||||||
else
|
else
|
||||||
ErrorCode=1;
|
ErrorCode=1;
|
||||||
return
|
return
|
||||||
|
|
||||||
end
|
end
|
||||||
|
|
||||||
|
|
||||||
%%%%%%%%%% Local Machine %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
%%%%%%%%%% Local Machine %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||||
|
|
||||||
% In this case we need to check only the variable 'NumCPU'.
|
% In this case we need to check only the variable 'NumCPU'.
|
||||||
|
|
||||||
% We run the parallel code on local computer, so the others fields are automatically
|
% We run the parallel code on local computer, so the others fields are automatically
|
||||||
% fixed by Dynare. Then the user can also fill them with wrong values.
|
% fixed by Dynare. Then the user can also fill them with wrong values.
|
||||||
|
|
||||||
if (DataInput.Local == 1)
|
if (DataInput.Local == 1)
|
||||||
|
|
||||||
yn=isempty(DataInput.NumCPU);
|
yn=isempty(DataInput.NumCPU);
|
||||||
|
|
||||||
if yn==1
|
if yn==1
|
||||||
ErrorCode=2;
|
ErrorCode=2;
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
|
|
||||||
% We look for the information on local computer hardware.
|
% We look for the information on local computer hardware.
|
||||||
|
|
||||||
si=[];
|
si=[];
|
||||||
de=[];
|
de=[];
|
||||||
|
|
||||||
[si de]=system(['psinfo \\']);
|
[si de]=system(['psinfo \\']);
|
||||||
|
|
||||||
RealNumCPU=-1;
|
RealNumCPU=-1;
|
||||||
RealNumCPU=GiveCPUnumber(de);
|
RealNumCPU=GiveCPUnumber(de);
|
||||||
|
|
||||||
% Trasforming the input data provided in a form [n1:n2] in a single numerical
|
% Trasforming the input data provided in a form [n1:n2] in a single numerical
|
||||||
% value.
|
% value.
|
||||||
|
|
||||||
DataInput.NumCPU=length(DataInput.NumCPU);
|
DataInput.NumCPU=length(DataInput.NumCPU);
|
||||||
|
|
||||||
if DataInput.NumCPU == RealNumCPU
|
if DataInput.NumCPU == RealNumCPU
|
||||||
% It is Ok!
|
% It is Ok!
|
||||||
end
|
end
|
||||||
|
|
||||||
if DataInput.NumCPU > RealNumCPU
|
if DataInput.NumCPU > RealNumCPU
|
||||||
ErrorCode=2.1;
|
ErrorCode=2.1;
|
||||||
|
|
||||||
end
|
end
|
||||||
if DataInput.NumCPU < RealNumCPU
|
if DataInput.NumCPU < RealNumCPU
|
||||||
ErrorCode=2.2;
|
ErrorCode=2.2;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
||||||
%%%%%%%%%% Remote Machine %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
%%%%%%%%%% Remote Machine %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||||
|
|
||||||
% In this case we need more sophisticated check.
|
% In this case we need more sophisticated check.
|
||||||
|
|
||||||
|
|
||||||
if (DataInput.Local == 0)
|
if (DataInput.Local == 0)
|
||||||
|
|
||||||
si=[];
|
si=[];
|
||||||
de=[];
|
de=[];
|
||||||
|
|
||||||
[si de]=system(['ping ', DataInput.PcName]);
|
[si de]=system(['ping ', DataInput.PcName]);
|
||||||
|
|
||||||
if si==1
|
if si==1
|
||||||
% It is impossiblie to be connected to the
|
% It is impossiblie to be connected to the
|
||||||
% remote computer.
|
% remote computer.
|
||||||
|
|
||||||
ErrorCode=3;
|
ErrorCode=3;
|
||||||
return;
|
return;
|
||||||
end
|
end
|
||||||
|
|
||||||
|
|
||||||
% -> IL CODICE SEGUENTE E' DA CONTROLLARE E VERIFICARE!
|
% -> IL CODICE SEGUENTE E' DA CONTROLLARE E VERIFICARE!
|
||||||
|
|
||||||
% The Local Machine can be connetted with Remote Computer.
|
% The Local Machine can be connetted with Remote Computer.
|
||||||
% Now we verify if user name and password are correct and if remote
|
% Now we verify if user name and password are correct and if remote
|
||||||
% drive and remote folder exist on the remote computer and it is
|
% drive and remote folder exist on the remote computer and it is
|
||||||
% possible to exchange data with them.
|
% possible to exchange data with them.
|
||||||
|
|
||||||
si=[];
|
si=[];
|
||||||
de=[];
|
de=[];
|
||||||
|
|
||||||
[si de]=system(['psinfo \\', DataInput.PcName, ' -u ',DataInput.user, ' -p ',DataInput.passwd ]);
|
[si de]=system(['psinfo \\', DataInput.PcName, ' -u ',DataInput.user, ' -p ',DataInput.passwd ]);
|
||||||
|
|
||||||
if si<0
|
if si<0
|
||||||
% It is possible to be connected to the remote computer but it is not usable because the user
|
% It is possible to be connected to the remote computer but it is not usable because the user
|
||||||
% name and/or password is/are incorrect.
|
% name and/or password is/are incorrect.
|
||||||
|
|
||||||
ErrorCodeComputer=4;
|
ErrorCodeComputer=4;
|
||||||
return;
|
return;
|
||||||
else
|
else
|
||||||
% Username and Password are correct!
|
% Username and Password are correct!
|
||||||
end
|
end
|
||||||
|
|
||||||
% Now we verify if it possible to exchange data with the remote
|
% Now we verify if it possible to exchange data with the remote
|
||||||
% computer:
|
% computer:
|
||||||
|
|
||||||
|
|
||||||
fid = fopen('Tracing.txt','w+');
|
fid = fopen('Tracing.txt','w+');
|
||||||
fclose (fid);
|
fclose (fid);
|
||||||
|
|
||||||
% ATTENZIONE: verificare perche sembra funzionare anche se il RemoteFolder non
|
% ATTENZIONE: verificare perche sembra funzionare anche se il RemoteFolder non
|
||||||
% esiste.
|
% esiste.
|
||||||
|
|
||||||
Status=movefile('Tracing.txt', ['\\',DataInput.PcName,'\',DataInput.RemoteDrive,'$\',DataInput.RemoteFolder]);
|
Status=movefile('Tracing.txt', ['\\',DataInput.PcName,'\',DataInput.RemoteDrive,'$\',DataInput.RemoteFolder]);
|
||||||
|
|
||||||
if Status==1
|
if Status==1
|
||||||
% Remote Drive/Folder exist on Remote computer and
|
% Remote Drive/Folder exist on Remote computer and
|
||||||
% it is possible to exchange data with him.
|
% it is possible to exchange data with him.
|
||||||
else
|
else
|
||||||
|
|
||||||
% Move file error!
|
% Move file error!
|
||||||
ErrorCodeComputer=5;
|
ErrorCodeComputer=5;
|
||||||
return;
|
return;
|
||||||
end
|
end
|
||||||
|
|
||||||
% At this point we can to analyze the remote computer hardware.
|
% At this point we can to analyze the remote computer hardware.
|
||||||
|
|
||||||
|
|
||||||
RealNumCPU=-1;
|
RealNumCPU=-1;
|
||||||
RealNumCPU=GiveCPUnumber(de);
|
RealNumCPU=GiveCPUnumber(de);
|
||||||
|
|
||||||
% Trasforming the input data provided in a form [n1:n2] in a single numerical
|
% Trasforming the input data provided in a form [n1:n2] in a single numerical
|
||||||
% value.
|
% value.
|
||||||
|
|
||||||
|
|
||||||
DataInput.NumCPU=length(DataInput.NumCPU);
|
DataInput.NumCPU=length(DataInput.NumCPU);
|
||||||
|
|
||||||
if DataInput.NumCPU == RealNumCPU
|
if DataInput.NumCPU == RealNumCPU
|
||||||
% It is Ok!
|
% It is Ok!
|
||||||
end
|
end
|
||||||
|
|
||||||
if DataInput.NumCPU > RealNumCPU
|
if DataInput.NumCPU > RealNumCPU
|
||||||
ErrorCode=2.1;
|
ErrorCode=2.1;
|
||||||
|
|
||||||
end
|
end
|
||||||
if DataInput.NumCPU < RealNumCPU
|
if DataInput.NumCPU < RealNumCPU
|
||||||
ErrorCode=2.2;
|
ErrorCode=2.2;
|
||||||
end
|
end
|
||||||
|
|
||||||
end
|
end
|
||||||
|
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -1,44 +1,44 @@
|
||||||
function DirectoryName = CheckPath(type)
|
function DirectoryName = CheckPath(type)
|
||||||
% Creates the subfolder "./M_.dname/type" if it does not exist yet.
|
% Creates the subfolder "./M_.dname/type" if it does not exist yet.
|
||||||
%
|
%
|
||||||
% INPUTS
|
% INPUTS
|
||||||
% type [string] Name of the subfolder.
|
% type [string] Name of the subfolder.
|
||||||
%
|
%
|
||||||
% OUTPUTS
|
% OUTPUTS
|
||||||
% none.
|
% none.
|
||||||
%
|
%
|
||||||
% SPECIAL REQUIREMENTS
|
% SPECIAL REQUIREMENTS
|
||||||
% none
|
% none
|
||||||
|
|
||||||
% Copyright (C) 2005-2009 Dynare Team
|
% Copyright (C) 2005-2009 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
% Dynare is free software: you can redistribute it and/or modify
|
||||||
% it under the terms of the GNU General Public License as published by
|
% it under the terms of the GNU General Public License as published by
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
% the Free Software Foundation, either version 3 of the License, or
|
||||||
% (at your option) any later version.
|
% (at your option) any later version.
|
||||||
%
|
%
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
% Dynare is distributed in the hope that it will be useful,
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
global M_
|
global M_
|
||||||
|
|
||||||
DirectoryName = [ M_.dname '/' type ];
|
DirectoryName = [ M_.dname '/' type ];
|
||||||
|
|
||||||
if ~isdir(M_.dname)
|
if ~isdir(M_.dname)
|
||||||
% Make sure there isn't a file with the same name, see trac ticket #47
|
% Make sure there isn't a file with the same name, see trac ticket #47
|
||||||
delete(M_.dname)
|
delete(M_.dname)
|
||||||
mkdir('.', M_.dname);
|
mkdir('.', M_.dname);
|
||||||
end
|
end
|
||||||
|
|
||||||
if ~isdir(DirectoryName)
|
if ~isdir(DirectoryName)
|
||||||
% Make sure there isn't a file with the same name, see trac ticket #47
|
% Make sure there isn't a file with the same name, see trac ticket #47
|
||||||
delete(DirectoryName)
|
delete(DirectoryName)
|
||||||
mkdir('.',DirectoryName);
|
mkdir('.',DirectoryName);
|
||||||
end
|
end
|
||||||
|
|
|
||||||
|
|
@ -1,177 +1,177 @@
|
||||||
function [alphahat,etahat,atilde, aK] = DiffuseKalmanSmoother1(T,R,Q,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf)
|
function [alphahat,etahat,atilde, aK] = DiffuseKalmanSmoother1(T,R,Q,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf)
|
||||||
|
|
||||||
% function [alphahat,etahat,a, aK] = DiffuseKalmanSmoother1(T,R,Q,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf)
|
% function [alphahat,etahat,a, aK] = DiffuseKalmanSmoother1(T,R,Q,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf)
|
||||||
% Computes the diffuse kalman smoother without measurement error, in the case of a non-singular var-cov matrix
|
% Computes the diffuse kalman smoother without measurement error, in the case of a non-singular var-cov matrix
|
||||||
%
|
%
|
||||||
% INPUTS
|
% INPUTS
|
||||||
% T: mm*mm matrix
|
% T: mm*mm matrix
|
||||||
% R: mm*rr matrix
|
% R: mm*rr matrix
|
||||||
% Q: rr*rr matrix
|
% Q: rr*rr matrix
|
||||||
% Pinf1: mm*mm diagonal matrix with with q ones and m-q zeros
|
% Pinf1: mm*mm diagonal matrix with with q ones and m-q zeros
|
||||||
% Pstar1: mm*mm variance-covariance matrix with stationary variables
|
% Pstar1: mm*mm variance-covariance matrix with stationary variables
|
||||||
% Y: pp*1 vector
|
% Y: pp*1 vector
|
||||||
% trend
|
% trend
|
||||||
% pp: number of observed variables
|
% pp: number of observed variables
|
||||||
% mm: number of state variables
|
% mm: number of state variables
|
||||||
% smpl: sample size
|
% smpl: sample size
|
||||||
% mf: observed variables index in the state vector
|
% mf: observed variables index in the state vector
|
||||||
%
|
%
|
||||||
% OUTPUTS
|
% OUTPUTS
|
||||||
% alphahat: smoothed state variables (a_{t|T})
|
% alphahat: smoothed state variables (a_{t|T})
|
||||||
% etahat: smoothed shocks
|
% etahat: smoothed shocks
|
||||||
% atilde: matrix of updated variables (a_{t|t})
|
% atilde: matrix of updated variables (a_{t|t})
|
||||||
% aK: 3D array of k step ahead filtered state variables (a_{t+k|t})
|
% aK: 3D array of k step ahead filtered state variables (a_{t+k|t})
|
||||||
|
|
||||||
% SPECIAL REQUIREMENTS
|
% SPECIAL REQUIREMENTS
|
||||||
% See "Filtering and Smoothing of State Vector for Diffuse State Space
|
% See "Filtering and Smoothing of State Vector for Diffuse State Space
|
||||||
% Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
|
% Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
|
||||||
% Analysis, vol. 24(1), pp. 85-98).
|
% Analysis, vol. 24(1), pp. 85-98).
|
||||||
|
|
||||||
% Copyright (C) 2004-2008 Dynare Team
|
% Copyright (C) 2004-2008 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
% Dynare is free software: you can redistribute it and/or modify
|
||||||
% it under the terms of the GNU General Public License as published by
|
% it under the terms of the GNU General Public License as published by
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
% the Free Software Foundation, either version 3 of the License, or
|
||||||
% (at your option) any later version.
|
% (at your option) any later version.
|
||||||
%
|
%
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
% Dynare is distributed in the hope that it will be useful,
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
% modified by M. Ratto:
|
% modified by M. Ratto:
|
||||||
% new output argument aK (1-step to k-step predictions)
|
% new output argument aK (1-step to k-step predictions)
|
||||||
% new options_.nk: the max step ahed prediction in aK (default is 4)
|
% new options_.nk: the max step ahed prediction in aK (default is 4)
|
||||||
% new crit1 value for rank of Pinf
|
% new crit1 value for rank of Pinf
|
||||||
% it is assured that P is symmetric
|
% it is assured that P is symmetric
|
||||||
|
|
||||||
|
|
||||||
global options_
|
global options_
|
||||||
|
|
||||||
nk = options_.nk;
|
nk = options_.nk;
|
||||||
spinf = size(Pinf1);
|
spinf = size(Pinf1);
|
||||||
spstar = size(Pstar1);
|
spstar = size(Pstar1);
|
||||||
v = zeros(pp,smpl);
|
v = zeros(pp,smpl);
|
||||||
a = zeros(mm,smpl+1);
|
a = zeros(mm,smpl+1);
|
||||||
atilde = zeros(mm,smpl);
|
atilde = zeros(mm,smpl);
|
||||||
aK = zeros(nk,mm,smpl+1);
|
aK = zeros(nk,mm,smpl+1);
|
||||||
iF = zeros(pp,pp,smpl);
|
iF = zeros(pp,pp,smpl);
|
||||||
Fstar = zeros(pp,pp,smpl);
|
Fstar = zeros(pp,pp,smpl);
|
||||||
iFinf = zeros(pp,pp,smpl);
|
iFinf = zeros(pp,pp,smpl);
|
||||||
K = zeros(mm,pp,smpl);
|
K = zeros(mm,pp,smpl);
|
||||||
L = zeros(mm,mm,smpl);
|
L = zeros(mm,mm,smpl);
|
||||||
Linf = zeros(mm,mm,smpl);
|
Linf = zeros(mm,mm,smpl);
|
||||||
Kstar = zeros(mm,pp,smpl);
|
Kstar = zeros(mm,pp,smpl);
|
||||||
P = zeros(mm,mm,smpl+1);
|
P = zeros(mm,mm,smpl+1);
|
||||||
Pstar = zeros(spstar(1),spstar(2),smpl+1); Pstar(:,:,1) = Pstar1;
|
Pstar = zeros(spstar(1),spstar(2),smpl+1); Pstar(:,:,1) = Pstar1;
|
||||||
Pinf = zeros(spinf(1),spinf(2),smpl+1); Pinf(:,:,1) = Pinf1;
|
Pinf = zeros(spinf(1),spinf(2),smpl+1); Pinf(:,:,1) = Pinf1;
|
||||||
crit = options_.kalman_tol;
|
crit = options_.kalman_tol;
|
||||||
crit1 = 1.e-8;
|
crit1 = 1.e-8;
|
||||||
steady = smpl;
|
steady = smpl;
|
||||||
rr = size(Q,1);
|
rr = size(Q,1);
|
||||||
QQ = R*Q*transpose(R);
|
QQ = R*Q*transpose(R);
|
||||||
QRt = Q*transpose(R);
|
QRt = Q*transpose(R);
|
||||||
alphahat = zeros(mm,smpl);
|
alphahat = zeros(mm,smpl);
|
||||||
etahat = zeros(rr,smpl);
|
etahat = zeros(rr,smpl);
|
||||||
r = zeros(mm,smpl+1);
|
r = zeros(mm,smpl+1);
|
||||||
|
|
||||||
Z = zeros(pp,mm);
|
Z = zeros(pp,mm);
|
||||||
for i=1:pp;
|
for i=1:pp;
|
||||||
Z(i,mf(i)) = 1;
|
Z(i,mf(i)) = 1;
|
||||||
end
|
end
|
||||||
|
|
||||||
t = 0;
|
t = 0;
|
||||||
while rank(Pinf(:,:,t+1),crit1) & t<smpl
|
while rank(Pinf(:,:,t+1),crit1) & t<smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
v(:,t) = Y(:,t) - a(mf,t) - trend(:,t);
|
v(:,t) = Y(:,t) - a(mf,t) - trend(:,t);
|
||||||
if rcond(Pinf(mf,mf,t)) < crit
|
if rcond(Pinf(mf,mf,t)) < crit
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
iFinf(:,:,t) = inv(Pinf(mf,mf,t));
|
iFinf(:,:,t) = inv(Pinf(mf,mf,t));
|
||||||
PZI = Pinf(:,mf,t)*iFinf(:,:,t);
|
PZI = Pinf(:,mf,t)*iFinf(:,:,t);
|
||||||
atilde(:,t) = a(:,t) + PZI*v(:,t);
|
atilde(:,t) = a(:,t) + PZI*v(:,t);
|
||||||
Kinf(:,:,t) = T*PZI;
|
Kinf(:,:,t) = T*PZI;
|
||||||
a(:,t+1) = T*atilde(:,t);
|
a(:,t+1) = T*atilde(:,t);
|
||||||
aK(1,:,t+1) = a(:,t+1);
|
aK(1,:,t+1) = a(:,t+1);
|
||||||
for jnk=2:nk,
|
for jnk=2:nk,
|
||||||
aK(jnk,:,t+jnk) = T^(jnk-1)*a(:,t+1);
|
aK(jnk,:,t+jnk) = T^(jnk-1)*a(:,t+1);
|
||||||
end
|
end
|
||||||
Linf(:,:,t) = T - Kinf(:,:,t)*Z;
|
Linf(:,:,t) = T - Kinf(:,:,t)*Z;
|
||||||
Fstar(:,:,t) = Pstar(mf,mf,t);
|
Fstar(:,:,t) = Pstar(mf,mf,t);
|
||||||
Kstar(:,:,t) = (T*Pstar(:,mf,t)-Kinf(:,:,t)*Fstar(:,:,t))*iFinf(:,:,t);
|
Kstar(:,:,t) = (T*Pstar(:,mf,t)-Kinf(:,:,t)*Fstar(:,:,t))*iFinf(:,:,t);
|
||||||
Pstar(:,:,t+1) = T*Pstar(:,:,t)*transpose(T)-T*Pstar(:,mf,t)*transpose(Kinf(:,:,t))-Kinf(:,:,t)*Pinf(mf,mf,t)*transpose(Kstar(:,:,t)) + QQ;
|
Pstar(:,:,t+1) = T*Pstar(:,:,t)*transpose(T)-T*Pstar(:,mf,t)*transpose(Kinf(:,:,t))-Kinf(:,:,t)*Pinf(mf,mf,t)*transpose(Kstar(:,:,t)) + QQ;
|
||||||
Pinf(:,:,t+1) = T*Pinf(:,:,t)*transpose(T)-T*Pinf(:,mf,t)*transpose(Kinf(:,:,t));
|
Pinf(:,:,t+1) = T*Pinf(:,:,t)*transpose(T)-T*Pinf(:,mf,t)*transpose(Kinf(:,:,t));
|
||||||
end
|
end
|
||||||
d = t;
|
d = t;
|
||||||
P(:,:,d+1) = Pstar(:,:,d+1);
|
P(:,:,d+1) = Pstar(:,:,d+1);
|
||||||
iFinf = iFinf(:,:,1:d);
|
iFinf = iFinf(:,:,1:d);
|
||||||
Linf = Linf(:,:,1:d);
|
Linf = Linf(:,:,1:d);
|
||||||
Fstar = Fstar(:,:,1:d);
|
Fstar = Fstar(:,:,1:d);
|
||||||
Kstar = Kstar(:,:,1:d);
|
Kstar = Kstar(:,:,1:d);
|
||||||
Pstar = Pstar(:,:,1:d);
|
Pstar = Pstar(:,:,1:d);
|
||||||
Pinf = Pinf(:,:,1:d);
|
Pinf = Pinf(:,:,1:d);
|
||||||
notsteady = 1;
|
notsteady = 1;
|
||||||
while notsteady & t<smpl
|
while notsteady & t<smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
v(:,t) = Y(:,t) - a(mf,t) - trend(:,t);
|
v(:,t) = Y(:,t) - a(mf,t) - trend(:,t);
|
||||||
P(:,:,t)=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
|
P(:,:,t)=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
|
||||||
if rcond(P(mf,mf,t)) < crit
|
if rcond(P(mf,mf,t)) < crit
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
iF(:,:,t) = inv(P(mf,mf,t));
|
iF(:,:,t) = inv(P(mf,mf,t));
|
||||||
PZI = P(:,mf,t)*iF(:,:,t);
|
PZI = P(:,mf,t)*iF(:,:,t);
|
||||||
atilde(:,t) = a(:,t) + PZI*v(:,t);
|
atilde(:,t) = a(:,t) + PZI*v(:,t);
|
||||||
K(:,:,t) = T*PZI;
|
K(:,:,t) = T*PZI;
|
||||||
L(:,:,t) = T-K(:,:,t)*Z;
|
L(:,:,t) = T-K(:,:,t)*Z;
|
||||||
a(:,t+1) = T*atilde(:,t);
|
a(:,t+1) = T*atilde(:,t);
|
||||||
aK(1,:,t+1) = a(:,t+1);
|
aK(1,:,t+1) = a(:,t+1);
|
||||||
for jnk=2:nk,
|
for jnk=2:nk,
|
||||||
aK(jnk,:,t+jnk) = T^(jnk-1)*a(:,t+1);
|
aK(jnk,:,t+jnk) = T^(jnk-1)*a(:,t+1);
|
||||||
end
|
end
|
||||||
P(:,:,t+1) = T*P(:,:,t)*transpose(T)-T*P(:,mf,t)*transpose(K(:,:,t)) + QQ;
|
P(:,:,t+1) = T*P(:,:,t)*transpose(T)-T*P(:,mf,t)*transpose(K(:,:,t)) + QQ;
|
||||||
notsteady = ~(max(max(abs(P(:,:,t+1)-P(:,:,t))))<crit);
|
notsteady = ~(max(max(abs(P(:,:,t+1)-P(:,:,t))))<crit);
|
||||||
end
|
end
|
||||||
if t<smpl
|
if t<smpl
|
||||||
PZI_s = PZI;
|
PZI_s = PZI;
|
||||||
K_s = K(:,:,t);
|
K_s = K(:,:,t);
|
||||||
iF_s = iF(:,:,t);
|
iF_s = iF(:,:,t);
|
||||||
P_s = P(:,:,t+1);
|
P_s = P(:,:,t+1);
|
||||||
t_steady = t+1;
|
t_steady = t+1;
|
||||||
P = cat(3,P(:,:,1:t),repmat(P(:,:,t),[1 1 smpl-t_steady+1]));
|
P = cat(3,P(:,:,1:t),repmat(P(:,:,t),[1 1 smpl-t_steady+1]));
|
||||||
iF = cat(3,iF(:,:,1:t),repmat(inv(P_s(mf,mf)),[1 1 smpl-t_steady+1]));
|
iF = cat(3,iF(:,:,1:t),repmat(inv(P_s(mf,mf)),[1 1 smpl-t_steady+1]));
|
||||||
L = cat(3,L(:,:,1:t),repmat(T-K_s*Z,[1 1 smpl-t_steady+1]));
|
L = cat(3,L(:,:,1:t),repmat(T-K_s*Z,[1 1 smpl-t_steady+1]));
|
||||||
K = cat(3,K(:,:,1:t),repmat(T*P_s(:,mf)*iF_s,[1 1 smpl-t_steady+1]));
|
K = cat(3,K(:,:,1:t),repmat(T*P_s(:,mf)*iF_s,[1 1 smpl-t_steady+1]));
|
||||||
end
|
end
|
||||||
while t<smpl
|
while t<smpl
|
||||||
t=t+1;
|
t=t+1;
|
||||||
v(:,t) = Y(:,t) - a(mf,t) - trend(:,t);
|
v(:,t) = Y(:,t) - a(mf,t) - trend(:,t);
|
||||||
atilde(:,t) = a(:,t) + PZI*v(:,t);
|
atilde(:,t) = a(:,t) + PZI*v(:,t);
|
||||||
a(:,t+1) = T*a(:,t) + K_s*v(:,t);
|
a(:,t+1) = T*a(:,t) + K_s*v(:,t);
|
||||||
aK(1,:,t+1) = a(:,t+1);
|
aK(1,:,t+1) = a(:,t+1);
|
||||||
for jnk=2:nk,
|
for jnk=2:nk,
|
||||||
aK(jnk,:,t+jnk) = T^(jnk-1)*a(:,t+1);
|
aK(jnk,:,t+jnk) = T^(jnk-1)*a(:,t+1);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
t = smpl+1;
|
t = smpl+1;
|
||||||
while t>d+1
|
while t>d+1
|
||||||
t = t-1;
|
t = t-1;
|
||||||
r(:,t) = Z'*iF(:,:,t)*v(:,t) + L(:,:,t)'*r(:,t+1);
|
r(:,t) = Z'*iF(:,:,t)*v(:,t) + L(:,:,t)'*r(:,t+1);
|
||||||
alphahat(:,t) = a(:,t) + P(:,:,t)*r(:,t);
|
alphahat(:,t) = a(:,t) + P(:,:,t)*r(:,t);
|
||||||
etahat(:,t) = QRt*r(:,t);
|
etahat(:,t) = QRt*r(:,t);
|
||||||
end
|
end
|
||||||
if d
|
if d
|
||||||
r0 = zeros(mm,d+1);
|
r0 = zeros(mm,d+1);
|
||||||
r0(:,d+1) = r(:,d+1);
|
r0(:,d+1) = r(:,d+1);
|
||||||
r1 = zeros(mm,d+1);
|
r1 = zeros(mm,d+1);
|
||||||
for t = d:-1:1
|
for t = d:-1:1
|
||||||
r0(:,t) = Linf(:,:,t)'*r0(:,t+1);
|
r0(:,t) = Linf(:,:,t)'*r0(:,t+1);
|
||||||
r1(:,t) = Z'*(iFinf(:,:,t)*v(:,t)-Kstar(:,:,t)'*r0(:,t+1)) + Linf(:,:,t)'*r1(:,t+1);
|
r1(:,t) = Z'*(iFinf(:,:,t)*v(:,t)-Kstar(:,:,t)'*r0(:,t+1)) + Linf(:,:,t)'*r1(:,t+1);
|
||||||
alphahat(:,t) = a(:,t) + Pstar(:,:,t)*r0(:,t) + Pinf(:,:,t)*r1(:,t);
|
alphahat(:,t) = a(:,t) + Pstar(:,:,t)*r0(:,t) + Pinf(:,:,t)*r1(:,t);
|
||||||
etahat(:,t) = QRt*r0(:,t);
|
etahat(:,t) = QRt*r0(:,t);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
|
||||||
|
|
@ -1,208 +1,208 @@
|
||||||
function [alphahat,etahat,atilde,P,aK,PK,d,decomp] = DiffuseKalmanSmoother1_Z(T,Z,R,Q,Pinf1,Pstar1,Y,pp,mm,smpl)
|
function [alphahat,etahat,atilde,P,aK,PK,d,decomp] = DiffuseKalmanSmoother1_Z(T,Z,R,Q,Pinf1,Pstar1,Y,pp,mm,smpl)
|
||||||
|
|
||||||
% function [alphahat,etahat,a, aK] = DiffuseKalmanSmoother1(T,Z,R,Q,Pinf1,Pstar1,Y,pp,mm,smpl)
|
% function [alphahat,etahat,a, aK] = DiffuseKalmanSmoother1(T,Z,R,Q,Pinf1,Pstar1,Y,pp,mm,smpl)
|
||||||
% Computes the diffuse kalman smoother without measurement error, in the case of a non-singular var-cov matrix
|
% Computes the diffuse kalman smoother without measurement error, in the case of a non-singular var-cov matrix
|
||||||
%
|
%
|
||||||
% INPUTS
|
% INPUTS
|
||||||
% T: mm*mm matrix
|
% T: mm*mm matrix
|
||||||
% Z: pp*mm matrix
|
% Z: pp*mm matrix
|
||||||
% R: mm*rr matrix
|
% R: mm*rr matrix
|
||||||
% Q: rr*rr matrix
|
% Q: rr*rr matrix
|
||||||
% Pinf1: mm*mm diagonal matrix with with q ones and m-q zeros
|
% Pinf1: mm*mm diagonal matrix with with q ones and m-q zeros
|
||||||
% Pstar1: mm*mm variance-covariance matrix with stationary variables
|
% Pstar1: mm*mm variance-covariance matrix with stationary variables
|
||||||
% Y: pp*1 vector
|
% Y: pp*1 vector
|
||||||
% pp: number of observed variables
|
% pp: number of observed variables
|
||||||
% mm: number of state variables
|
% mm: number of state variables
|
||||||
% smpl: sample size
|
% smpl: sample size
|
||||||
%
|
%
|
||||||
% OUTPUTS
|
% OUTPUTS
|
||||||
% alphahat: smoothed variables (a_{t|T})
|
% alphahat: smoothed variables (a_{t|T})
|
||||||
% etahat: smoothed shocks
|
% etahat: smoothed shocks
|
||||||
% atilde: matrix of updated variables (a_{t|t})
|
% atilde: matrix of updated variables (a_{t|t})
|
||||||
% aK: 3D array of k step ahead filtered state variables (a_{t+k|t)
|
% aK: 3D array of k step ahead filtered state variables (a_{t+k|t)
|
||||||
% (meaningless for periods 1:d)
|
% (meaningless for periods 1:d)
|
||||||
% P: 3D array of one-step ahead forecast error variance
|
% P: 3D array of one-step ahead forecast error variance
|
||||||
% matrices
|
% matrices
|
||||||
% PK: 4D array of k-step ahead forecast error variance
|
% PK: 4D array of k-step ahead forecast error variance
|
||||||
% matrices (meaningless for periods 1:d)
|
% matrices (meaningless for periods 1:d)
|
||||||
% d: number of periods where filter remains in diffuse part
|
% d: number of periods where filter remains in diffuse part
|
||||||
% (should be equal to the order of integration of the model)
|
% (should be equal to the order of integration of the model)
|
||||||
% decomp: decomposition of the effect of shocks on filtered values
|
% decomp: decomposition of the effect of shocks on filtered values
|
||||||
%
|
%
|
||||||
% SPECIAL REQUIREMENTS
|
% SPECIAL REQUIREMENTS
|
||||||
% See "Filtering and Smoothing of State Vector for Diffuse State Space
|
% See "Filtering and Smoothing of State Vector for Diffuse State Space
|
||||||
% Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
|
% Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
|
||||||
% Analysis, vol. 24(1), pp. 85-98).
|
% Analysis, vol. 24(1), pp. 85-98).
|
||||||
|
|
||||||
% Copyright (C) 2004-2008 Dynare Team
|
% Copyright (C) 2004-2008 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
% Dynare is free software: you can redistribute it and/or modify
|
||||||
% it under the terms of the GNU General Public License as published by
|
% it under the terms of the GNU General Public License as published by
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
% the Free Software Foundation, either version 3 of the License, or
|
||||||
% (at your option) any later version.
|
% (at your option) any later version.
|
||||||
%
|
%
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
% Dynare is distributed in the hope that it will be useful,
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
% modified by M. Ratto:
|
% modified by M. Ratto:
|
||||||
% new output argument aK (1-step to k-step predictions)
|
% new output argument aK (1-step to k-step predictions)
|
||||||
% new options_.nk: the max step ahed prediction in aK (default is 4)
|
% new options_.nk: the max step ahed prediction in aK (default is 4)
|
||||||
% new crit1 value for rank of Pinf
|
% new crit1 value for rank of Pinf
|
||||||
% it is assured that P is symmetric
|
% it is assured that P is symmetric
|
||||||
|
|
||||||
|
|
||||||
global options_
|
global options_
|
||||||
|
|
||||||
d = 0;
|
d = 0;
|
||||||
decomp = [];
|
decomp = [];
|
||||||
nk = options_.nk;
|
nk = options_.nk;
|
||||||
spinf = size(Pinf1);
|
spinf = size(Pinf1);
|
||||||
spstar = size(Pstar1);
|
spstar = size(Pstar1);
|
||||||
v = zeros(pp,smpl);
|
v = zeros(pp,smpl);
|
||||||
a = zeros(mm,smpl+1);
|
a = zeros(mm,smpl+1);
|
||||||
atilde = zeros(mm,smpl);
|
atilde = zeros(mm,smpl);
|
||||||
aK = zeros(nk,mm,smpl+nk);
|
aK = zeros(nk,mm,smpl+nk);
|
||||||
PK = zeros(nk,mm,mm,smpl+nk);
|
PK = zeros(nk,mm,mm,smpl+nk);
|
||||||
iF = zeros(pp,pp,smpl);
|
iF = zeros(pp,pp,smpl);
|
||||||
Fstar = zeros(pp,pp,smpl);
|
Fstar = zeros(pp,pp,smpl);
|
||||||
iFinf = zeros(pp,pp,smpl);
|
iFinf = zeros(pp,pp,smpl);
|
||||||
K = zeros(mm,pp,smpl);
|
K = zeros(mm,pp,smpl);
|
||||||
L = zeros(mm,mm,smpl);
|
L = zeros(mm,mm,smpl);
|
||||||
Linf = zeros(mm,mm,smpl);
|
Linf = zeros(mm,mm,smpl);
|
||||||
Kstar = zeros(mm,pp,smpl);
|
Kstar = zeros(mm,pp,smpl);
|
||||||
P = zeros(mm,mm,smpl+1);
|
P = zeros(mm,mm,smpl+1);
|
||||||
Pstar = zeros(spstar(1),spstar(2),smpl+1); Pstar(:,:,1) = Pstar1;
|
Pstar = zeros(spstar(1),spstar(2),smpl+1); Pstar(:,:,1) = Pstar1;
|
||||||
Pinf = zeros(spinf(1),spinf(2),smpl+1); Pinf(:,:,1) = Pinf1;
|
Pinf = zeros(spinf(1),spinf(2),smpl+1); Pinf(:,:,1) = Pinf1;
|
||||||
crit = options_.kalman_tol;
|
crit = options_.kalman_tol;
|
||||||
crit1 = 1.e-8;
|
crit1 = 1.e-8;
|
||||||
steady = smpl;
|
steady = smpl;
|
||||||
rr = size(Q,1);
|
rr = size(Q,1);
|
||||||
QQ = R*Q*transpose(R);
|
QQ = R*Q*transpose(R);
|
||||||
QRt = Q*transpose(R);
|
QRt = Q*transpose(R);
|
||||||
alphahat = zeros(mm,smpl);
|
alphahat = zeros(mm,smpl);
|
||||||
etahat = zeros(rr,smpl);
|
etahat = zeros(rr,smpl);
|
||||||
r = zeros(mm,smpl+1);
|
r = zeros(mm,smpl+1);
|
||||||
|
|
||||||
t = 0;
|
t = 0;
|
||||||
while rank(Pinf(:,:,t+1),crit1) & t<smpl
|
while rank(Pinf(:,:,t+1),crit1) & t<smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
v(:,t)= Y(:,t) - Z*a(:,t);
|
v(:,t)= Y(:,t) - Z*a(:,t);
|
||||||
F = Z*Pinf(:,:,t)*Z';
|
F = Z*Pinf(:,:,t)*Z';
|
||||||
if rcond(F) < crit
|
if rcond(F) < crit
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
iFinf(:,:,t) = inv(F);
|
iFinf(:,:,t) = inv(F);
|
||||||
PZI = Pinf(:,:,t)*Z'*iFinf(:,:,t);
|
PZI = Pinf(:,:,t)*Z'*iFinf(:,:,t);
|
||||||
atilde(:,t) = a(:,t) + PZI*v(:,t);
|
atilde(:,t) = a(:,t) + PZI*v(:,t);
|
||||||
Kinf(:,:,t) = T*PZI;
|
Kinf(:,:,t) = T*PZI;
|
||||||
a(:,t+1) = T*atilde(:,t);
|
a(:,t+1) = T*atilde(:,t);
|
||||||
aK(1,:,t+1) = a(:,t+1);
|
aK(1,:,t+1) = a(:,t+1);
|
||||||
% isn't a meaningless as long as we are in the diffuse part? MJ
|
% isn't a meaningless as long as we are in the diffuse part? MJ
|
||||||
for jnk=2:nk,
|
for jnk=2:nk,
|
||||||
aK(jnk,:,t+jnk) = T^(jnk-1)*a(:,t+1);
|
aK(jnk,:,t+jnk) = T^(jnk-1)*a(:,t+1);
|
||||||
end
|
end
|
||||||
Linf(:,:,t) = T - Kinf(:,:,t)*Z;
|
Linf(:,:,t) = T - Kinf(:,:,t)*Z;
|
||||||
Fstar(:,:,t) = Z*Pstar(:,:,t)*Z';
|
Fstar(:,:,t) = Z*Pstar(:,:,t)*Z';
|
||||||
Kstar(:,:,t) = (T*Pstar(:,:,t)*Z'-Kinf(:,:,t)*Fstar(:,:,t))*iFinf(:,:,t);
|
Kstar(:,:,t) = (T*Pstar(:,:,t)*Z'-Kinf(:,:,t)*Fstar(:,:,t))*iFinf(:,:,t);
|
||||||
Pstar(:,:,t+1) = T*Pstar(:,:,t)*T'-T*Pstar(:,:,t)*Z'*Kinf(:,:,t)'-Kinf(:,:,t)*F*Kstar(:,:,t)' + QQ;
|
Pstar(:,:,t+1) = T*Pstar(:,:,t)*T'-T*Pstar(:,:,t)*Z'*Kinf(:,:,t)'-Kinf(:,:,t)*F*Kstar(:,:,t)' + QQ;
|
||||||
Pinf(:,:,t+1) = T*Pinf(:,:,t)*T'-T*Pinf(:,:,t)*Z'*Kinf(:,:,t)';
|
Pinf(:,:,t+1) = T*Pinf(:,:,t)*T'-T*Pinf(:,:,t)*Z'*Kinf(:,:,t)';
|
||||||
end
|
end
|
||||||
d = t;
|
d = t;
|
||||||
P(:,:,d+1) = Pstar(:,:,d+1);
|
P(:,:,d+1) = Pstar(:,:,d+1);
|
||||||
iFinf = iFinf(:,:,1:d);
|
iFinf = iFinf(:,:,1:d);
|
||||||
Linf = Linf(:,:,1:d);
|
Linf = Linf(:,:,1:d);
|
||||||
Fstar = Fstar(:,:,1:d);
|
Fstar = Fstar(:,:,1:d);
|
||||||
Kstar = Kstar(:,:,1:d);
|
Kstar = Kstar(:,:,1:d);
|
||||||
Pstar = Pstar(:,:,1:d);
|
Pstar = Pstar(:,:,1:d);
|
||||||
Pinf = Pinf(:,:,1:d);
|
Pinf = Pinf(:,:,1:d);
|
||||||
notsteady = 1;
|
notsteady = 1;
|
||||||
while notsteady & t<smpl
|
while notsteady & t<smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
v(:,t) = Y(:,t) - Z*a(:,t);
|
v(:,t) = Y(:,t) - Z*a(:,t);
|
||||||
P(:,:,t)=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
|
P(:,:,t)=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
|
||||||
F = Z*P(:,:,t)*Z';
|
F = Z*P(:,:,t)*Z';
|
||||||
if rcond(F) < crit
|
if rcond(F) < crit
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
iF(:,:,t) = inv(F);
|
iF(:,:,t) = inv(F);
|
||||||
PZI = P(:,:,t)*Z'*iF(:,:,t);
|
PZI = P(:,:,t)*Z'*iF(:,:,t);
|
||||||
atilde(:,t) = a(:,t) + PZI*v(:,t);
|
atilde(:,t) = a(:,t) + PZI*v(:,t);
|
||||||
K(:,:,t) = T*PZI;
|
K(:,:,t) = T*PZI;
|
||||||
L(:,:,t) = T-K(:,:,t)*Z;
|
L(:,:,t) = T-K(:,:,t)*Z;
|
||||||
a(:,t+1) = T*atilde(:,t);
|
a(:,t+1) = T*atilde(:,t);
|
||||||
Pf = P(:,:,t);
|
Pf = P(:,:,t);
|
||||||
for jnk=1:nk,
|
for jnk=1:nk,
|
||||||
Pf = T*Pf*T' + QQ;
|
Pf = T*Pf*T' + QQ;
|
||||||
aK(jnk,:,t+jnk) = T^jnk*atilde(:,t);
|
aK(jnk,:,t+jnk) = T^jnk*atilde(:,t);
|
||||||
PK(jnk,:,:,t+jnk) = Pf;
|
PK(jnk,:,:,t+jnk) = Pf;
|
||||||
end
|
end
|
||||||
P(:,:,t+1) = T*P(:,:,t)*T'-T*P(:,:,t)*Z'*K(:,:,t)' + QQ;
|
P(:,:,t+1) = T*P(:,:,t)*T'-T*P(:,:,t)*Z'*K(:,:,t)' + QQ;
|
||||||
notsteady = ~(max(max(abs(P(:,:,t+1)-P(:,:,t))))<crit);
|
notsteady = ~(max(max(abs(P(:,:,t+1)-P(:,:,t))))<crit);
|
||||||
end
|
end
|
||||||
if t<smpl
|
if t<smpl
|
||||||
PZI_s = PZI;
|
PZI_s = PZI;
|
||||||
K_s = K(:,:,t);
|
K_s = K(:,:,t);
|
||||||
iF_s = iF(:,:,t);
|
iF_s = iF(:,:,t);
|
||||||
P_s = P(:,:,t+1);
|
P_s = P(:,:,t+1);
|
||||||
P = cat(3,P(:,:,1:t),repmat(P_s,[1 1 smpl-t]));
|
P = cat(3,P(:,:,1:t),repmat(P_s,[1 1 smpl-t]));
|
||||||
iF = cat(3,iF(:,:,1:t),repmat(iF_s,[1 1 smpl-t]));
|
iF = cat(3,iF(:,:,1:t),repmat(iF_s,[1 1 smpl-t]));
|
||||||
L = cat(3,L(:,:,1:t),repmat(T-K_s*Z,[1 1 smpl-t]));
|
L = cat(3,L(:,:,1:t),repmat(T-K_s*Z,[1 1 smpl-t]));
|
||||||
K = cat(3,K(:,:,1:t),repmat(T*P_s*Z'*iF_s,[1 1 smpl-t]));
|
K = cat(3,K(:,:,1:t),repmat(T*P_s*Z'*iF_s,[1 1 smpl-t]));
|
||||||
end
|
end
|
||||||
while t<smpl
|
while t<smpl
|
||||||
t=t+1;
|
t=t+1;
|
||||||
v(:,t) = Y(:,t) - Z*a(:,t);
|
v(:,t) = Y(:,t) - Z*a(:,t);
|
||||||
atilde(:,t) = a(:,t) + PZI*v(:,t);
|
atilde(:,t) = a(:,t) + PZI*v(:,t);
|
||||||
a(:,t+1) = T*atilde(:,t);
|
a(:,t+1) = T*atilde(:,t);
|
||||||
Pf = P(:,:,t);
|
Pf = P(:,:,t);
|
||||||
for jnk=1:nk,
|
for jnk=1:nk,
|
||||||
Pf = T*Pf*T' + QQ;
|
Pf = T*Pf*T' + QQ;
|
||||||
aK(jnk,:,t+jnk) = T^jnk*atilde(:,t);
|
aK(jnk,:,t+jnk) = T^jnk*atilde(:,t);
|
||||||
PK(jnk,:,:,t+jnk) = Pf;
|
PK(jnk,:,:,t+jnk) = Pf;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
t = smpl+1;
|
t = smpl+1;
|
||||||
while t>d+1
|
while t>d+1
|
||||||
t = t-1;
|
t = t-1;
|
||||||
r(:,t) = Z'*iF(:,:,t)*v(:,t) + L(:,:,t)'*r(:,t+1);
|
r(:,t) = Z'*iF(:,:,t)*v(:,t) + L(:,:,t)'*r(:,t+1);
|
||||||
alphahat(:,t) = a(:,t) + P(:,:,t)*r(:,t);
|
alphahat(:,t) = a(:,t) + P(:,:,t)*r(:,t);
|
||||||
etahat(:,t) = QRt*r(:,t);
|
etahat(:,t) = QRt*r(:,t);
|
||||||
end
|
end
|
||||||
if d
|
if d
|
||||||
r0 = zeros(mm,d+1);
|
r0 = zeros(mm,d+1);
|
||||||
r0(:,d+1) = r(:,d+1);
|
r0(:,d+1) = r(:,d+1);
|
||||||
r1 = zeros(mm,d+1);
|
r1 = zeros(mm,d+1);
|
||||||
for t = d:-1:1
|
for t = d:-1:1
|
||||||
r0(:,t) = Linf(:,:,t)'*r0(:,t+1);
|
r0(:,t) = Linf(:,:,t)'*r0(:,t+1);
|
||||||
r1(:,t) = Z'*(iFinf(:,:,t)*v(:,t)-Kstar(:,:,t)'*r0(:,t+1)) + Linf(:,:,t)'*r1(:,t+1);
|
r1(:,t) = Z'*(iFinf(:,:,t)*v(:,t)-Kstar(:,:,t)'*r0(:,t+1)) + Linf(:,:,t)'*r1(:,t+1);
|
||||||
alphahat(:,t) = a(:,t) + Pstar(:,:,t)*r0(:,t) + Pinf(:,:,t)*r1(:,t);
|
alphahat(:,t) = a(:,t) + Pstar(:,:,t)*r0(:,t) + Pinf(:,:,t)*r1(:,t);
|
||||||
etahat(:,t) = QRt*r0(:,t);
|
etahat(:,t) = QRt*r0(:,t);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
||||||
if nargout > 7
|
if nargout > 7
|
||||||
decomp = zeros(nk,mm,rr,smpl+nk);
|
decomp = zeros(nk,mm,rr,smpl+nk);
|
||||||
ZRQinv = inv(Z*QQ*Z');
|
ZRQinv = inv(Z*QQ*Z');
|
||||||
for t = max(d,1):smpl
|
for t = max(d,1):smpl
|
||||||
ri_d = Z'*iF(:,:,t)*v(:,t);
|
ri_d = Z'*iF(:,:,t)*v(:,t);
|
||||||
|
|
||||||
% calculate eta_tm1t
|
% calculate eta_tm1t
|
||||||
eta_tm1t = QRt*ri_d;
|
eta_tm1t = QRt*ri_d;
|
||||||
% calculate decomposition
|
% calculate decomposition
|
||||||
Ttok = eye(mm,mm);
|
Ttok = eye(mm,mm);
|
||||||
for h = 1:nk
|
for h = 1:nk
|
||||||
for j=1:rr
|
for j=1:rr
|
||||||
eta=zeros(rr,1);
|
eta=zeros(rr,1);
|
||||||
eta(j) = eta_tm1t(j);
|
eta(j) = eta_tm1t(j);
|
||||||
decomp(h,:,j,t+h) = T^(h-1)*P(:,:,t)*Z'*ZRQinv*Z*R*eta;
|
decomp(h,:,j,t+h) = T^(h-1)*P(:,:,t)*Z'*ZRQinv*Z*R*eta;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
|
||||||
|
|
@ -70,9 +70,9 @@ aK = zeros(nk,mm,smpl+nk);
|
||||||
PK = zeros(nk,mm,mm,smpl+nk);
|
PK = zeros(nk,mm,mm,smpl+nk);
|
||||||
|
|
||||||
if isempty(options_.diffuse_d),
|
if isempty(options_.diffuse_d),
|
||||||
smpl_diff = 1;
|
smpl_diff = 1;
|
||||||
else
|
else
|
||||||
smpl_diff=rank(Pinf1);
|
smpl_diff=rank(Pinf1);
|
||||||
end
|
end
|
||||||
|
|
||||||
Fstar = zeros(pp,smpl_diff);
|
Fstar = zeros(pp,smpl_diff);
|
||||||
|
|
@ -101,81 +101,81 @@ r = zeros(mm,smpl+1);
|
||||||
|
|
||||||
Z = zeros(pp,mm);
|
Z = zeros(pp,mm);
|
||||||
for i=1:pp;
|
for i=1:pp;
|
||||||
Z(i,mf(i)) = 1;
|
Z(i,mf(i)) = 1;
|
||||||
end
|
end
|
||||||
|
|
||||||
t = 0;
|
t = 0;
|
||||||
icc=0;
|
icc=0;
|
||||||
newRank = rank(Pinf(:,:,1),crit1);
|
newRank = rank(Pinf(:,:,1),crit1);
|
||||||
while newRank & t < smpl
|
while newRank & t < smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
a(:,t) = a1(:,t);
|
a(:,t) = a1(:,t);
|
||||||
Pstar(:,:,t)=tril(Pstar(:,:,t))+transpose(tril(Pstar(:,:,t),-1));
|
Pstar(:,:,t)=tril(Pstar(:,:,t))+transpose(tril(Pstar(:,:,t),-1));
|
||||||
Pinf(:,:,t)=tril(Pinf(:,:,t))+transpose(tril(Pinf(:,:,t),-1));
|
Pinf(:,:,t)=tril(Pinf(:,:,t))+transpose(tril(Pinf(:,:,t),-1));
|
||||||
Pstar1(:,:,t) = Pstar(:,:,t);
|
Pstar1(:,:,t) = Pstar(:,:,t);
|
||||||
Pinf1(:,:,t) = Pinf(:,:,t);
|
Pinf1(:,:,t) = Pinf(:,:,t);
|
||||||
for i=1:pp
|
for i=1:pp
|
||||||
v(i,t) = Y(i,t)-a(mf(i),t)-trend(i,t);
|
v(i,t) = Y(i,t)-a(mf(i),t)-trend(i,t);
|
||||||
Fstar(i,t) = Pstar(mf(i),mf(i),t);
|
Fstar(i,t) = Pstar(mf(i),mf(i),t);
|
||||||
Finf(i,t) = Pinf(mf(i),mf(i),t);
|
Finf(i,t) = Pinf(mf(i),mf(i),t);
|
||||||
Kstar(:,i,t) = Pstar(:,mf(i),t);
|
Kstar(:,i,t) = Pstar(:,mf(i),t);
|
||||||
if Finf(i,t) > crit & newRank, % original MJ: if Finf(i,t) > crit
|
if Finf(i,t) > crit & newRank, % original MJ: if Finf(i,t) > crit
|
||||||
icc=icc+1;
|
icc=icc+1;
|
||||||
Kinf(:,i,t) = Pinf(:,mf(i),t);
|
Kinf(:,i,t) = Pinf(:,mf(i),t);
|
||||||
Linf(:,:,i,t) = eye(mm) - Kinf(:,i,t)*Z(i,:)/Finf(i,t);
|
Linf(:,:,i,t) = eye(mm) - Kinf(:,i,t)*Z(i,:)/Finf(i,t);
|
||||||
L0(:,:,i,t) = (Kinf(:,i,t)*Fstar(i,t)/Finf(i,t) - Kstar(:,i,t))*Z(i,:)/Finf(i,t);
|
L0(:,:,i,t) = (Kinf(:,i,t)*Fstar(i,t)/Finf(i,t) - Kstar(:,i,t))*Z(i,:)/Finf(i,t);
|
||||||
a(:,t) = a(:,t) + Kinf(:,i,t)*v(i,t)/Finf(i,t);
|
a(:,t) = a(:,t) + Kinf(:,i,t)*v(i,t)/Finf(i,t);
|
||||||
Pstar(:,:,t) = Pstar(:,:,t) + ...
|
Pstar(:,:,t) = Pstar(:,:,t) + ...
|
||||||
Kinf(:,i,t)*transpose(Kinf(:,i,t))*Fstar(i,t)/(Finf(i,t)*Finf(i,t)) - ...
|
Kinf(:,i,t)*transpose(Kinf(:,i,t))*Fstar(i,t)/(Finf(i,t)*Finf(i,t)) - ...
|
||||||
(Kstar(:,i,t)*transpose(Kinf(:,i,t)) +...
|
(Kstar(:,i,t)*transpose(Kinf(:,i,t)) +...
|
||||||
Kinf(:,i,t)*transpose(Kstar(:,i,t)))/Finf(i,t);
|
Kinf(:,i,t)*transpose(Kstar(:,i,t)))/Finf(i,t);
|
||||||
Pinf(:,:,t) = Pinf(:,:,t) - Kinf(:,i,t)*transpose(Kinf(:,i,t))/Finf(i,t);
|
Pinf(:,:,t) = Pinf(:,:,t) - Kinf(:,i,t)*transpose(Kinf(:,i,t))/Finf(i,t);
|
||||||
Pstar(:,:,t)=tril(Pstar(:,:,t))+transpose(tril(Pstar(:,:,t),-1));
|
Pstar(:,:,t)=tril(Pstar(:,:,t))+transpose(tril(Pstar(:,:,t),-1));
|
||||||
Pinf(:,:,t)=tril(Pinf(:,:,t))+transpose(tril(Pinf(:,:,t),-1));
|
Pinf(:,:,t)=tril(Pinf(:,:,t))+transpose(tril(Pinf(:,:,t),-1));
|
||||||
% new terminiation criteria by M. Ratto
|
% new terminiation criteria by M. Ratto
|
||||||
P0=Pinf(:,:,t);
|
P0=Pinf(:,:,t);
|
||||||
% newRank = any(diag(P0(mf,mf))>crit);
|
% newRank = any(diag(P0(mf,mf))>crit);
|
||||||
% if newRank==0, id = i; end,
|
% if newRank==0, id = i; end,
|
||||||
if ~isempty(options_.diffuse_d),
|
if ~isempty(options_.diffuse_d),
|
||||||
newRank = (icc<options_.diffuse_d);
|
newRank = (icc<options_.diffuse_d);
|
||||||
%if newRank & any(diag(P0(mf,mf))>crit)==0;
|
%if newRank & any(diag(P0(mf,mf))>crit)==0;
|
||||||
if newRank & (any(diag(P0(mf,mf))>crit)==0 & rank(P0,crit1)==0);
|
if newRank & (any(diag(P0(mf,mf))>crit)==0 & rank(P0,crit1)==0);
|
||||||
disp('WARNING!! Change in OPTIONS_.DIFFUSE_D in univariate DKF')
|
disp('WARNING!! Change in OPTIONS_.DIFFUSE_D in univariate DKF')
|
||||||
options_.diffuse_d = icc;
|
options_.diffuse_d = icc;
|
||||||
newRank=0;
|
newRank=0;
|
||||||
|
end
|
||||||
|
else
|
||||||
|
%newRank = any(diag(P0(mf,mf))>crit);
|
||||||
|
newRank = (any(diag(P0(mf,mf))>crit) | rank(P0,crit1));
|
||||||
|
if newRank==0,
|
||||||
|
options_.diffuse_d = icc;
|
||||||
|
end
|
||||||
|
end,
|
||||||
|
% if newRank==0,
|
||||||
|
% options_.diffuse_d=i; %this is buggy
|
||||||
|
% end
|
||||||
|
% end new terminiation criteria by M. Ratto
|
||||||
|
elseif Fstar(i,t) > crit
|
||||||
|
%% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition
|
||||||
|
%% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that
|
||||||
|
%% rank(Pinf)=0. [stéphane,11-03-2004].
|
||||||
|
Li(:,:,i,t) = eye(mm)-Kstar(:,i,t)*Z(i,:)/Fstar(i,t); % we need to store Li for DKF smoother
|
||||||
|
a(:,t) = a(:,t) + Kstar(:,i,t)*v(i,t)/Fstar(i,t);
|
||||||
|
Pstar(:,:,t) = Pstar(:,:,t) - Kstar(:,i,t)*transpose(Kstar(:,i,t))/Fstar(i,t);
|
||||||
|
Pstar(:,:,t)=tril(Pstar(:,:,t))+transpose(tril(Pstar(:,:,t),-1));
|
||||||
end
|
end
|
||||||
else
|
|
||||||
%newRank = any(diag(P0(mf,mf))>crit);
|
|
||||||
newRank = (any(diag(P0(mf,mf))>crit) | rank(P0,crit1));
|
|
||||||
if newRank==0,
|
|
||||||
options_.diffuse_d = icc;
|
|
||||||
end
|
|
||||||
end,
|
|
||||||
% if newRank==0,
|
|
||||||
% options_.diffuse_d=i; %this is buggy
|
|
||||||
% end
|
|
||||||
% end new terminiation criteria by M. Ratto
|
|
||||||
elseif Fstar(i,t) > crit
|
|
||||||
%% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition
|
|
||||||
%% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that
|
|
||||||
%% rank(Pinf)=0. [stéphane,11-03-2004].
|
|
||||||
Li(:,:,i,t) = eye(mm)-Kstar(:,i,t)*Z(i,:)/Fstar(i,t); % we need to store Li for DKF smoother
|
|
||||||
a(:,t) = a(:,t) + Kstar(:,i,t)*v(i,t)/Fstar(i,t);
|
|
||||||
Pstar(:,:,t) = Pstar(:,:,t) - Kstar(:,i,t)*transpose(Kstar(:,i,t))/Fstar(i,t);
|
|
||||||
Pstar(:,:,t)=tril(Pstar(:,:,t))+transpose(tril(Pstar(:,:,t),-1));
|
|
||||||
end
|
end
|
||||||
end
|
a1(:,t+1) = T*a(:,t);
|
||||||
a1(:,t+1) = T*a(:,t);
|
for jnk=1:nk,
|
||||||
for jnk=1:nk,
|
aK(jnk,:,t+jnk) = T^jnk*a(:,t);
|
||||||
aK(jnk,:,t+jnk) = T^jnk*a(:,t);
|
end
|
||||||
end
|
Pstar(:,:,t+1) = T*Pstar(:,:,t)*transpose(T)+ QQ;
|
||||||
Pstar(:,:,t+1) = T*Pstar(:,:,t)*transpose(T)+ QQ;
|
Pinf(:,:,t+1) = T*Pinf(:,:,t)*transpose(T);
|
||||||
Pinf(:,:,t+1) = T*Pinf(:,:,t)*transpose(T);
|
P0=Pinf(:,:,t+1);
|
||||||
P0=Pinf(:,:,t+1);
|
if newRank,
|
||||||
if newRank,
|
%newRank = any(diag(P0(mf,mf))>crit);
|
||||||
%newRank = any(diag(P0(mf,mf))>crit);
|
newRank = rank(P0,crit1);
|
||||||
newRank = rank(P0,crit1);
|
end
|
||||||
end
|
|
||||||
end
|
end
|
||||||
|
|
||||||
|
|
||||||
|
|
@ -192,96 +192,96 @@ Pstar1 = Pstar1(:,:,1:d);
|
||||||
Pinf1 = Pinf1(:,:,1:d);
|
Pinf1 = Pinf1(:,:,1:d);
|
||||||
notsteady = 1;
|
notsteady = 1;
|
||||||
while notsteady & t<smpl
|
while notsteady & t<smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
a(:,t) = a1(:,t);
|
a(:,t) = a1(:,t);
|
||||||
P(:,:,t)=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
|
P(:,:,t)=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
|
||||||
P1(:,:,t) = P(:,:,t);
|
P1(:,:,t) = P(:,:,t);
|
||||||
for i=1:pp
|
for i=1:pp
|
||||||
v(i,t) = Y(i,t) - a(mf(i),t) - trend(i,t);
|
v(i,t) = Y(i,t) - a(mf(i),t) - trend(i,t);
|
||||||
Fi(i,t) = P(mf(i),mf(i),t);
|
Fi(i,t) = P(mf(i),mf(i),t);
|
||||||
Ki(:,i,t) = P(:,mf(i),t);
|
Ki(:,i,t) = P(:,mf(i),t);
|
||||||
if Fi(i,t) > crit
|
if Fi(i,t) > crit
|
||||||
Li(:,:,i,t) = eye(mm)-Ki(:,i,t)*Z(i,:)/Fi(i,t);
|
Li(:,:,i,t) = eye(mm)-Ki(:,i,t)*Z(i,:)/Fi(i,t);
|
||||||
a(:,t) = a(:,t) + Ki(:,i,t)*v(i,t)/Fi(i,t);
|
a(:,t) = a(:,t) + Ki(:,i,t)*v(i,t)/Fi(i,t);
|
||||||
P(:,:,t) = P(:,:,t) - Ki(:,i,t)*transpose(Ki(:,i,t))/Fi(i,t);
|
P(:,:,t) = P(:,:,t) - Ki(:,i,t)*transpose(Ki(:,i,t))/Fi(i,t);
|
||||||
P(:,:,t)=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
|
P(:,:,t)=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
|
||||||
|
end
|
||||||
end
|
end
|
||||||
end
|
a1(:,t+1) = T*a(:,t);
|
||||||
a1(:,t+1) = T*a(:,t);
|
Pf = P(:,:,t);
|
||||||
Pf = P(:,:,t);
|
for jnk=1:nk,
|
||||||
for jnk=1:nk,
|
Pf = T*Pf*T' + QQ;
|
||||||
Pf = T*Pf*T' + QQ;
|
aK(jnk,:,t+jnk) = T^jnk*a(:,t);
|
||||||
aK(jnk,:,t+jnk) = T^jnk*a(:,t);
|
PK(jnk,:,:,t+jnk) = Pf;
|
||||||
PK(jnk,:,:,t+jnk) = Pf;
|
end
|
||||||
end
|
P(:,:,t+1) = T*P(:,:,t)*transpose(T) + QQ;
|
||||||
P(:,:,t+1) = T*P(:,:,t)*transpose(T) + QQ;
|
notsteady = ~(max(max(abs(P(:,:,t+1)-P(:,:,t))))<crit);
|
||||||
notsteady = ~(max(max(abs(P(:,:,t+1)-P(:,:,t))))<crit);
|
|
||||||
end
|
end
|
||||||
P_s=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
|
P_s=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
|
||||||
Fi_s = Fi(:,t);
|
Fi_s = Fi(:,t);
|
||||||
Ki_s = Ki(:,:,t);
|
Ki_s = Ki(:,:,t);
|
||||||
L_s =Li(:,:,:,t);
|
L_s =Li(:,:,:,t);
|
||||||
if t<smpl
|
if t<smpl
|
||||||
t_steady = t+1;
|
t_steady = t+1;
|
||||||
P = cat(3,P(:,:,1:t),repmat(P(:,:,t),[1 1 smpl-t_steady+1]));
|
P = cat(3,P(:,:,1:t),repmat(P(:,:,t),[1 1 smpl-t_steady+1]));
|
||||||
Fi = cat(2,Fi(:,1:t),repmat(Fi_s,[1 1 smpl-t_steady+1]));
|
Fi = cat(2,Fi(:,1:t),repmat(Fi_s,[1 1 smpl-t_steady+1]));
|
||||||
Li = cat(4,Li(:,:,:,1:t),repmat(L_s,[1 1 smpl-t_steady+1]));
|
Li = cat(4,Li(:,:,:,1:t),repmat(L_s,[1 1 smpl-t_steady+1]));
|
||||||
Ki = cat(3,Ki(:,:,1:t),repmat(Ki_s,[1 1 smpl-t_steady+1]));
|
Ki = cat(3,Ki(:,:,1:t),repmat(Ki_s,[1 1 smpl-t_steady+1]));
|
||||||
end
|
end
|
||||||
while t<smpl
|
while t<smpl
|
||||||
t=t+1;
|
t=t+1;
|
||||||
a(:,t) = a1(:,t);
|
a(:,t) = a1(:,t);
|
||||||
for i=1:pp
|
for i=1:pp
|
||||||
v(i,t) = Y(i,t) - a(mf(i),t) - trend(i,t);
|
v(i,t) = Y(i,t) - a(mf(i),t) - trend(i,t);
|
||||||
if Fi_s(i) > crit
|
if Fi_s(i) > crit
|
||||||
a(:,t) = a(:,t) + Ki_s(:,i)*v(i,t)/Fi_s(i);
|
a(:,t) = a(:,t) + Ki_s(:,i)*v(i,t)/Fi_s(i);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
a1(:,t+1) = T*a(:,t);
|
||||||
|
Pf = P(:,:,t);
|
||||||
|
for jnk=1:nk,
|
||||||
|
Pf = T*Pf*T' + QQ;
|
||||||
|
aK(jnk,:,t+jnk) = T^jnk*a(:,t);
|
||||||
|
PK(jnk,:,:,t+jnk) = Pf;
|
||||||
end
|
end
|
||||||
end
|
|
||||||
a1(:,t+1) = T*a(:,t);
|
|
||||||
Pf = P(:,:,t);
|
|
||||||
for jnk=1:nk,
|
|
||||||
Pf = T*Pf*T' + QQ;
|
|
||||||
aK(jnk,:,t+jnk) = T^jnk*a(:,t);
|
|
||||||
PK(jnk,:,:,t+jnk) = Pf;
|
|
||||||
end
|
|
||||||
end
|
end
|
||||||
ri=zeros(mm,1);
|
ri=zeros(mm,1);
|
||||||
t = smpl+1;
|
t = smpl+1;
|
||||||
while t>d+1
|
while t>d+1
|
||||||
t = t-1;
|
t = t-1;
|
||||||
for i=pp:-1:1
|
for i=pp:-1:1
|
||||||
if Fi(i,t) > crit
|
if Fi(i,t) > crit
|
||||||
ri = Z(i,:)'/Fi(i,t)*v(i,t)+Li(:,:,i,t)'*ri;
|
ri = Z(i,:)'/Fi(i,t)*v(i,t)+Li(:,:,i,t)'*ri;
|
||||||
|
end
|
||||||
end
|
end
|
||||||
end
|
r(:,t) = ri;
|
||||||
r(:,t) = ri;
|
alphahat(:,t) = a1(:,t) + P1(:,:,t)*r(:,t);
|
||||||
alphahat(:,t) = a1(:,t) + P1(:,:,t)*r(:,t);
|
etahat(:,t) = QRt*r(:,t);
|
||||||
etahat(:,t) = QRt*r(:,t);
|
ri = T'*ri;
|
||||||
ri = T'*ri;
|
|
||||||
end
|
end
|
||||||
if d
|
if d
|
||||||
r0 = zeros(mm,d);
|
r0 = zeros(mm,d);
|
||||||
r0(:,d) = ri;
|
r0(:,d) = ri;
|
||||||
r1 = zeros(mm,d);
|
r1 = zeros(mm,d);
|
||||||
for t = d:-1:1
|
for t = d:-1:1
|
||||||
for i=pp:-1:1
|
for i=pp:-1:1
|
||||||
if Finf(i,t) > crit & ~(t==d & i>options_.diffuse_d), % use of options_.diffuse_d to be sure of DKF termination
|
if Finf(i,t) > crit & ~(t==d & i>options_.diffuse_d), % use of options_.diffuse_d to be sure of DKF termination
|
||||||
%r1(:,t) = transpose(Z)*v(:,t)/Finf(i,t) + ... BUG HERE in transpose(Z)
|
%r1(:,t) = transpose(Z)*v(:,t)/Finf(i,t) + ... BUG HERE in transpose(Z)
|
||||||
r1(:,t) = Z(i,:)'*v(i,t)/Finf(i,t) + ...
|
r1(:,t) = Z(i,:)'*v(i,t)/Finf(i,t) + ...
|
||||||
L0(:,:,i,t)'*r0(:,t) + Linf(:,:,i,t)'*r1(:,t);
|
L0(:,:,i,t)'*r0(:,t) + Linf(:,:,i,t)'*r1(:,t);
|
||||||
r0(:,t) = Linf(:,:,i,t)'*r0(:,t);
|
r0(:,t) = Linf(:,:,i,t)'*r0(:,t);
|
||||||
elseif Fstar(i,t) > crit % step needed whe Finf == 0
|
elseif Fstar(i,t) > crit % step needed whe Finf == 0
|
||||||
r0(:,t) = Z(i,:)'/Fstar(i,t)*v(i,t)+Li(:,:,i,t)'*r0(:,t);
|
r0(:,t) = Z(i,:)'/Fstar(i,t)*v(i,t)+Li(:,:,i,t)'*r0(:,t);
|
||||||
end
|
end
|
||||||
|
end
|
||||||
|
alphahat(:,t) = a1(:,t) + Pstar1(:,:,t)*r0(:,t) + Pinf1(:,:,t)*r1(:,t);
|
||||||
|
r(:,t) = r0(:,t);
|
||||||
|
etahat(:,t) = QRt*r(:,t);
|
||||||
|
if t > 1
|
||||||
|
r0(:,t-1) = T'*r0(:,t);
|
||||||
|
r1(:,t-1) = T'*r1(:,t);
|
||||||
|
end
|
||||||
end
|
end
|
||||||
alphahat(:,t) = a1(:,t) + Pstar1(:,:,t)*r0(:,t) + Pinf1(:,:,t)*r1(:,t);
|
|
||||||
r(:,t) = r0(:,t);
|
|
||||||
etahat(:,t) = QRt*r(:,t);
|
|
||||||
if t > 1
|
|
||||||
r0(:,t-1) = T'*r0(:,t);
|
|
||||||
r1(:,t-1) = T'*r1(:,t);
|
|
||||||
end
|
|
||||||
end
|
|
||||||
end
|
end
|
||||||
|
|
||||||
if nargout > 7
|
if nargout > 7
|
||||||
|
|
|
||||||
|
|
@ -76,9 +76,9 @@ a1 = zeros(mm,smpl+1);
|
||||||
aK = zeros(nk,mm,smpl+nk);
|
aK = zeros(nk,mm,smpl+nk);
|
||||||
|
|
||||||
if isempty(options_.diffuse_d),
|
if isempty(options_.diffuse_d),
|
||||||
smpl_diff = 1;
|
smpl_diff = 1;
|
||||||
else
|
else
|
||||||
smpl_diff=rank(Pinf1);
|
smpl_diff=rank(Pinf1);
|
||||||
end
|
end
|
||||||
|
|
||||||
Fstar = zeros(pp,smpl_diff);
|
Fstar = zeros(pp,smpl_diff);
|
||||||
|
|
@ -111,67 +111,67 @@ t = 0;
|
||||||
icc=0;
|
icc=0;
|
||||||
newRank = rank(Pinf(:,:,1),crit1);
|
newRank = rank(Pinf(:,:,1),crit1);
|
||||||
while newRank & t < smpl
|
while newRank & t < smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
a(:,t) = a1(:,t);
|
a(:,t) = a1(:,t);
|
||||||
Pstar(:,:,t)=tril(Pstar(:,:,t))+tril(Pstar(:,:,t),-1)';
|
Pstar(:,:,t)=tril(Pstar(:,:,t))+tril(Pstar(:,:,t),-1)';
|
||||||
Pinf(:,:,t)=tril(Pinf(:,:,t))+tril(Pinf(:,:,t),-1)';
|
Pinf(:,:,t)=tril(Pinf(:,:,t))+tril(Pinf(:,:,t),-1)';
|
||||||
Pstar1(:,:,t) = Pstar(:,:,t);
|
Pstar1(:,:,t) = Pstar(:,:,t);
|
||||||
Pinf1(:,:,t) = Pinf(:,:,t);
|
Pinf1(:,:,t) = Pinf(:,:,t);
|
||||||
for i=1:pp
|
for i=1:pp
|
||||||
Zi = Z(i,:);
|
Zi = Z(i,:);
|
||||||
v(i,t) = Y(i,t)-Zi*a(:,t);
|
v(i,t) = Y(i,t)-Zi*a(:,t);
|
||||||
Fstar(i,t) = Zi*Pstar(:,:,t)*Zi';
|
Fstar(i,t) = Zi*Pstar(:,:,t)*Zi';
|
||||||
Finf(i,t) = Zi*Pinf(:,:,t)*Zi';
|
Finf(i,t) = Zi*Pinf(:,:,t)*Zi';
|
||||||
Kstar(:,i,t) = Pstar(:,:,t)*Zi';
|
Kstar(:,i,t) = Pstar(:,:,t)*Zi';
|
||||||
if Finf(i,t) > crit & newRank
|
if Finf(i,t) > crit & newRank
|
||||||
icc=icc+1;
|
icc=icc+1;
|
||||||
Kinf(:,i,t) = Pinf(:,:,t)*Zi';
|
Kinf(:,i,t) = Pinf(:,:,t)*Zi';
|
||||||
Linf(:,:,i,t) = eye(mm) - Kinf(:,i,t)*Z(i,:)/Finf(i,t);
|
Linf(:,:,i,t) = eye(mm) - Kinf(:,i,t)*Z(i,:)/Finf(i,t);
|
||||||
L0(:,:,i,t) = (Kinf(:,i,t)*Fstar(i,t)/Finf(i,t) - Kstar(:,i,t))*Zi/Finf(i,t);
|
L0(:,:,i,t) = (Kinf(:,i,t)*Fstar(i,t)/Finf(i,t) - Kstar(:,i,t))*Zi/Finf(i,t);
|
||||||
a(:,t) = a(:,t) + Kinf(:,i,t)*v(i,t)/Finf(i,t);
|
a(:,t) = a(:,t) + Kinf(:,i,t)*v(i,t)/Finf(i,t);
|
||||||
Pstar(:,:,t) = Pstar(:,:,t) + ...
|
Pstar(:,:,t) = Pstar(:,:,t) + ...
|
||||||
Kinf(:,i,t)*Kinf(:,i,t)'*Fstar(i,t)/(Finf(i,t)*Finf(i,t)) - ...
|
Kinf(:,i,t)*Kinf(:,i,t)'*Fstar(i,t)/(Finf(i,t)*Finf(i,t)) - ...
|
||||||
(Kstar(:,i,t)*Kinf(:,i,t)' +...
|
(Kstar(:,i,t)*Kinf(:,i,t)' +...
|
||||||
Kinf(:,i,t)*Kstar(:,i,t)')/Finf(i,t);
|
Kinf(:,i,t)*Kstar(:,i,t)')/Finf(i,t);
|
||||||
Pinf(:,:,t) = Pinf(:,:,t) - Kinf(:,i,t)*Kinf(:,i,t)'/Finf(i,t);
|
Pinf(:,:,t) = Pinf(:,:,t) - Kinf(:,i,t)*Kinf(:,i,t)'/Finf(i,t);
|
||||||
Pstar(:,:,t)=tril(Pstar(:,:,t))+tril(Pstar(:,:,t),-1)';
|
Pstar(:,:,t)=tril(Pstar(:,:,t))+tril(Pstar(:,:,t),-1)';
|
||||||
Pinf(:,:,t)=tril(Pinf(:,:,t))+tril(Pinf(:,:,t),-1)';
|
Pinf(:,:,t)=tril(Pinf(:,:,t))+tril(Pinf(:,:,t),-1)';
|
||||||
% new terminiation criteria by M. Ratto
|
% new terminiation criteria by M. Ratto
|
||||||
P0=Pinf(:,:,t);
|
P0=Pinf(:,:,t);
|
||||||
if ~isempty(options_.diffuse_d),
|
if ~isempty(options_.diffuse_d),
|
||||||
newRank = (icc<options_.diffuse_d);
|
newRank = (icc<options_.diffuse_d);
|
||||||
if newRank & (any(diag(Z*P0*Z')>crit)==0 & rank(P0,crit1)==0);
|
if newRank & (any(diag(Z*P0*Z')>crit)==0 & rank(P0,crit1)==0);
|
||||||
disp('WARNING!! Change in OPTIONS_.DIFFUSE_D in univariate DKF')
|
disp('WARNING!! Change in OPTIONS_.DIFFUSE_D in univariate DKF')
|
||||||
options_.diffuse_d = icc;
|
options_.diffuse_d = icc;
|
||||||
newRank=0;
|
newRank=0;
|
||||||
|
end
|
||||||
|
else
|
||||||
|
newRank = (any(diag(Z*P0*Z')>crit) | rank(P0,crit1));
|
||||||
|
if newRank==0,
|
||||||
|
options_.diffuse_d = icc;
|
||||||
|
end
|
||||||
|
end,
|
||||||
|
% end new terminiation criteria by M. Ratto
|
||||||
|
elseif Fstar(i,t) > crit
|
||||||
|
%% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition
|
||||||
|
%% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that
|
||||||
|
%% rank(Pinf)=0. [stéphane,11-03-2004].
|
||||||
|
Li(:,:,i,t) = eye(mm)-Kstar(:,i,t)*Z(i,:)/Fstar(i,t); % we need to store Li for DKF smoother
|
||||||
|
a(:,t) = a(:,t) + Kstar(:,i,t)*v(i,t)/Fstar(i,t);
|
||||||
|
Pstar(:,:,t) = Pstar(:,:,t) - Kstar(:,i,t)*Kstar(:,i,t)'/Fstar(i,t);
|
||||||
|
Pstar(:,:,t)=tril(Pstar(:,:,t))+tril(Pstar(:,:,t),-1)';
|
||||||
end
|
end
|
||||||
else
|
|
||||||
newRank = (any(diag(Z*P0*Z')>crit) | rank(P0,crit1));
|
|
||||||
if newRank==0,
|
|
||||||
options_.diffuse_d = icc;
|
|
||||||
end
|
|
||||||
end,
|
|
||||||
% end new terminiation criteria by M. Ratto
|
|
||||||
elseif Fstar(i,t) > crit
|
|
||||||
%% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition
|
|
||||||
%% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that
|
|
||||||
%% rank(Pinf)=0. [stéphane,11-03-2004].
|
|
||||||
Li(:,:,i,t) = eye(mm)-Kstar(:,i,t)*Z(i,:)/Fstar(i,t); % we need to store Li for DKF smoother
|
|
||||||
a(:,t) = a(:,t) + Kstar(:,i,t)*v(i,t)/Fstar(i,t);
|
|
||||||
Pstar(:,:,t) = Pstar(:,:,t) - Kstar(:,i,t)*Kstar(:,i,t)'/Fstar(i,t);
|
|
||||||
Pstar(:,:,t)=tril(Pstar(:,:,t))+tril(Pstar(:,:,t),-1)';
|
|
||||||
end
|
end
|
||||||
end
|
a1(:,t+1) = T*a(:,t);
|
||||||
a1(:,t+1) = T*a(:,t);
|
for jnk=1:nk,
|
||||||
for jnk=1:nk,
|
aK(jnk,:,t+jnk) = T^jnk*a(:,t);
|
||||||
aK(jnk,:,t+jnk) = T^jnk*a(:,t);
|
end
|
||||||
end
|
Pstar(:,:,t+1) = T*Pstar(:,:,t)*T'+ QQ;
|
||||||
Pstar(:,:,t+1) = T*Pstar(:,:,t)*T'+ QQ;
|
Pinf(:,:,t+1) = T*Pinf(:,:,t)*T';
|
||||||
Pinf(:,:,t+1) = T*Pinf(:,:,t)*T';
|
P0=Pinf(:,:,t+1);
|
||||||
P0=Pinf(:,:,t+1);
|
if newRank,
|
||||||
if newRank,
|
newRank = rank(P0,crit1);
|
||||||
newRank = rank(P0,crit1);
|
end
|
||||||
end
|
|
||||||
end
|
end
|
||||||
|
|
||||||
|
|
||||||
|
|
@ -188,31 +188,31 @@ Pstar1 = Pstar1(:,:,1:d);
|
||||||
Pinf1 = Pinf1(:,:,1:d);
|
Pinf1 = Pinf1(:,:,1:d);
|
||||||
notsteady = 1;
|
notsteady = 1;
|
||||||
while notsteady & t<smpl
|
while notsteady & t<smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
a(:,t) = a1(:,t);
|
a(:,t) = a1(:,t);
|
||||||
P(:,:,t)=tril(P(:,:,t))+tril(P(:,:,t),-1)';
|
P(:,:,t)=tril(P(:,:,t))+tril(P(:,:,t),-1)';
|
||||||
P1(:,:,t) = P(:,:,t);
|
P1(:,:,t) = P(:,:,t);
|
||||||
for i=1:pp
|
for i=1:pp
|
||||||
Zi = Z(i,:);
|
Zi = Z(i,:);
|
||||||
v(i,t) = Y(i,t) - Zi*a(:,t);
|
v(i,t) = Y(i,t) - Zi*a(:,t);
|
||||||
Fi(i,t) = Zi*P(:,:,t)*Zi';
|
Fi(i,t) = Zi*P(:,:,t)*Zi';
|
||||||
Ki(:,i,t) = P(:,:,t)*Zi';
|
Ki(:,i,t) = P(:,:,t)*Zi';
|
||||||
if Fi(i,t) > crit
|
if Fi(i,t) > crit
|
||||||
Li(:,:,i,t) = eye(mm)-Ki(:,i,t)*Z(i,:)/Fi(i,t);
|
Li(:,:,i,t) = eye(mm)-Ki(:,i,t)*Z(i,:)/Fi(i,t);
|
||||||
a(:,t) = a(:,t) + Ki(:,i,t)*v(i,t)/Fi(i,t);
|
a(:,t) = a(:,t) + Ki(:,i,t)*v(i,t)/Fi(i,t);
|
||||||
P(:,:,t) = P(:,:,t) - Ki(:,i,t)*Ki(:,i,t)'/Fi(i,t);
|
P(:,:,t) = P(:,:,t) - Ki(:,i,t)*Ki(:,i,t)'/Fi(i,t);
|
||||||
P(:,:,t)=tril(P(:,:,t))+tril(P(:,:,t),-1)';
|
P(:,:,t)=tril(P(:,:,t))+tril(P(:,:,t),-1)';
|
||||||
|
end
|
||||||
end
|
end
|
||||||
end
|
a1(:,t+1) = T*a(:,t);
|
||||||
a1(:,t+1) = T*a(:,t);
|
Pf = P(:,:,t);
|
||||||
Pf = P(:,:,t);
|
for jnk=1:nk,
|
||||||
for jnk=1:nk,
|
Pf = T*Pf*T' + QQ;
|
||||||
Pf = T*Pf*T' + QQ;
|
aK(jnk,:,t+jnk) = T^jnk*a(:,t);
|
||||||
aK(jnk,:,t+jnk) = T^jnk*a(:,t);
|
PK(jnk,:,:,t+jnk) = Pf;
|
||||||
PK(jnk,:,:,t+jnk) = Pf;
|
end
|
||||||
end
|
P(:,:,t+1) = T*P(:,:,t)*T' + QQ;
|
||||||
P(:,:,t+1) = T*P(:,:,t)*T' + QQ;
|
notsteady = ~(max(max(abs(P(:,:,t+1)-P(:,:,t))))<crit);
|
||||||
notsteady = ~(max(max(abs(P(:,:,t+1)-P(:,:,t))))<crit);
|
|
||||||
end
|
end
|
||||||
P_s=tril(P(:,:,t))+tril(P(:,:,t),-1)';
|
P_s=tril(P(:,:,t))+tril(P(:,:,t),-1)';
|
||||||
P1_s=tril(P1(:,:,t))+tril(P1(:,:,t),-1)';
|
P1_s=tril(P1(:,:,t))+tril(P1(:,:,t),-1)';
|
||||||
|
|
@ -220,67 +220,67 @@ Fi_s = Fi(:,t);
|
||||||
Ki_s = Ki(:,:,t);
|
Ki_s = Ki(:,:,t);
|
||||||
L_s =Li(:,:,:,t);
|
L_s =Li(:,:,:,t);
|
||||||
if t<smpl
|
if t<smpl
|
||||||
P = cat(3,P(:,:,1:t),repmat(P_s,[1 1 smpl-t]));
|
P = cat(3,P(:,:,1:t),repmat(P_s,[1 1 smpl-t]));
|
||||||
P1 = cat(3,P1(:,:,1:t),repmat(P1_s,[1 1 smpl-t]));
|
P1 = cat(3,P1(:,:,1:t),repmat(P1_s,[1 1 smpl-t]));
|
||||||
Fi = cat(2,Fi(:,1:t),repmat(Fi_s,[1 1 smpl-t]));
|
Fi = cat(2,Fi(:,1:t),repmat(Fi_s,[1 1 smpl-t]));
|
||||||
Li = cat(4,Li(:,:,:,1:t),repmat(L_s,[1 1 smpl-t]));
|
Li = cat(4,Li(:,:,:,1:t),repmat(L_s,[1 1 smpl-t]));
|
||||||
Ki = cat(3,Ki(:,:,1:t),repmat(Ki_s,[1 1 smpl-t]));
|
Ki = cat(3,Ki(:,:,1:t),repmat(Ki_s,[1 1 smpl-t]));
|
||||||
end
|
end
|
||||||
while t<smpl
|
while t<smpl
|
||||||
t=t+1;
|
t=t+1;
|
||||||
a(:,t) = a1(:,t);
|
a(:,t) = a1(:,t);
|
||||||
for i=1:pp
|
for i=1:pp
|
||||||
Zi = Z(i,:);
|
Zi = Z(i,:);
|
||||||
v(i,t) = Y(i,t) - Zi*a(:,t);
|
v(i,t) = Y(i,t) - Zi*a(:,t);
|
||||||
if Fi_s(i) > crit
|
if Fi_s(i) > crit
|
||||||
a(:,t) = a(:,t) + Ki_s(:,i)*v(i,t)/Fi_s(i);
|
a(:,t) = a(:,t) + Ki_s(:,i)*v(i,t)/Fi_s(i);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
a1(:,t+1) = T*a(:,t);
|
||||||
|
Pf = P(:,:,t);
|
||||||
|
for jnk=1:nk,
|
||||||
|
Pf = T*Pf*T' + QQ;
|
||||||
|
aK(jnk,:,t+jnk) = T^jnk*a(:,t);
|
||||||
|
PK(jnk,:,:,t+jnk) = Pf;
|
||||||
end
|
end
|
||||||
end
|
|
||||||
a1(:,t+1) = T*a(:,t);
|
|
||||||
Pf = P(:,:,t);
|
|
||||||
for jnk=1:nk,
|
|
||||||
Pf = T*Pf*T' + QQ;
|
|
||||||
aK(jnk,:,t+jnk) = T^jnk*a(:,t);
|
|
||||||
PK(jnk,:,:,t+jnk) = Pf;
|
|
||||||
end
|
|
||||||
end
|
end
|
||||||
ri=zeros(mm,1);
|
ri=zeros(mm,1);
|
||||||
t = smpl+1;
|
t = smpl+1;
|
||||||
while t > d+1
|
while t > d+1
|
||||||
t = t-1;
|
t = t-1;
|
||||||
for i=pp:-1:1
|
for i=pp:-1:1
|
||||||
if Fi(i,t) > crit
|
if Fi(i,t) > crit
|
||||||
ri = Z(i,:)'/Fi(i,t)*v(i,t)+Li(:,:,i,t)'*ri;
|
ri = Z(i,:)'/Fi(i,t)*v(i,t)+Li(:,:,i,t)'*ri;
|
||||||
|
end
|
||||||
end
|
end
|
||||||
end
|
r(:,t) = ri;
|
||||||
r(:,t) = ri;
|
alphahat(:,t) = a1(:,t) + P1(:,:,t)*r(:,t);
|
||||||
alphahat(:,t) = a1(:,t) + P1(:,:,t)*r(:,t);
|
etahat(:,t) = QRt*r(:,t);
|
||||||
etahat(:,t) = QRt*r(:,t);
|
ri = T'*ri;
|
||||||
ri = T'*ri;
|
|
||||||
end
|
end
|
||||||
if d
|
if d
|
||||||
r0 = zeros(mm,d);
|
r0 = zeros(mm,d);
|
||||||
r0(:,d) = ri;
|
r0(:,d) = ri;
|
||||||
r1 = zeros(mm,d);
|
r1 = zeros(mm,d);
|
||||||
for t = d:-1:1
|
for t = d:-1:1
|
||||||
for i=pp:-1:1
|
for i=pp:-1:1
|
||||||
% if Finf(i,t) > crit & ~(t==d & i>options_.diffuse_d), % use of options_.diffuse_d to be sure of DKF termination
|
% if Finf(i,t) > crit & ~(t==d & i>options_.diffuse_d), % use of options_.diffuse_d to be sure of DKF termination
|
||||||
if Finf(i,t) > crit
|
if Finf(i,t) > crit
|
||||||
r1(:,t) = Z(i,:)'*v(i,t)/Finf(i,t) + ...
|
r1(:,t) = Z(i,:)'*v(i,t)/Finf(i,t) + ...
|
||||||
L0(:,:,i,t)'*r0(:,t) + Linf(:,:,i,t)'*r1(:,t);
|
L0(:,:,i,t)'*r0(:,t) + Linf(:,:,i,t)'*r1(:,t);
|
||||||
r0(:,t) = Linf(:,:,i,t)'*r0(:,t);
|
r0(:,t) = Linf(:,:,i,t)'*r0(:,t);
|
||||||
elseif Fstar(i,t) > crit % step needed whe Finf == 0
|
elseif Fstar(i,t) > crit % step needed whe Finf == 0
|
||||||
r0(:,t) = Z(i,:)'/Fstar(i,t)*v(i,t)+Li(:,:,i,t)'*r0(:,t);
|
r0(:,t) = Z(i,:)'/Fstar(i,t)*v(i,t)+Li(:,:,i,t)'*r0(:,t);
|
||||||
end
|
end
|
||||||
|
end
|
||||||
|
alphahat(:,t) = a1(:,t) + Pstar1(:,:,t)*r0(:,t) + Pinf1(:,:,t)*r1(:,t);
|
||||||
|
r(:,t) = r0(:,t);
|
||||||
|
etahat(:,t) = QRt*r(:,t);
|
||||||
|
if t > 1
|
||||||
|
r0(:,t-1) = T'*r0(:,t);
|
||||||
|
r1(:,t-1) = T'*r1(:,t);
|
||||||
|
end
|
||||||
end
|
end
|
||||||
alphahat(:,t) = a1(:,t) + Pstar1(:,:,t)*r0(:,t) + Pinf1(:,:,t)*r1(:,t);
|
|
||||||
r(:,t) = r0(:,t);
|
|
||||||
etahat(:,t) = QRt*r(:,t);
|
|
||||||
if t > 1
|
|
||||||
r0(:,t-1) = T'*r0(:,t);
|
|
||||||
r1(:,t-1) = T'*r1(:,t);
|
|
||||||
end
|
|
||||||
end
|
|
||||||
end
|
end
|
||||||
|
|
||||||
if nargout > 7
|
if nargout > 7
|
||||||
|
|
|
||||||
|
|
@ -1,212 +1,212 @@
|
||||||
function [alphahat,epsilonhat,etahat,atilde,P,aK,PK,d,decomp] = DiffuseKalmanSmootherH1_Z(T,Z,R,Q,H,Pinf1,Pstar1,Y,pp,mm,smpl)
|
function [alphahat,epsilonhat,etahat,atilde,P,aK,PK,d,decomp] = DiffuseKalmanSmootherH1_Z(T,Z,R,Q,H,Pinf1,Pstar1,Y,pp,mm,smpl)
|
||||||
|
|
||||||
% function [alphahat,etahat,atilde, aK] = DiffuseKalmanSmootherH1_Z(T,Z,R,Q,H,Pinf1,Pstar1,Y,pp,mm,smpl)
|
% function [alphahat,etahat,atilde, aK] = DiffuseKalmanSmootherH1_Z(T,Z,R,Q,H,Pinf1,Pstar1,Y,pp,mm,smpl)
|
||||||
% Computes the diffuse kalman smoother without measurement error, in the case of a non-singular var-cov matrix
|
% Computes the diffuse kalman smoother without measurement error, in the case of a non-singular var-cov matrix
|
||||||
%
|
%
|
||||||
% INPUTS
|
% INPUTS
|
||||||
% T: mm*mm matrix
|
% T: mm*mm matrix
|
||||||
% Z: pp*mm matrix
|
% Z: pp*mm matrix
|
||||||
% R: mm*rr matrix
|
% R: mm*rr matrix
|
||||||
% Q: rr*rr matrix
|
% Q: rr*rr matrix
|
||||||
% H: pp*pp matrix variance of measurement errors
|
% H: pp*pp matrix variance of measurement errors
|
||||||
% Pinf1: mm*mm diagonal matrix with with q ones and m-q zeros
|
% Pinf1: mm*mm diagonal matrix with with q ones and m-q zeros
|
||||||
% Pstar1: mm*mm variance-covariance matrix with stationary variables
|
% Pstar1: mm*mm variance-covariance matrix with stationary variables
|
||||||
% Y: pp*1 vector
|
% Y: pp*1 vector
|
||||||
% pp: number of observed variables
|
% pp: number of observed variables
|
||||||
% mm: number of state variables
|
% mm: number of state variables
|
||||||
% smpl: sample size
|
% smpl: sample size
|
||||||
%
|
%
|
||||||
% OUTPUTS
|
% OUTPUTS
|
||||||
% alphahat: smoothed variables (a_{t|T})
|
% alphahat: smoothed variables (a_{t|T})
|
||||||
% epsilonhat:smoothed measurement errors
|
% epsilonhat:smoothed measurement errors
|
||||||
% etahat: smoothed shocks
|
% etahat: smoothed shocks
|
||||||
% atilde: matrix of updated variables (a_{t|t})
|
% atilde: matrix of updated variables (a_{t|t})
|
||||||
% aK: 3D array of k step ahead filtered state variables (a_{t+k|t)
|
% aK: 3D array of k step ahead filtered state variables (a_{t+k|t)
|
||||||
% (meaningless for periods 1:d)
|
% (meaningless for periods 1:d)
|
||||||
% P: 3D array of one-step ahead forecast error variance
|
% P: 3D array of one-step ahead forecast error variance
|
||||||
% matrices
|
% matrices
|
||||||
% PK: 4D array of k-step ahead forecast error variance
|
% PK: 4D array of k-step ahead forecast error variance
|
||||||
% matrices (meaningless for periods 1:d)
|
% matrices (meaningless for periods 1:d)
|
||||||
% d: number of periods where filter remains in diffuse part
|
% d: number of periods where filter remains in diffuse part
|
||||||
% (should be equal to the order of integration of the model)
|
% (should be equal to the order of integration of the model)
|
||||||
% decomp: decomposition of the effect of shocks on filtered values
|
% decomp: decomposition of the effect of shocks on filtered values
|
||||||
%
|
%
|
||||||
% SPECIAL REQUIREMENTS
|
% SPECIAL REQUIREMENTS
|
||||||
% See "Filtering and Smoothing of State Vector for Diffuse State Space
|
% See "Filtering and Smoothing of State Vector for Diffuse State Space
|
||||||
% Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
|
% Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
|
||||||
% Analysis, vol. 24(1), pp. 85-98).
|
% Analysis, vol. 24(1), pp. 85-98).
|
||||||
|
|
||||||
% Copyright (C) 2004-2008 Dynare Team
|
% Copyright (C) 2004-2008 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
% Dynare is free software: you can redistribute it and/or modify
|
||||||
% it under the terms of the GNU General Public License as published by
|
% it under the terms of the GNU General Public License as published by
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
% the Free Software Foundation, either version 3 of the License, or
|
||||||
% (at your option) any later version.
|
% (at your option) any later version.
|
||||||
%
|
%
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
% Dynare is distributed in the hope that it will be useful,
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
% modified by M. Ratto:
|
% modified by M. Ratto:
|
||||||
% new output argument aK (1-step to k-step predictions)
|
% new output argument aK (1-step to k-step predictions)
|
||||||
% new options_.nk: the max step ahed prediction in aK (default is 4)
|
% new options_.nk: the max step ahed prediction in aK (default is 4)
|
||||||
% new crit1 value for rank of Pinf
|
% new crit1 value for rank of Pinf
|
||||||
% it is assured that P is symmetric
|
% it is assured that P is symmetric
|
||||||
|
|
||||||
|
|
||||||
global options_
|
global options_
|
||||||
|
|
||||||
d = 0;
|
d = 0;
|
||||||
decomp = [];
|
decomp = [];
|
||||||
nk = options_.nk;
|
nk = options_.nk;
|
||||||
spinf = size(Pinf1);
|
spinf = size(Pinf1);
|
||||||
spstar = size(Pstar1);
|
spstar = size(Pstar1);
|
||||||
v = zeros(pp,smpl);
|
v = zeros(pp,smpl);
|
||||||
a = zeros(mm,smpl+1);
|
a = zeros(mm,smpl+1);
|
||||||
atilde = zeros(mm,smpl);
|
atilde = zeros(mm,smpl);
|
||||||
aK = zeros(nk,mm,smpl+nk);
|
aK = zeros(nk,mm,smpl+nk);
|
||||||
PK = zeros(nk,mm,mm,smpl+nk);
|
PK = zeros(nk,mm,mm,smpl+nk);
|
||||||
iF = zeros(pp,pp,smpl);
|
iF = zeros(pp,pp,smpl);
|
||||||
Fstar = zeros(pp,pp,smpl);
|
Fstar = zeros(pp,pp,smpl);
|
||||||
iFinf = zeros(pp,pp,smpl);
|
iFinf = zeros(pp,pp,smpl);
|
||||||
K = zeros(mm,pp,smpl);
|
K = zeros(mm,pp,smpl);
|
||||||
L = zeros(mm,mm,smpl);
|
L = zeros(mm,mm,smpl);
|
||||||
Linf = zeros(mm,mm,smpl);
|
Linf = zeros(mm,mm,smpl);
|
||||||
Kstar = zeros(mm,pp,smpl);
|
Kstar = zeros(mm,pp,smpl);
|
||||||
P = zeros(mm,mm,smpl+1);
|
P = zeros(mm,mm,smpl+1);
|
||||||
Pstar = zeros(spstar(1),spstar(2),smpl+1); Pstar(:,:,1) = Pstar1;
|
Pstar = zeros(spstar(1),spstar(2),smpl+1); Pstar(:,:,1) = Pstar1;
|
||||||
Pinf = zeros(spinf(1),spinf(2),smpl+1); Pinf(:,:,1) = Pinf1;
|
Pinf = zeros(spinf(1),spinf(2),smpl+1); Pinf(:,:,1) = Pinf1;
|
||||||
crit = options_.kalman_tol;
|
crit = options_.kalman_tol;
|
||||||
crit1 = 1.e-8;
|
crit1 = 1.e-8;
|
||||||
steady = smpl;
|
steady = smpl;
|
||||||
rr = size(Q,1);
|
rr = size(Q,1);
|
||||||
QQ = R*Q*transpose(R);
|
QQ = R*Q*transpose(R);
|
||||||
QRt = Q*transpose(R);
|
QRt = Q*transpose(R);
|
||||||
alphahat = zeros(mm,smpl);
|
alphahat = zeros(mm,smpl);
|
||||||
etahat = zeros(rr,smpl);
|
etahat = zeros(rr,smpl);
|
||||||
epsilonhat = zeros(size(Y));
|
epsilonhat = zeros(size(Y));
|
||||||
r = zeros(mm,smpl+1);
|
r = zeros(mm,smpl+1);
|
||||||
|
|
||||||
t = 0;
|
t = 0;
|
||||||
while rank(Pinf(:,:,t+1),crit1) & t<smpl
|
while rank(Pinf(:,:,t+1),crit1) & t<smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
v(:,t)= Y(:,t) - Z*a(:,t);
|
v(:,t)= Y(:,t) - Z*a(:,t);
|
||||||
F = Z*Pinf(:,:,t)*Z';
|
F = Z*Pinf(:,:,t)*Z';
|
||||||
if rcond(F) < crit
|
if rcond(F) < crit
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
iFinf(:,:,t) = inv(F);
|
iFinf(:,:,t) = inv(F);
|
||||||
PZI = Pinf(:,:,t)*Z'*iFinf(:,:,t);
|
PZI = Pinf(:,:,t)*Z'*iFinf(:,:,t);
|
||||||
atilde(:,t) = a(:,t) + PZI*v(:,t);
|
atilde(:,t) = a(:,t) + PZI*v(:,t);
|
||||||
Kinf(:,:,t) = T*PZI;
|
Kinf(:,:,t) = T*PZI;
|
||||||
a(:,t+1) = T*atilde(:,t);
|
a(:,t+1) = T*atilde(:,t);
|
||||||
aK(1,:,t+1) = a(:,t+1);
|
aK(1,:,t+1) = a(:,t+1);
|
||||||
% isn't a meaningless as long as we are in the diffuse part? MJ
|
% isn't a meaningless as long as we are in the diffuse part? MJ
|
||||||
for jnk=2:nk,
|
for jnk=2:nk,
|
||||||
aK(jnk,:,t+jnk) = T^(jnk-1)*a(:,t+1);
|
aK(jnk,:,t+jnk) = T^(jnk-1)*a(:,t+1);
|
||||||
end
|
end
|
||||||
Linf(:,:,t) = T - Kinf(:,:,t)*Z;
|
Linf(:,:,t) = T - Kinf(:,:,t)*Z;
|
||||||
Fstar(:,:,t) = Z*Pstar(:,:,t)*Z' + H;
|
Fstar(:,:,t) = Z*Pstar(:,:,t)*Z' + H;
|
||||||
Kstar(:,:,t) = (T*Pstar(:,:,t)*Z'-Kinf(:,:,t)*Fstar(:,:,t))*iFinf(:,:,t);
|
Kstar(:,:,t) = (T*Pstar(:,:,t)*Z'-Kinf(:,:,t)*Fstar(:,:,t))*iFinf(:,:,t);
|
||||||
Pstar(:,:,t+1) = T*Pstar(:,:,t)*T'-T*Pstar(:,:,t)*Z'*Kinf(:,:,t)'-Kinf(:,:,t)*F*Kstar(:,:,t)' + QQ;
|
Pstar(:,:,t+1) = T*Pstar(:,:,t)*T'-T*Pstar(:,:,t)*Z'*Kinf(:,:,t)'-Kinf(:,:,t)*F*Kstar(:,:,t)' + QQ;
|
||||||
Pinf(:,:,t+1) = T*Pinf(:,:,t)*T'-T*Pinf(:,:,t)*Z'*Kinf(:,:,t)';
|
Pinf(:,:,t+1) = T*Pinf(:,:,t)*T'-T*Pinf(:,:,t)*Z'*Kinf(:,:,t)';
|
||||||
end
|
end
|
||||||
d = t;
|
d = t;
|
||||||
P(:,:,d+1) = Pstar(:,:,d+1);
|
P(:,:,d+1) = Pstar(:,:,d+1);
|
||||||
iFinf = iFinf(:,:,1:d);
|
iFinf = iFinf(:,:,1:d);
|
||||||
Linf = Linf(:,:,1:d);
|
Linf = Linf(:,:,1:d);
|
||||||
Fstar = Fstar(:,:,1:d);
|
Fstar = Fstar(:,:,1:d);
|
||||||
Kstar = Kstar(:,:,1:d);
|
Kstar = Kstar(:,:,1:d);
|
||||||
Pstar = Pstar(:,:,1:d);
|
Pstar = Pstar(:,:,1:d);
|
||||||
Pinf = Pinf(:,:,1:d);
|
Pinf = Pinf(:,:,1:d);
|
||||||
notsteady = 1;
|
notsteady = 1;
|
||||||
while notsteady & t<smpl
|
while notsteady & t<smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
v(:,t) = Y(:,t) - Z*a(:,t);
|
v(:,t) = Y(:,t) - Z*a(:,t);
|
||||||
P(:,:,t)=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
|
P(:,:,t)=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
|
||||||
F = Z*P(:,:,t)*Z' + H;
|
F = Z*P(:,:,t)*Z' + H;
|
||||||
if rcond(F) < crit
|
if rcond(F) < crit
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
iF(:,:,t) = inv(F);
|
iF(:,:,t) = inv(F);
|
||||||
PZI = P(:,:,t)*Z'*iF(:,:,t);
|
PZI = P(:,:,t)*Z'*iF(:,:,t);
|
||||||
atilde(:,t) = a(:,t) + PZI*v(:,t);
|
atilde(:,t) = a(:,t) + PZI*v(:,t);
|
||||||
K(:,:,t) = T*PZI;
|
K(:,:,t) = T*PZI;
|
||||||
L(:,:,t) = T-K(:,:,t)*Z;
|
L(:,:,t) = T-K(:,:,t)*Z;
|
||||||
a(:,t+1) = T*atilde(:,t);
|
a(:,t+1) = T*atilde(:,t);
|
||||||
Pf = P(:,:,t);
|
Pf = P(:,:,t);
|
||||||
for jnk=1:nk,
|
for jnk=1:nk,
|
||||||
Pf = T*Pf*T' + QQ;
|
Pf = T*Pf*T' + QQ;
|
||||||
aK(jnk,:,t+jnk) = T^jnk*atilde(:,t);
|
aK(jnk,:,t+jnk) = T^jnk*atilde(:,t);
|
||||||
PK(jnk,:,:,t+jnk) = Pf;
|
PK(jnk,:,:,t+jnk) = Pf;
|
||||||
end
|
end
|
||||||
P(:,:,t+1) = T*P(:,:,t)*T'-T*P(:,:,t)*Z'*K(:,:,t)' + QQ;
|
P(:,:,t+1) = T*P(:,:,t)*T'-T*P(:,:,t)*Z'*K(:,:,t)' + QQ;
|
||||||
notsteady = ~(max(max(abs(P(:,:,t+1)-P(:,:,t))))<crit);
|
notsteady = ~(max(max(abs(P(:,:,t+1)-P(:,:,t))))<crit);
|
||||||
end
|
end
|
||||||
if t<smpl
|
if t<smpl
|
||||||
PZI_s = PZI;
|
PZI_s = PZI;
|
||||||
K_s = K(:,:,t);
|
K_s = K(:,:,t);
|
||||||
iF_s = iF(:,:,t);
|
iF_s = iF(:,:,t);
|
||||||
P_s = P(:,:,t+1);
|
P_s = P(:,:,t+1);
|
||||||
P = cat(3,P(:,:,1:t),repmat(P_s,[1 1 smpl-t]));
|
P = cat(3,P(:,:,1:t),repmat(P_s,[1 1 smpl-t]));
|
||||||
iF = cat(3,iF(:,:,1:t),repmat(iF_s,[1 1 smpl-t]));
|
iF = cat(3,iF(:,:,1:t),repmat(iF_s,[1 1 smpl-t]));
|
||||||
L = cat(3,L(:,:,1:t),repmat(T-K_s*Z,[1 1 smpl-t]));
|
L = cat(3,L(:,:,1:t),repmat(T-K_s*Z,[1 1 smpl-t]));
|
||||||
K = cat(3,K(:,:,1:t),repmat(T*P_s*Z'*iF_s,[1 1 smpl-t]));
|
K = cat(3,K(:,:,1:t),repmat(T*P_s*Z'*iF_s,[1 1 smpl-t]));
|
||||||
end
|
end
|
||||||
while t<smpl
|
while t<smpl
|
||||||
t=t+1;
|
t=t+1;
|
||||||
v(:,t) = Y(:,t) - Z*a(:,t);
|
v(:,t) = Y(:,t) - Z*a(:,t);
|
||||||
atilde(:,t) = a(:,t) + PZI*v(:,t);
|
atilde(:,t) = a(:,t) + PZI*v(:,t);
|
||||||
a(:,t+1) = T*atilde(:,t);
|
a(:,t+1) = T*atilde(:,t);
|
||||||
Pf = P(:,:,t);
|
Pf = P(:,:,t);
|
||||||
for jnk=1:nk,
|
for jnk=1:nk,
|
||||||
Pf = T*Pf*T' + QQ;
|
Pf = T*Pf*T' + QQ;
|
||||||
aK(jnk,:,t+jnk) = T^jnk*atilde(:,t);
|
aK(jnk,:,t+jnk) = T^jnk*atilde(:,t);
|
||||||
PK(jnk,:,:,t+jnk) = Pf;
|
PK(jnk,:,:,t+jnk) = Pf;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
t = smpl+1;
|
t = smpl+1;
|
||||||
while t>d+1
|
while t>d+1
|
||||||
t = t-1;
|
t = t-1;
|
||||||
r(:,t) = Z'*iF(:,:,t)*v(:,t) + L(:,:,t)'*r(:,t+1);
|
r(:,t) = Z'*iF(:,:,t)*v(:,t) + L(:,:,t)'*r(:,t+1);
|
||||||
alphahat(:,t) = a(:,t) + P(:,:,t)*r(:,t);
|
alphahat(:,t) = a(:,t) + P(:,:,t)*r(:,t);
|
||||||
etahat(:,t) = QRt*r(:,t);
|
etahat(:,t) = QRt*r(:,t);
|
||||||
end
|
end
|
||||||
if d
|
if d
|
||||||
r0 = zeros(mm,d+1);
|
r0 = zeros(mm,d+1);
|
||||||
r0(:,d+1) = r(:,d+1);
|
r0(:,d+1) = r(:,d+1);
|
||||||
r1 = zeros(mm,d+1);
|
r1 = zeros(mm,d+1);
|
||||||
for t = d:-1:1
|
for t = d:-1:1
|
||||||
r0(:,t) = Linf(:,:,t)'*r0(:,t+1);
|
r0(:,t) = Linf(:,:,t)'*r0(:,t+1);
|
||||||
r1(:,t) = Z'*(iFinf(:,:,t)*v(:,t)-Kstar(:,:,t)'*r0(:,t+1)) + Linf(:,:,t)'*r1(:,t+1);
|
r1(:,t) = Z'*(iFinf(:,:,t)*v(:,t)-Kstar(:,:,t)'*r0(:,t+1)) + Linf(:,:,t)'*r1(:,t+1);
|
||||||
alphahat(:,t) = a(:,t) + Pstar(:,:,t)*r0(:,t) + Pinf(:,:,t)*r1(:,t);
|
alphahat(:,t) = a(:,t) + Pstar(:,:,t)*r0(:,t) + Pinf(:,:,t)*r1(:,t);
|
||||||
etahat(:,t) = QRt*r0(:,t);
|
etahat(:,t) = QRt*r0(:,t);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
||||||
if nargout > 7
|
if nargout > 7
|
||||||
decomp = zeros(nk,mm,rr,smpl+nk);
|
decomp = zeros(nk,mm,rr,smpl+nk);
|
||||||
ZRQinv = inv(Z*QQ*Z');
|
ZRQinv = inv(Z*QQ*Z');
|
||||||
for t = max(d,1):smpl
|
for t = max(d,1):smpl
|
||||||
ri_d = Z'*iF(:,:,t)*v(:,t);
|
ri_d = Z'*iF(:,:,t)*v(:,t);
|
||||||
|
|
||||||
% calculate eta_tm1t
|
% calculate eta_tm1t
|
||||||
eta_tm1t = QRt*ri_d;
|
eta_tm1t = QRt*ri_d;
|
||||||
% calculate decomposition
|
% calculate decomposition
|
||||||
Ttok = eye(mm,mm);
|
Ttok = eye(mm,mm);
|
||||||
for h = 1:nk
|
for h = 1:nk
|
||||||
for j=1:rr
|
for j=1:rr
|
||||||
eta=zeros(rr,1);
|
eta=zeros(rr,1);
|
||||||
eta(j) = eta_tm1t(j);
|
eta(j) = eta_tm1t(j);
|
||||||
decomp(h,:,j,t+h) = T^(h-1)*P(:,:,t)*Z'*ZRQinv*Z*R*eta;
|
decomp(h,:,j,t+h) = T^(h-1)*P(:,:,t)*Z'*ZRQinv*Z*R*eta;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
epsilonhat = Y-Z*alphahat;
|
epsilonhat = Y-Z*alphahat;
|
||||||
|
|
|
||||||
|
|
@ -68,9 +68,9 @@ a1 = zeros(mm,smpl+1);
|
||||||
aK = zeros(nk,mm,smpl+nk);
|
aK = zeros(nk,mm,smpl+nk);
|
||||||
|
|
||||||
if isempty(options_.diffuse_d),
|
if isempty(options_.diffuse_d),
|
||||||
smpl_diff = 1;
|
smpl_diff = 1;
|
||||||
else
|
else
|
||||||
smpl_diff=rank(Pinf1);
|
smpl_diff=rank(Pinf1);
|
||||||
end
|
end
|
||||||
|
|
||||||
Fstar = zeros(pp,smpl_diff);
|
Fstar = zeros(pp,smpl_diff);
|
||||||
|
|
@ -99,81 +99,81 @@ r = zeros(mm,smpl+1);
|
||||||
|
|
||||||
Z = zeros(pp,mm);
|
Z = zeros(pp,mm);
|
||||||
for i=1:pp;
|
for i=1:pp;
|
||||||
Z(i,mf(i)) = 1;
|
Z(i,mf(i)) = 1;
|
||||||
end
|
end
|
||||||
|
|
||||||
t = 0;
|
t = 0;
|
||||||
icc=0;
|
icc=0;
|
||||||
newRank = rank(Pinf(:,:,1),crit1);
|
newRank = rank(Pinf(:,:,1),crit1);
|
||||||
while newRank & t < smpl
|
while newRank & t < smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
a(:,t) = a1(:,t);
|
a(:,t) = a1(:,t);
|
||||||
Pstar(:,:,t)=tril(Pstar(:,:,t))+transpose(tril(Pstar(:,:,t),-1));
|
Pstar(:,:,t)=tril(Pstar(:,:,t))+transpose(tril(Pstar(:,:,t),-1));
|
||||||
Pinf(:,:,t)=tril(Pinf(:,:,t))+transpose(tril(Pinf(:,:,t),-1));
|
Pinf(:,:,t)=tril(Pinf(:,:,t))+transpose(tril(Pinf(:,:,t),-1));
|
||||||
Pstar1(:,:,t) = Pstar(:,:,t);
|
Pstar1(:,:,t) = Pstar(:,:,t);
|
||||||
Pinf1(:,:,t) = Pinf(:,:,t);
|
Pinf1(:,:,t) = Pinf(:,:,t);
|
||||||
for i=1:pp
|
for i=1:pp
|
||||||
v(i,t) = Y(i,t)-a(mf(i),t)-trend(i,t);
|
v(i,t) = Y(i,t)-a(mf(i),t)-trend(i,t);
|
||||||
Fstar(i,t) = Pstar(mf(i),mf(i),t) + H(i,i);
|
Fstar(i,t) = Pstar(mf(i),mf(i),t) + H(i,i);
|
||||||
Finf(i,t) = Pinf(mf(i),mf(i),t);
|
Finf(i,t) = Pinf(mf(i),mf(i),t);
|
||||||
Kstar(:,i,t) = Pstar(:,mf(i),t);
|
Kstar(:,i,t) = Pstar(:,mf(i),t);
|
||||||
if Finf(i,t) > crit & newRank, % original MJ: if Finf(i,t) > crit
|
if Finf(i,t) > crit & newRank, % original MJ: if Finf(i,t) > crit
|
||||||
icc=icc+1;
|
icc=icc+1;
|
||||||
Kinf(:,i,t) = Pinf(:,mf(i),t);
|
Kinf(:,i,t) = Pinf(:,mf(i),t);
|
||||||
Linf(:,:,i,t) = eye(mm) - Kinf(:,i,t)*Z(i,:)/Finf(i,t);
|
Linf(:,:,i,t) = eye(mm) - Kinf(:,i,t)*Z(i,:)/Finf(i,t);
|
||||||
L0(:,:,i,t) = (Kinf(:,i,t)*Fstar(i,t)/Finf(i,t) - Kstar(:,i,t))*Z(i,:)/Finf(i,t);
|
L0(:,:,i,t) = (Kinf(:,i,t)*Fstar(i,t)/Finf(i,t) - Kstar(:,i,t))*Z(i,:)/Finf(i,t);
|
||||||
a(:,t) = a(:,t) + Kinf(:,i,t)*v(i,t)/Finf(i,t);
|
a(:,t) = a(:,t) + Kinf(:,i,t)*v(i,t)/Finf(i,t);
|
||||||
Pstar(:,:,t) = Pstar(:,:,t) + ...
|
Pstar(:,:,t) = Pstar(:,:,t) + ...
|
||||||
Kinf(:,i,t)*transpose(Kinf(:,i,t))*Fstar(i,t)/(Finf(i,t)*Finf(i,t)) - ...
|
Kinf(:,i,t)*transpose(Kinf(:,i,t))*Fstar(i,t)/(Finf(i,t)*Finf(i,t)) - ...
|
||||||
(Kstar(:,i,t)*transpose(Kinf(:,i,t)) +...
|
(Kstar(:,i,t)*transpose(Kinf(:,i,t)) +...
|
||||||
Kinf(:,i,t)*transpose(Kstar(:,i,t)))/Finf(i,t);
|
Kinf(:,i,t)*transpose(Kstar(:,i,t)))/Finf(i,t);
|
||||||
Pinf(:,:,t) = Pinf(:,:,t) - Kinf(:,i,t)*transpose(Kinf(:,i,t))/Finf(i,t);
|
Pinf(:,:,t) = Pinf(:,:,t) - Kinf(:,i,t)*transpose(Kinf(:,i,t))/Finf(i,t);
|
||||||
Pstar(:,:,t)=tril(Pstar(:,:,t))+transpose(tril(Pstar(:,:,t),-1));
|
Pstar(:,:,t)=tril(Pstar(:,:,t))+transpose(tril(Pstar(:,:,t),-1));
|
||||||
Pinf(:,:,t)=tril(Pinf(:,:,t))+transpose(tril(Pinf(:,:,t),-1));
|
Pinf(:,:,t)=tril(Pinf(:,:,t))+transpose(tril(Pinf(:,:,t),-1));
|
||||||
% new terminiation criteria by M. Ratto
|
% new terminiation criteria by M. Ratto
|
||||||
P0=Pinf(:,:,t);
|
P0=Pinf(:,:,t);
|
||||||
% newRank = any(diag(P0(mf,mf))>crit);
|
% newRank = any(diag(P0(mf,mf))>crit);
|
||||||
% if newRank==0, options_.diffuse_d = i; end,
|
% if newRank==0, options_.diffuse_d = i; end,
|
||||||
if ~isempty(options_.diffuse_d),
|
if ~isempty(options_.diffuse_d),
|
||||||
newRank = (icc<options_.diffuse_d);
|
newRank = (icc<options_.diffuse_d);
|
||||||
%if newRank & any(diag(P0(mf,mf))>crit)==0;
|
%if newRank & any(diag(P0(mf,mf))>crit)==0;
|
||||||
if newRank & (any(diag(P0(mf,mf))>crit)==0 & rank(P0,crit1)==0);
|
if newRank & (any(diag(P0(mf,mf))>crit)==0 & rank(P0,crit1)==0);
|
||||||
disp('WARNING!! Change in OPTIONS_.DIFFUSE_D in univariate DKF')
|
disp('WARNING!! Change in OPTIONS_.DIFFUSE_D in univariate DKF')
|
||||||
options_.diffuse_d = icc;
|
options_.diffuse_d = icc;
|
||||||
newRank=0;
|
newRank=0;
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
%newRank = any(diag(P0(mf,mf))>crit);
|
%newRank = any(diag(P0(mf,mf))>crit);
|
||||||
newRank = (any(diag(P0(mf,mf))>crit) | rank(P0,crit1));
|
newRank = (any(diag(P0(mf,mf))>crit) | rank(P0,crit1));
|
||||||
if newRank==0,
|
if newRank==0,
|
||||||
options_.diffuse_d = icc;
|
options_.diffuse_d = icc;
|
||||||
end
|
end
|
||||||
end,
|
end,
|
||||||
% if newRank==0,
|
% if newRank==0,
|
||||||
% options_.diffuse_d=i; % this line is buggy!
|
% options_.diffuse_d=i; % this line is buggy!
|
||||||
% end
|
% end
|
||||||
% end new terminiation criteria by M. Ratto
|
% end new terminiation criteria by M. Ratto
|
||||||
elseif Fstar(i,t) > crit
|
elseif Fstar(i,t) > crit
|
||||||
%% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition
|
%% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition
|
||||||
%% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that
|
%% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that
|
||||||
%% rank(Pinf)=0. [stéphane,11-03-2004].
|
%% rank(Pinf)=0. [stéphane,11-03-2004].
|
||||||
Li(:,:,i,t) = eye(mm)-Kstar(:,i,t)*Z(i,:)/Fstar(i,t); % we need to store Li for DKF smoother
|
Li(:,:,i,t) = eye(mm)-Kstar(:,i,t)*Z(i,:)/Fstar(i,t); % we need to store Li for DKF smoother
|
||||||
a(:,t) = a(:,t) + Kstar(:,i,t)*v(i,t)/Fstar(i,t);
|
a(:,t) = a(:,t) + Kstar(:,i,t)*v(i,t)/Fstar(i,t);
|
||||||
Pstar(:,:,t) = Pstar(:,:,t) - Kstar(:,i,t)*transpose(Kstar(:,i,t))/Fstar(i,t);
|
Pstar(:,:,t) = Pstar(:,:,t) - Kstar(:,i,t)*transpose(Kstar(:,i,t))/Fstar(i,t);
|
||||||
Pstar(:,:,t)=tril(Pstar(:,:,t))+transpose(tril(Pstar(:,:,t),-1));
|
Pstar(:,:,t)=tril(Pstar(:,:,t))+transpose(tril(Pstar(:,:,t),-1));
|
||||||
|
end
|
||||||
|
end
|
||||||
|
a1(:,t+1) = T*a(:,t);
|
||||||
|
for jnk=1:nk,
|
||||||
|
aK(jnk,:,t+jnk) = T^jnk*a(:,t);
|
||||||
|
end
|
||||||
|
Pstar(:,:,t+1) = T*Pstar(:,:,t)*transpose(T)+ QQ;
|
||||||
|
Pinf(:,:,t+1) = T*Pinf(:,:,t)*transpose(T);
|
||||||
|
P0=Pinf(:,:,t+1);
|
||||||
|
if newRank,
|
||||||
|
%newRank = any(diag(P0(mf,mf))>crit);
|
||||||
|
newRank = rank(P0,crit1);
|
||||||
end
|
end
|
||||||
end
|
|
||||||
a1(:,t+1) = T*a(:,t);
|
|
||||||
for jnk=1:nk,
|
|
||||||
aK(jnk,:,t+jnk) = T^jnk*a(:,t);
|
|
||||||
end
|
|
||||||
Pstar(:,:,t+1) = T*Pstar(:,:,t)*transpose(T)+ QQ;
|
|
||||||
Pinf(:,:,t+1) = T*Pinf(:,:,t)*transpose(T);
|
|
||||||
P0=Pinf(:,:,t+1);
|
|
||||||
if newRank,
|
|
||||||
%newRank = any(diag(P0(mf,mf))>crit);
|
|
||||||
newRank = rank(P0,crit1);
|
|
||||||
end
|
|
||||||
end
|
end
|
||||||
|
|
||||||
|
|
||||||
|
|
@ -190,89 +190,89 @@ Pstar1 = Pstar1(:,:,1:d);
|
||||||
Pinf1 = Pinf1(:,:,1:d);
|
Pinf1 = Pinf1(:,:,1:d);
|
||||||
notsteady = 1;
|
notsteady = 1;
|
||||||
while notsteady & t<smpl
|
while notsteady & t<smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
a(:,t) = a1(:,t);
|
a(:,t) = a1(:,t);
|
||||||
P(:,:,t)=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
|
P(:,:,t)=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
|
||||||
P1(:,:,t) = P(:,:,t);
|
P1(:,:,t) = P(:,:,t);
|
||||||
for i=1:pp
|
for i=1:pp
|
||||||
v(i,t) = Y(i,t) - a(mf(i),t) - trend(i,t);
|
v(i,t) = Y(i,t) - a(mf(i),t) - trend(i,t);
|
||||||
Fi(i,t) = P(mf(i),mf(i),t)+H(i,i);
|
Fi(i,t) = P(mf(i),mf(i),t)+H(i,i);
|
||||||
Ki(:,i,t) = P(:,mf(i),t);
|
Ki(:,i,t) = P(:,mf(i),t);
|
||||||
if Fi(i,t) > crit
|
if Fi(i,t) > crit
|
||||||
Li(:,:,i,t) = eye(mm)-Ki(:,i,t)*Z(i,:)/Fi(i,t);
|
Li(:,:,i,t) = eye(mm)-Ki(:,i,t)*Z(i,:)/Fi(i,t);
|
||||||
a(:,t) = a(:,t) + Ki(:,i,t)*v(i,t)/Fi(i,t);
|
a(:,t) = a(:,t) + Ki(:,i,t)*v(i,t)/Fi(i,t);
|
||||||
P(:,:,t) = P(:,:,t) - Ki(:,i,t)*transpose(Ki(:,i,t))/Fi(i,t);
|
P(:,:,t) = P(:,:,t) - Ki(:,i,t)*transpose(Ki(:,i,t))/Fi(i,t);
|
||||||
P(:,:,t)=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
|
P(:,:,t)=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
|
||||||
|
end
|
||||||
end
|
end
|
||||||
end
|
a1(:,t+1) = T*a(:,t);
|
||||||
a1(:,t+1) = T*a(:,t);
|
for jnk=1:nk,
|
||||||
for jnk=1:nk,
|
aK(jnk,:,t+jnk) = T^jnk*a(:,t);
|
||||||
aK(jnk,:,t+jnk) = T^jnk*a(:,t);
|
end
|
||||||
end
|
P(:,:,t+1) = T*P(:,:,t)*transpose(T) + QQ;
|
||||||
P(:,:,t+1) = T*P(:,:,t)*transpose(T) + QQ;
|
notsteady = ~(max(max(abs(P(:,:,t+1)-P(:,:,t))))<crit);
|
||||||
notsteady = ~(max(max(abs(P(:,:,t+1)-P(:,:,t))))<crit);
|
|
||||||
end
|
end
|
||||||
P_s=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
|
P_s=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
|
||||||
Fi_s = Fi(:,t);
|
Fi_s = Fi(:,t);
|
||||||
Ki_s = Ki(:,:,t);
|
Ki_s = Ki(:,:,t);
|
||||||
L_s =Li(:,:,:,t);
|
L_s =Li(:,:,:,t);
|
||||||
if t<smpl
|
if t<smpl
|
||||||
t_steady = t+1;
|
t_steady = t+1;
|
||||||
P = cat(3,P(:,:,1:t),repmat(P(:,:,t),[1 1 smpl-t_steady+1]));
|
P = cat(3,P(:,:,1:t),repmat(P(:,:,t),[1 1 smpl-t_steady+1]));
|
||||||
Fi = cat(2,Fi(:,1:t),repmat(Fi_s,[1 1 smpl-t_steady+1]));
|
Fi = cat(2,Fi(:,1:t),repmat(Fi_s,[1 1 smpl-t_steady+1]));
|
||||||
Li = cat(4,Li(:,:,:,1:t),repmat(L_s,[1 1 smpl-t_steady+1]));
|
Li = cat(4,Li(:,:,:,1:t),repmat(L_s,[1 1 smpl-t_steady+1]));
|
||||||
Ki = cat(3,Ki(:,:,1:t),repmat(Ki_s,[1 1 smpl-t_steady+1]));
|
Ki = cat(3,Ki(:,:,1:t),repmat(Ki_s,[1 1 smpl-t_steady+1]));
|
||||||
end
|
end
|
||||||
while t<smpl
|
while t<smpl
|
||||||
t=t+1;
|
t=t+1;
|
||||||
a(:,t) = a1(:,t);
|
a(:,t) = a1(:,t);
|
||||||
for i=1:pp
|
for i=1:pp
|
||||||
v(i,t) = Y(i,t) - a(mf(i),t) - trend(i,t);
|
v(i,t) = Y(i,t) - a(mf(i),t) - trend(i,t);
|
||||||
if Fi_s(i) > crit
|
if Fi_s(i) > crit
|
||||||
a(:,t) = a(:,t) + Ki_s(:,i)*v(i,t)/Fi_s(i);
|
a(:,t) = a(:,t) + Ki_s(:,i)*v(i,t)/Fi_s(i);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
a1(:,t+1) = T*a(:,t);
|
||||||
|
for jnk=1:nk,
|
||||||
|
aK(jnk,:,t+jnk) = T^jnk*a(:,t);
|
||||||
end
|
end
|
||||||
end
|
|
||||||
a1(:,t+1) = T*a(:,t);
|
|
||||||
for jnk=1:nk,
|
|
||||||
aK(jnk,:,t+jnk) = T^jnk*a(:,t);
|
|
||||||
end
|
|
||||||
end
|
end
|
||||||
ri=zeros(mm,1);
|
ri=zeros(mm,1);
|
||||||
t = smpl+1;
|
t = smpl+1;
|
||||||
while t>d+1
|
while t>d+1
|
||||||
t = t-1;
|
t = t-1;
|
||||||
for i=pp:-1:1
|
for i=pp:-1:1
|
||||||
if Fi(i,t) > crit
|
if Fi(i,t) > crit
|
||||||
ri=Z(i,:)'/Fi(i,t)*v(i,t)+Li(:,:,i,t)'*ri;
|
ri=Z(i,:)'/Fi(i,t)*v(i,t)+Li(:,:,i,t)'*ri;
|
||||||
|
end
|
||||||
end
|
end
|
||||||
end
|
r(:,t) = ri;
|
||||||
r(:,t) = ri;
|
alphahat(:,t) = a1(:,t) + P1(:,:,t)*r(:,t);
|
||||||
alphahat(:,t) = a1(:,t) + P1(:,:,t)*r(:,t);
|
etahat(:,t) = QRt*r(:,t);
|
||||||
etahat(:,t) = QRt*r(:,t);
|
ri = T'*ri;
|
||||||
ri = T'*ri;
|
|
||||||
end
|
end
|
||||||
if d
|
if d
|
||||||
r0 = zeros(mm,d);
|
r0 = zeros(mm,d);
|
||||||
r0(:,d) = ri;
|
r0(:,d) = ri;
|
||||||
r1 = zeros(mm,d);
|
r1 = zeros(mm,d);
|
||||||
for t = d:-1:1
|
for t = d:-1:1
|
||||||
for i=pp:-1:1
|
for i=pp:-1:1
|
||||||
if Finf(i,t) > crit & ~(t==d & i>options_.diffuse_d), % use of options_.diffuse_d to be sure of DKF termination
|
if Finf(i,t) > crit & ~(t==d & i>options_.diffuse_d), % use of options_.diffuse_d to be sure of DKF termination
|
||||||
r1(:,t) = Z(i,:)'*v(i,t)/Finf(i,t) + ...
|
r1(:,t) = Z(i,:)'*v(i,t)/Finf(i,t) + ...
|
||||||
L0(:,:,i,t)'*r0(:,t) + Linf(:,:,i,t)'*r1(:,t);
|
L0(:,:,i,t)'*r0(:,t) + Linf(:,:,i,t)'*r1(:,t);
|
||||||
r0(:,t) = Linf(:,:,i,t)'*r0(:,t);
|
r0(:,t) = Linf(:,:,i,t)'*r0(:,t);
|
||||||
elseif Fstar(i,t) > crit % step needed whe Finf == 0
|
elseif Fstar(i,t) > crit % step needed whe Finf == 0
|
||||||
r0(:,t)=Z(i,:)'/Fstar(i,t)*v(i,t)+Li(:,:,i,t)'*r0(:,t);
|
r0(:,t)=Z(i,:)'/Fstar(i,t)*v(i,t)+Li(:,:,i,t)'*r0(:,t);
|
||||||
end
|
end
|
||||||
|
end
|
||||||
|
alphahat(:,t) = a1(:,t) + Pstar1(:,:,t)*r0(:,t) + Pinf1(:,:,t)*r1(:,t);
|
||||||
|
r(:,t) = r0(:,t);
|
||||||
|
etahat(:,t) = QRt*r(:,t);
|
||||||
|
if t > 1
|
||||||
|
r0(:,t-1) = transpose(T)*r0(:,t);
|
||||||
|
r1(:,t-1) = transpose(T)*r1(:,t);
|
||||||
|
end
|
||||||
end
|
end
|
||||||
alphahat(:,t) = a1(:,t) + Pstar1(:,:,t)*r0(:,t) + Pinf1(:,:,t)*r1(:,t);
|
|
||||||
r(:,t) = r0(:,t);
|
|
||||||
etahat(:,t) = QRt*r(:,t);
|
|
||||||
if t > 1
|
|
||||||
r0(:,t-1) = transpose(T)*r0(:,t);
|
|
||||||
r1(:,t-1) = transpose(T)*r1(:,t);
|
|
||||||
end
|
|
||||||
end
|
|
||||||
end
|
end
|
||||||
epsilonhat = Y-alphahat(mf,:)-trend;
|
epsilonhat = Y-alphahat(mf,:)-trend;
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -77,9 +77,9 @@ a1 = zeros(mm,smpl+1);
|
||||||
aK = zeros(nk,mm,smpl+nk);
|
aK = zeros(nk,mm,smpl+nk);
|
||||||
|
|
||||||
if isempty(options_.diffuse_d),
|
if isempty(options_.diffuse_d),
|
||||||
smpl_diff = 1;
|
smpl_diff = 1;
|
||||||
else
|
else
|
||||||
smpl_diff=rank(Pinf1);
|
smpl_diff=rank(Pinf1);
|
||||||
end
|
end
|
||||||
|
|
||||||
Fstar = zeros(pp,smpl_diff);
|
Fstar = zeros(pp,smpl_diff);
|
||||||
|
|
@ -112,67 +112,67 @@ t = 0;
|
||||||
icc=0;
|
icc=0;
|
||||||
newRank = rank(Pinf(:,:,1),crit1);
|
newRank = rank(Pinf(:,:,1),crit1);
|
||||||
while newRank & t < smpl
|
while newRank & t < smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
a(:,t) = a1(:,t);
|
a(:,t) = a1(:,t);
|
||||||
Pstar(:,:,t)=tril(Pstar(:,:,t))+tril(Pstar(:,:,t),-1)';
|
Pstar(:,:,t)=tril(Pstar(:,:,t))+tril(Pstar(:,:,t),-1)';
|
||||||
Pinf(:,:,t)=tril(Pinf(:,:,t))+tril(Pinf(:,:,t),-1)';
|
Pinf(:,:,t)=tril(Pinf(:,:,t))+tril(Pinf(:,:,t),-1)';
|
||||||
Pstar1(:,:,t) = Pstar(:,:,t);
|
Pstar1(:,:,t) = Pstar(:,:,t);
|
||||||
Pinf1(:,:,t) = Pinf(:,:,t);
|
Pinf1(:,:,t) = Pinf(:,:,t);
|
||||||
for i=1:pp
|
for i=1:pp
|
||||||
Zi = Z(i,:);
|
Zi = Z(i,:);
|
||||||
v(i,t) = Y(i,t)-Zi*a(:,t);
|
v(i,t) = Y(i,t)-Zi*a(:,t);
|
||||||
Fstar(i,t) = Zi*Pstar(:,:,t)*Zi' +H(i,i);
|
Fstar(i,t) = Zi*Pstar(:,:,t)*Zi' +H(i,i);
|
||||||
Finf(i,t) = Zi*Pinf(:,:,t)*Zi';
|
Finf(i,t) = Zi*Pinf(:,:,t)*Zi';
|
||||||
Kstar(:,i,t) = Pstar(:,:,t)*Zi';
|
Kstar(:,i,t) = Pstar(:,:,t)*Zi';
|
||||||
if Finf(i,t) > crit & newRank
|
if Finf(i,t) > crit & newRank
|
||||||
icc=icc+1;
|
icc=icc+1;
|
||||||
Kinf(:,i,t) = Pinf(:,:,t)*Zi';
|
Kinf(:,i,t) = Pinf(:,:,t)*Zi';
|
||||||
Linf(:,:,i,t) = eye(mm) - Kinf(:,i,t)*Z(i,:)/Finf(i,t);
|
Linf(:,:,i,t) = eye(mm) - Kinf(:,i,t)*Z(i,:)/Finf(i,t);
|
||||||
L0(:,:,i,t) = (Kinf(:,i,t)*Fstar(i,t)/Finf(i,t) - Kstar(:,i,t))*Zi/Finf(i,t);
|
L0(:,:,i,t) = (Kinf(:,i,t)*Fstar(i,t)/Finf(i,t) - Kstar(:,i,t))*Zi/Finf(i,t);
|
||||||
a(:,t) = a(:,t) + Kinf(:,i,t)*v(i,t)/Finf(i,t);
|
a(:,t) = a(:,t) + Kinf(:,i,t)*v(i,t)/Finf(i,t);
|
||||||
Pstar(:,:,t) = Pstar(:,:,t) + ...
|
Pstar(:,:,t) = Pstar(:,:,t) + ...
|
||||||
Kinf(:,i,t)*Kinf(:,i,t)'*Fstar(i,t)/(Finf(i,t)*Finf(i,t)) - ...
|
Kinf(:,i,t)*Kinf(:,i,t)'*Fstar(i,t)/(Finf(i,t)*Finf(i,t)) - ...
|
||||||
(Kstar(:,i,t)*Kinf(:,i,t)' +...
|
(Kstar(:,i,t)*Kinf(:,i,t)' +...
|
||||||
Kinf(:,i,t)*Kstar(:,i,t)')/Finf(i,t);
|
Kinf(:,i,t)*Kstar(:,i,t)')/Finf(i,t);
|
||||||
Pinf(:,:,t) = Pinf(:,:,t) - Kinf(:,i,t)*Kinf(:,i,t)'/Finf(i,t);
|
Pinf(:,:,t) = Pinf(:,:,t) - Kinf(:,i,t)*Kinf(:,i,t)'/Finf(i,t);
|
||||||
Pstar(:,:,t)=tril(Pstar(:,:,t))+tril(Pstar(:,:,t),-1)';
|
Pstar(:,:,t)=tril(Pstar(:,:,t))+tril(Pstar(:,:,t),-1)';
|
||||||
Pinf(:,:,t)=tril(Pinf(:,:,t))+tril(Pinf(:,:,t),-1)';
|
Pinf(:,:,t)=tril(Pinf(:,:,t))+tril(Pinf(:,:,t),-1)';
|
||||||
% new terminiation criteria by M. Ratto
|
% new terminiation criteria by M. Ratto
|
||||||
P0=Pinf(:,:,t);
|
P0=Pinf(:,:,t);
|
||||||
if ~isempty(options_.diffuse_d),
|
if ~isempty(options_.diffuse_d),
|
||||||
newRank = (icc<options_.diffuse_d);
|
newRank = (icc<options_.diffuse_d);
|
||||||
if newRank & (any(diag(Z*P0*Z')>crit)==0 & rank(P0,crit1)==0);
|
if newRank & (any(diag(Z*P0*Z')>crit)==0 & rank(P0,crit1)==0);
|
||||||
disp('WARNING!! Change in OPTIONS_.DIFFUSE_D in univariate DKF')
|
disp('WARNING!! Change in OPTIONS_.DIFFUSE_D in univariate DKF')
|
||||||
options_.diffuse_d = icc;
|
options_.diffuse_d = icc;
|
||||||
newRank=0;
|
newRank=0;
|
||||||
|
end
|
||||||
|
else
|
||||||
|
newRank = (any(diag(Z*P0*Z')>crit) | rank(P0,crit1));
|
||||||
|
if newRank==0,
|
||||||
|
options_.diffuse_d = icc;
|
||||||
|
end
|
||||||
|
end,
|
||||||
|
% end new terminiation criteria by M. Ratto
|
||||||
|
elseif Fstar(i,t) > crit
|
||||||
|
%% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition
|
||||||
|
%% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that
|
||||||
|
%% rank(Pinf)=0. [stéphane,11-03-2004].
|
||||||
|
Li(:,:,i,t) = eye(mm)-Kstar(:,i,t)*Z(i,:)/Fstar(i,t); % we need to store Li for DKF smoother
|
||||||
|
a(:,t) = a(:,t) + Kstar(:,i,t)*v(i,t)/Fstar(i,t);
|
||||||
|
Pstar(:,:,t) = Pstar(:,:,t) - Kstar(:,i,t)*Kstar(:,i,t)'/Fstar(i,t);
|
||||||
|
Pstar(:,:,t)=tril(Pstar(:,:,t))+tril(Pstar(:,:,t),-1)';
|
||||||
end
|
end
|
||||||
else
|
|
||||||
newRank = (any(diag(Z*P0*Z')>crit) | rank(P0,crit1));
|
|
||||||
if newRank==0,
|
|
||||||
options_.diffuse_d = icc;
|
|
||||||
end
|
|
||||||
end,
|
|
||||||
% end new terminiation criteria by M. Ratto
|
|
||||||
elseif Fstar(i,t) > crit
|
|
||||||
%% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition
|
|
||||||
%% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that
|
|
||||||
%% rank(Pinf)=0. [stéphane,11-03-2004].
|
|
||||||
Li(:,:,i,t) = eye(mm)-Kstar(:,i,t)*Z(i,:)/Fstar(i,t); % we need to store Li for DKF smoother
|
|
||||||
a(:,t) = a(:,t) + Kstar(:,i,t)*v(i,t)/Fstar(i,t);
|
|
||||||
Pstar(:,:,t) = Pstar(:,:,t) - Kstar(:,i,t)*Kstar(:,i,t)'/Fstar(i,t);
|
|
||||||
Pstar(:,:,t)=tril(Pstar(:,:,t))+tril(Pstar(:,:,t),-1)';
|
|
||||||
end
|
end
|
||||||
end
|
a1(:,t+1) = T*a(:,t);
|
||||||
a1(:,t+1) = T*a(:,t);
|
for jnk=1:nk,
|
||||||
for jnk=1:nk,
|
aK(jnk,:,t+jnk) = T^jnk*a(:,t);
|
||||||
aK(jnk,:,t+jnk) = T^jnk*a(:,t);
|
end
|
||||||
end
|
Pstar(:,:,t+1) = T*Pstar(:,:,t)*T'+ QQ;
|
||||||
Pstar(:,:,t+1) = T*Pstar(:,:,t)*T'+ QQ;
|
Pinf(:,:,t+1) = T*Pinf(:,:,t)*T';
|
||||||
Pinf(:,:,t+1) = T*Pinf(:,:,t)*T';
|
P0=Pinf(:,:,t+1);
|
||||||
P0=Pinf(:,:,t+1);
|
if newRank,
|
||||||
if newRank,
|
newRank = rank(P0,crit1);
|
||||||
newRank = rank(P0,crit1);
|
end
|
||||||
end
|
|
||||||
end
|
end
|
||||||
|
|
||||||
|
|
||||||
|
|
@ -189,31 +189,31 @@ Pstar1 = Pstar1(:,:,1:d);
|
||||||
Pinf1 = Pinf1(:,:,1:d);
|
Pinf1 = Pinf1(:,:,1:d);
|
||||||
notsteady = 1;
|
notsteady = 1;
|
||||||
while notsteady & t<smpl
|
while notsteady & t<smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
a(:,t) = a1(:,t);
|
a(:,t) = a1(:,t);
|
||||||
P(:,:,t)=tril(P(:,:,t))+tril(P(:,:,t),-1)';
|
P(:,:,t)=tril(P(:,:,t))+tril(P(:,:,t),-1)';
|
||||||
P1(:,:,t) = P(:,:,t);
|
P1(:,:,t) = P(:,:,t);
|
||||||
for i=1:pp
|
for i=1:pp
|
||||||
Zi = Z(i,:);
|
Zi = Z(i,:);
|
||||||
v(i,t) = Y(i,t) - Zi*a(:,t);
|
v(i,t) = Y(i,t) - Zi*a(:,t);
|
||||||
Fi(i,t) = Zi*P(:,:,t)*Zi' + H(i,i);
|
Fi(i,t) = Zi*P(:,:,t)*Zi' + H(i,i);
|
||||||
Ki(:,i,t) = P(:,:,t)*Zi';
|
Ki(:,i,t) = P(:,:,t)*Zi';
|
||||||
if Fi(i,t) > crit
|
if Fi(i,t) > crit
|
||||||
Li(:,:,i,t) = eye(mm)-Ki(:,i,t)*Z(i,:)/Fi(i,t);
|
Li(:,:,i,t) = eye(mm)-Ki(:,i,t)*Z(i,:)/Fi(i,t);
|
||||||
a(:,t) = a(:,t) + Ki(:,i,t)*v(i,t)/Fi(i,t);
|
a(:,t) = a(:,t) + Ki(:,i,t)*v(i,t)/Fi(i,t);
|
||||||
P(:,:,t) = P(:,:,t) - Ki(:,i,t)*Ki(:,i,t)'/Fi(i,t);
|
P(:,:,t) = P(:,:,t) - Ki(:,i,t)*Ki(:,i,t)'/Fi(i,t);
|
||||||
P(:,:,t)=tril(P(:,:,t))+tril(P(:,:,t),-1)';
|
P(:,:,t)=tril(P(:,:,t))+tril(P(:,:,t),-1)';
|
||||||
|
end
|
||||||
end
|
end
|
||||||
end
|
a1(:,t+1) = T*a(:,t);
|
||||||
a1(:,t+1) = T*a(:,t);
|
Pf = P(:,:,t);
|
||||||
Pf = P(:,:,t);
|
for jnk=1:nk,
|
||||||
for jnk=1:nk,
|
Pf = T*Pf*T' + QQ;
|
||||||
Pf = T*Pf*T' + QQ;
|
aK(jnk,:,t+jnk) = T^jnk*a(:,t);
|
||||||
aK(jnk,:,t+jnk) = T^jnk*a(:,t);
|
PK(jnk,:,:,t+jnk) = Pf;
|
||||||
PK(jnk,:,:,t+jnk) = Pf;
|
end
|
||||||
end
|
P(:,:,t+1) = T*P(:,:,t)*T' + QQ;
|
||||||
P(:,:,t+1) = T*P(:,:,t)*T' + QQ;
|
notsteady = ~(max(max(abs(P(:,:,t+1)-P(:,:,t))))<crit);
|
||||||
notsteady = ~(max(max(abs(P(:,:,t+1)-P(:,:,t))))<crit);
|
|
||||||
end
|
end
|
||||||
P_s=tril(P(:,:,t))+tril(P(:,:,t),-1)';
|
P_s=tril(P(:,:,t))+tril(P(:,:,t),-1)';
|
||||||
P1_s=tril(P1(:,:,t))+tril(P1(:,:,t),-1)';
|
P1_s=tril(P1(:,:,t))+tril(P1(:,:,t),-1)';
|
||||||
|
|
@ -221,67 +221,67 @@ Fi_s = Fi(:,t);
|
||||||
Ki_s = Ki(:,:,t);
|
Ki_s = Ki(:,:,t);
|
||||||
L_s =Li(:,:,:,t);
|
L_s =Li(:,:,:,t);
|
||||||
if t<smpl
|
if t<smpl
|
||||||
P = cat(3,P(:,:,1:t),repmat(P_s,[1 1 smpl-t]));
|
P = cat(3,P(:,:,1:t),repmat(P_s,[1 1 smpl-t]));
|
||||||
P1 = cat(3,P1(:,:,1:t),repmat(P1_s,[1 1 smpl-t]));
|
P1 = cat(3,P1(:,:,1:t),repmat(P1_s,[1 1 smpl-t]));
|
||||||
Fi = cat(2,Fi(:,1:t),repmat(Fi_s,[1 1 smpl-t]));
|
Fi = cat(2,Fi(:,1:t),repmat(Fi_s,[1 1 smpl-t]));
|
||||||
Li = cat(4,Li(:,:,:,1:t),repmat(L_s,[1 1 smpl-t]));
|
Li = cat(4,Li(:,:,:,1:t),repmat(L_s,[1 1 smpl-t]));
|
||||||
Ki = cat(3,Ki(:,:,1:t),repmat(Ki_s,[1 1 smpl-t]));
|
Ki = cat(3,Ki(:,:,1:t),repmat(Ki_s,[1 1 smpl-t]));
|
||||||
end
|
end
|
||||||
while t<smpl
|
while t<smpl
|
||||||
t=t+1;
|
t=t+1;
|
||||||
a(:,t) = a1(:,t);
|
a(:,t) = a1(:,t);
|
||||||
for i=1:pp
|
for i=1:pp
|
||||||
Zi = Z(i,:);
|
Zi = Z(i,:);
|
||||||
v(i,t) = Y(i,t) - Zi*a(:,t);
|
v(i,t) = Y(i,t) - Zi*a(:,t);
|
||||||
if Fi_s(i) > crit
|
if Fi_s(i) > crit
|
||||||
a(:,t) = a(:,t) + Ki_s(:,i)*v(i,t)/Fi_s(i);
|
a(:,t) = a(:,t) + Ki_s(:,i)*v(i,t)/Fi_s(i);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
a1(:,t+1) = T*a(:,t);
|
||||||
|
Pf = P(:,:,t);
|
||||||
|
for jnk=1:nk,
|
||||||
|
Pf = T*Pf*T' + QQ;
|
||||||
|
aK(jnk,:,t+jnk) = T^jnk*a(:,t);
|
||||||
|
PK(jnk,:,:,t+jnk) = Pf;
|
||||||
end
|
end
|
||||||
end
|
|
||||||
a1(:,t+1) = T*a(:,t);
|
|
||||||
Pf = P(:,:,t);
|
|
||||||
for jnk=1:nk,
|
|
||||||
Pf = T*Pf*T' + QQ;
|
|
||||||
aK(jnk,:,t+jnk) = T^jnk*a(:,t);
|
|
||||||
PK(jnk,:,:,t+jnk) = Pf;
|
|
||||||
end
|
|
||||||
end
|
end
|
||||||
ri=zeros(mm,1);
|
ri=zeros(mm,1);
|
||||||
t = smpl+1;
|
t = smpl+1;
|
||||||
while t>d+1
|
while t>d+1
|
||||||
t = t-1;
|
t = t-1;
|
||||||
for i=pp:-1:1
|
for i=pp:-1:1
|
||||||
if Fi(i,t) > crit
|
if Fi(i,t) > crit
|
||||||
ri = Z(i,:)'/Fi(i,t)*v(i,t)+Li(:,:,i,t)'*ri;
|
ri = Z(i,:)'/Fi(i,t)*v(i,t)+Li(:,:,i,t)'*ri;
|
||||||
|
end
|
||||||
end
|
end
|
||||||
end
|
r(:,t) = ri;
|
||||||
r(:,t) = ri;
|
alphahat(:,t) = a1(:,t) + P1(:,:,t)*r(:,t);
|
||||||
alphahat(:,t) = a1(:,t) + P1(:,:,t)*r(:,t);
|
etahat(:,t) = QRt*r(:,t);
|
||||||
etahat(:,t) = QRt*r(:,t);
|
ri = T'*ri;
|
||||||
ri = T'*ri;
|
|
||||||
end
|
end
|
||||||
if d
|
if d
|
||||||
r0 = zeros(mm,d);
|
r0 = zeros(mm,d);
|
||||||
r0(:,d) = ri;
|
r0(:,d) = ri;
|
||||||
r1 = zeros(mm,d);
|
r1 = zeros(mm,d);
|
||||||
for t = d:-1:2
|
for t = d:-1:2
|
||||||
for i=pp:-1:1
|
for i=pp:-1:1
|
||||||
% if Finf(i,t) > crit & ~(t==d & i>options_.diffuse_d), % use of options_.diffuse_d to be sure of DKF termination
|
% if Finf(i,t) > crit & ~(t==d & i>options_.diffuse_d), % use of options_.diffuse_d to be sure of DKF termination
|
||||||
if Finf(i,t) > crit
|
if Finf(i,t) > crit
|
||||||
r1(:,t) = Z(i,:)'*v(i,t)/Finf(i,t) + ...
|
r1(:,t) = Z(i,:)'*v(i,t)/Finf(i,t) + ...
|
||||||
L0(:,:,i,t)'*r0(:,t) + Linf(:,:,i,t)'*r1(:,t);
|
L0(:,:,i,t)'*r0(:,t) + Linf(:,:,i,t)'*r1(:,t);
|
||||||
r0(:,t) = Linf(:,:,i,t)'*r0(:,t);
|
r0(:,t) = Linf(:,:,i,t)'*r0(:,t);
|
||||||
elseif Fstar(i,t) > crit % step needed whe Finf == 0
|
elseif Fstar(i,t) > crit % step needed whe Finf == 0
|
||||||
r0(:,t) = Z(i,:)'/Fstar(i,t)*v(i,t)+Li(:,:,i,t)'*r0(:,t);
|
r0(:,t) = Z(i,:)'/Fstar(i,t)*v(i,t)+Li(:,:,i,t)'*r0(:,t);
|
||||||
end
|
end
|
||||||
|
end
|
||||||
|
alphahat(:,t) = a1(:,t) + Pstar1(:,:,t)*r0(:,t) + Pinf1(:,:,t)*r1(:,t);
|
||||||
|
r(:,t) = r0(:,t);
|
||||||
|
etahat(:,t) = QRt*r(:,t);
|
||||||
|
if t > 1
|
||||||
|
r0(:,t-1) = T'*r0(:,t);
|
||||||
|
r1(:,t-1) = T'*r1(:,t);
|
||||||
|
end
|
||||||
end
|
end
|
||||||
alphahat(:,t) = a1(:,t) + Pstar1(:,:,t)*r0(:,t) + Pinf1(:,:,t)*r1(:,t);
|
|
||||||
r(:,t) = r0(:,t);
|
|
||||||
etahat(:,t) = QRt*r(:,t);
|
|
||||||
if t > 1
|
|
||||||
r0(:,t-1) = T'*r0(:,t);
|
|
||||||
r1(:,t-1) = T'*r1(:,t);
|
|
||||||
end
|
|
||||||
end
|
|
||||||
end
|
end
|
||||||
|
|
||||||
if nargout > 7
|
if nargout > 7
|
||||||
|
|
|
||||||
|
|
@ -54,7 +54,7 @@ T = cat(1,cat(2,T,zeros(mm,pp)),zeros(pp,mm+pp));
|
||||||
R = cat(1,cat(2,R,zeros(mm,pp)),cat(2,zeros(pp,rr),eye(pp)));
|
R = cat(1,cat(2,R,zeros(mm,pp)),cat(2,zeros(pp,rr),eye(pp)));
|
||||||
Q = cat(1,cat(2,Q,zeros(rr,pp)),cat(2,zeros(pp,rr),H));
|
Q = cat(1,cat(2,Q,zeros(rr,pp)),cat(2,zeros(pp,rr),H));
|
||||||
if size(Pinf1,1) % Otherwise Pinf = 0 (no unit root)
|
if size(Pinf1,1) % Otherwise Pinf = 0 (no unit root)
|
||||||
Pinf1 = cat(1,cat(2,Pinf1,zeros(mm,pp)),zeros(pp,mm+pp));
|
Pinf1 = cat(1,cat(2,Pinf1,zeros(mm,pp)),zeros(pp,mm+pp));
|
||||||
end
|
end
|
||||||
Pstar1 = cat(1,cat(2,Pstar1,zeros(mm,pp)),cat(2,zeros(pp,mm),H));
|
Pstar1 = cat(1,cat(2,Pstar1,zeros(mm,pp)),cat(2,zeros(pp,mm),H));
|
||||||
spinf = size(Pinf1);
|
spinf = size(Pinf1);
|
||||||
|
|
@ -88,8 +88,8 @@ epsilonhat = zeros(pp,smpl);
|
||||||
r = zeros(mm+pp,smpl+1);
|
r = zeros(mm+pp,smpl+1);
|
||||||
Z = zeros(pp,mm+pp);
|
Z = zeros(pp,mm+pp);
|
||||||
for i=1:pp;
|
for i=1:pp;
|
||||||
Z(i,mf(i)) = 1;
|
Z(i,mf(i)) = 1;
|
||||||
Z(i,mm+i) = 1;
|
Z(i,mm+i) = 1;
|
||||||
end
|
end
|
||||||
%% [1] Kalman filter...
|
%% [1] Kalman filter...
|
||||||
t = 0;
|
t = 0;
|
||||||
|
|
@ -99,28 +99,28 @@ while newRank & t < smpl
|
||||||
a(:,t) = a1(:,t);
|
a(:,t) = a1(:,t);
|
||||||
Pstar1(:,:,t) = Pstar(:,:,t);
|
Pstar1(:,:,t) = Pstar(:,:,t);
|
||||||
Pinf1(:,:,t) = Pinf(:,:,t);
|
Pinf1(:,:,t) = Pinf(:,:,t);
|
||||||
for i=1:pp
|
for i=1:pp
|
||||||
v(i,t) = Y(i,t)-a(mf(i),t)-a(mm+i,t)-trend(i,t);
|
v(i,t) = Y(i,t)-a(mf(i),t)-a(mm+i,t)-trend(i,t);
|
||||||
Fstar(i,t) = Pstar(mf(i),mf(i),t)+Pstar(mm+i,mm+i,t);
|
Fstar(i,t) = Pstar(mf(i),mf(i),t)+Pstar(mm+i,mm+i,t);
|
||||||
Finf(i,t) = Pinf(mf(i),mf(i),t);
|
Finf(i,t) = Pinf(mf(i),mf(i),t);
|
||||||
Kstar(:,i,t) = Pstar(:,mf(i),t)+Pstar(:,mm+i,t);
|
Kstar(:,i,t) = Pstar(:,mf(i),t)+Pstar(:,mm+i,t);
|
||||||
if Finf(i,t) > crit
|
if Finf(i,t) > crit
|
||||||
Kinf(:,i,t) = Pinf(:,mf(i),t);
|
Kinf(:,i,t) = Pinf(:,mf(i),t);
|
||||||
Linf(:,:,i,t) = eye(mm+pp) - Kinf(:,i,t)*Z(i,:)/Finf(i,t);
|
Linf(:,:,i,t) = eye(mm+pp) - Kinf(:,i,t)*Z(i,:)/Finf(i,t);
|
||||||
L0(:,:,i,t) = (Kinf(:,i,t)*Fstar(i,t)/Finf(i,t) - Kstar(:,i,t))*Z(i,:)/Finf(i,t);
|
L0(:,:,i,t) = (Kinf(:,i,t)*Fstar(i,t)/Finf(i,t) - Kstar(:,i,t))*Z(i,:)/Finf(i,t);
|
||||||
a(:,t) = a(:,t) + Kinf(:,i,t)*v(i,t)/Finf(i,t);
|
a(:,t) = a(:,t) + Kinf(:,i,t)*v(i,t)/Finf(i,t);
|
||||||
Pstar(:,:,t) = Pstar(:,:,t) + ...
|
Pstar(:,:,t) = Pstar(:,:,t) + ...
|
||||||
Kinf(:,i,t)*transpose(Kinf(:,i,t))*Fstar(i,t)/(Finf(i,t)*Finf(i,t)) - ...
|
Kinf(:,i,t)*transpose(Kinf(:,i,t))*Fstar(i,t)/(Finf(i,t)*Finf(i,t)) - ...
|
||||||
(Kstar(:,i,t)*transpose(Kinf(:,i,t)) +...
|
(Kstar(:,i,t)*transpose(Kinf(:,i,t)) +...
|
||||||
Kinf(:,i,t)*transpose(Kstar(:,i,t)))/Finf(i,t);
|
Kinf(:,i,t)*transpose(Kstar(:,i,t)))/Finf(i,t);
|
||||||
Pinf(:,:,t) = Pinf(:,:,t) - Kinf(:,i,t)*transpose(Kinf(:,i,t))/Finf(i,t);
|
Pinf(:,:,t) = Pinf(:,:,t) - Kinf(:,i,t)*transpose(Kinf(:,i,t))/Finf(i,t);
|
||||||
else %% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition
|
else %% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition
|
||||||
%% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that
|
%% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that
|
||||||
%% rank(Pinf)=0. [stéphane,11-03-2004].
|
%% rank(Pinf)=0. [stéphane,11-03-2004].
|
||||||
a(:,t) = a(:,t) + Kstar(:,i,t)*v(i,t)/Fstar(i,t);
|
a(:,t) = a(:,t) + Kstar(:,i,t)*v(i,t)/Fstar(i,t);
|
||||||
Pstar(:,:,t) = Pstar(:,:,t) - Kstar(:,i,t)*transpose(Kstar(:,i,t))/Fstar(i,t);
|
Pstar(:,:,t) = Pstar(:,:,t) - Kstar(:,i,t)*transpose(Kstar(:,i,t))/Fstar(i,t);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
a1(:,t+1) = T*a(:,t);
|
a1(:,t+1) = T*a(:,t);
|
||||||
for jnk=1:nk,
|
for jnk=1:nk,
|
||||||
aK(jnk,:,t+jnk) = T^jnk*a(:,t);
|
aK(jnk,:,t+jnk) = T^jnk*a(:,t);
|
||||||
|
|
@ -147,11 +147,11 @@ while notsteady & t<smpl
|
||||||
a(:,t) = a1(:,t);
|
a(:,t) = a1(:,t);
|
||||||
P(:,:,t)=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
|
P(:,:,t)=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
|
||||||
P1(:,:,t) = P(:,:,t);
|
P1(:,:,t) = P(:,:,t);
|
||||||
for i=1:pp
|
for i=1:pp
|
||||||
v(i,t) = Y(i,t) - a(mf(i),t) - a(mm+i,t) - trend(i,t);
|
v(i,t) = Y(i,t) - a(mf(i),t) - a(mm+i,t) - trend(i,t);
|
||||||
Fi(i,t) = P(mf(i),mf(i),t)+P(mm+i,mm+i,t);
|
Fi(i,t) = P(mf(i),mf(i),t)+P(mm+i,mm+i,t);
|
||||||
Ki(:,i,t) = P(:,mf(i),t)+P(:,mm+i,t);
|
Ki(:,i,t) = P(:,mf(i),t)+P(:,mm+i,t);
|
||||||
if Fi(i,t) > crit
|
if Fi(i,t) > crit
|
||||||
Li(:,:,i,t) = eye(mm+pp)-Ki(:,i,t)*Z(i,:)/Fi(i,t);
|
Li(:,:,i,t) = eye(mm+pp)-Ki(:,i,t)*Z(i,:)/Fi(i,t);
|
||||||
a(:,t) = a(:,t) + Ki(:,i,t)*v(i,t)/Fi(i,t);
|
a(:,t) = a(:,t) + Ki(:,i,t)*v(i,t)/Fi(i,t);
|
||||||
P(:,:,t) = P(:,:,t) - Ki(:,i,t)*transpose(Ki(:,i,t))/Fi(i,t);
|
P(:,:,t) = P(:,:,t) - Ki(:,i,t)*transpose(Ki(:,i,t))/Fi(i,t);
|
||||||
|
|
@ -170,11 +170,11 @@ Fi_s = Fi(:,t);
|
||||||
Ki_s = Ki(:,:,t);
|
Ki_s = Ki(:,:,t);
|
||||||
L_s =Li(:,:,:,t);
|
L_s =Li(:,:,:,t);
|
||||||
if t<smpl
|
if t<smpl
|
||||||
t_steady = t+1;
|
t_steady = t+1;
|
||||||
P = cat(3,P(:,:,1:t),repmat(P(:,:,t),[1 1 smpl-t_steady+1]));
|
P = cat(3,P(:,:,1:t),repmat(P(:,:,t),[1 1 smpl-t_steady+1]));
|
||||||
Fi = cat(2,Fi(:,1:t),repmat(Fi_s,[1 1 smpl-t_steady+1]));
|
Fi = cat(2,Fi(:,1:t),repmat(Fi_s,[1 1 smpl-t_steady+1]));
|
||||||
Li = cat(4,Li(:,:,:,1:t),repmat(L_s,[1 1 smpl-t_steady+1]));
|
Li = cat(4,Li(:,:,:,1:t),repmat(L_s,[1 1 smpl-t_steady+1]));
|
||||||
Ki = cat(3,Ki(:,:,1:t),repmat(Ki_s,[1 1 smpl-t_steady+1]));
|
Ki = cat(3,Ki(:,:,1:t),repmat(Ki_s,[1 1 smpl-t_steady+1]));
|
||||||
end
|
end
|
||||||
while t<smpl
|
while t<smpl
|
||||||
t=t+1;
|
t=t+1;
|
||||||
|
|
@ -219,16 +219,16 @@ if d
|
||||||
L0(:,:,i,t)'*r0(:,t) + Linf(:,:,i,t)'*r1(:,t);
|
L0(:,:,i,t)'*r0(:,t) + Linf(:,:,i,t)'*r1(:,t);
|
||||||
r0(:,t) = transpose(Linf(:,:,i,t))*r0(:,t);
|
r0(:,t) = transpose(Linf(:,:,i,t))*r0(:,t);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
alphahat(:,t) = a1(:,t) + Pstar1(:,:,t)*r0(:,t) + Pinf1(:,:,t)*r1(:,t);
|
alphahat(:,t) = a1(:,t) + Pstar1(:,:,t)*r0(:,t) + Pinf1(:,:,t)*r1(:,t);
|
||||||
r(:,t-1) = r0(:,t);
|
r(:,t-1) = r0(:,t);
|
||||||
tmp = QRt*r(:,t);
|
tmp = QRt*r(:,t);
|
||||||
etahat(:,t) = tmp(1:rr);
|
etahat(:,t) = tmp(1:rr);
|
||||||
epsilonhat(:,t) = tmp(rr+(1:pp));
|
epsilonhat(:,t) = tmp(rr+(1:pp));
|
||||||
if t > 1
|
if t > 1
|
||||||
r0(:,t-1) = T'*r0(:,t);
|
r0(:,t-1) = T'*r0(:,t);
|
||||||
r1(:,t-1) = T'*r1(:,t);
|
r1(:,t-1) = T'*r1(:,t);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
alphahat = alphahat(1:mm,:);
|
alphahat = alphahat(1:mm,:);
|
||||||
|
|
@ -1,130 +1,130 @@
|
||||||
function [LIK, lik] = DiffuseLikelihood1(T,R,Q,Pinf,Pstar,Y,trend,start)
|
function [LIK, lik] = DiffuseLikelihood1(T,R,Q,Pinf,Pstar,Y,trend,start)
|
||||||
|
|
||||||
% function [LIK, lik] = DiffuseLikelihood1(T,R,Q,Pinf,Pstar,Y,trend,start)
|
% function [LIK, lik] = DiffuseLikelihood1(T,R,Q,Pinf,Pstar,Y,trend,start)
|
||||||
% Computes the diffuse likelihood without measurement error, in the case of a non-singular var-cov matrix
|
% Computes the diffuse likelihood without measurement error, in the case of a non-singular var-cov matrix
|
||||||
%
|
%
|
||||||
% INPUTS
|
% INPUTS
|
||||||
% T: mm*mm matrix
|
% T: mm*mm matrix
|
||||||
% R: mm*rr matrix
|
% R: mm*rr matrix
|
||||||
% Q: rr*rr matrix
|
% Q: rr*rr matrix
|
||||||
% Pinf: mm*mm diagonal matrix with with q ones and m-q zeros
|
% Pinf: mm*mm diagonal matrix with with q ones and m-q zeros
|
||||||
% Pstar: mm*mm variance-covariance matrix with stationary variables
|
% Pstar: mm*mm variance-covariance matrix with stationary variables
|
||||||
% Y: pp*1 vector
|
% Y: pp*1 vector
|
||||||
% trend
|
% trend
|
||||||
% start: likelihood evaluation at 'start'
|
% start: likelihood evaluation at 'start'
|
||||||
%
|
%
|
||||||
% OUTPUTS
|
% OUTPUTS
|
||||||
% LIK: likelihood
|
% LIK: likelihood
|
||||||
% lik: density vector in each period
|
% lik: density vector in each period
|
||||||
%
|
%
|
||||||
% SPECIAL REQUIREMENTS
|
% SPECIAL REQUIREMENTS
|
||||||
% See "Filtering and Smoothing of State Vector for Diffuse State Space
|
% See "Filtering and Smoothing of State Vector for Diffuse State Space
|
||||||
% Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
|
% Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
|
||||||
% Analysis, vol. 24(1), pp. 85-98).
|
% Analysis, vol. 24(1), pp. 85-98).
|
||||||
|
|
||||||
% Copyright (C) 2004-2008 Dynare Team
|
% Copyright (C) 2004-2008 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
% Dynare is free software: you can redistribute it and/or modify
|
||||||
% it under the terms of the GNU General Public License as published by
|
% it under the terms of the GNU General Public License as published by
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
% the Free Software Foundation, either version 3 of the License, or
|
||||||
% (at your option) any later version.
|
% (at your option) any later version.
|
||||||
%
|
%
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
% Dynare is distributed in the hope that it will be useful,
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
% M. Ratto added lik in output
|
% M. Ratto added lik in output
|
||||||
|
|
||||||
global bayestopt_ options_
|
global bayestopt_ options_
|
||||||
|
|
||||||
mf = bayestopt_.mf;
|
mf = bayestopt_.mf;
|
||||||
smpl = size(Y,2);
|
smpl = size(Y,2);
|
||||||
mm = size(T,2);
|
mm = size(T,2);
|
||||||
pp = size(Y,1);
|
pp = size(Y,1);
|
||||||
a = zeros(mm,1);
|
a = zeros(mm,1);
|
||||||
dF = 1;
|
dF = 1;
|
||||||
QQ = R*Q*transpose(R);
|
QQ = R*Q*transpose(R);
|
||||||
t = 0;
|
t = 0;
|
||||||
lik = zeros(smpl,1);
|
lik = zeros(smpl,1);
|
||||||
LIK = Inf;
|
LIK = Inf;
|
||||||
notsteady = 1;
|
notsteady = 1;
|
||||||
crit = options_.kalman_tol;
|
crit = options_.kalman_tol;
|
||||||
while rank(Pinf,crit) & t < smpl
|
while rank(Pinf,crit) & t < smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
v = Y(:,t)-a(mf)-trend(:,t);
|
v = Y(:,t)-a(mf)-trend(:,t);
|
||||||
Finf = Pinf(mf,mf);
|
Finf = Pinf(mf,mf);
|
||||||
if rcond(Finf) < crit
|
if rcond(Finf) < crit
|
||||||
if ~all(abs(Finf(:)) < crit)
|
if ~all(abs(Finf(:)) < crit)
|
||||||
return
|
return
|
||||||
else
|
else
|
||||||
iFstar = inv(Pstar(mf,mf));
|
iFstar = inv(Pstar(mf,mf));
|
||||||
dFstar = det(Pstar(mf,mf));
|
dFstar = det(Pstar(mf,mf));
|
||||||
Kstar = Pstar(:,mf)*iFstar;
|
Kstar = Pstar(:,mf)*iFstar;
|
||||||
lik(t) = log(dFstar) + transpose(v)*iFstar*v;
|
lik(t) = log(dFstar) + transpose(v)*iFstar*v;
|
||||||
Pinf = T*Pinf*transpose(T);
|
Pinf = T*Pinf*transpose(T);
|
||||||
Pstar = T*(Pstar-Pstar(:,mf)*transpose(Kstar))*transpose(T)+QQ;
|
Pstar = T*(Pstar-Pstar(:,mf)*transpose(Kstar))*transpose(T)+QQ;
|
||||||
a = T*(a+Kstar*v);
|
a = T*(a+Kstar*v);
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
lik(t) = log(det(Finf));
|
lik(t) = log(det(Finf));
|
||||||
iFinf = inv(Finf);
|
iFinf = inv(Finf);
|
||||||
Kinf = Pinf(:,mf)*iFinf; %% premultiplication by the transition matrix T is removed (stephane)
|
Kinf = Pinf(:,mf)*iFinf; %% premultiplication by the transition matrix T is removed (stephane)
|
||||||
Fstar = Pstar(mf,mf);
|
Fstar = Pstar(mf,mf);
|
||||||
Kstar = (Pstar(:,mf)-Kinf*Fstar)*iFinf; %% premultiplication by the transition matrix T is removed (stephane)
|
Kstar = (Pstar(:,mf)-Kinf*Fstar)*iFinf; %% premultiplication by the transition matrix T is removed (stephane)
|
||||||
Pstar = T*(Pstar-Pstar(:,mf)*transpose(Kinf)-Pinf(:,mf)*transpose(Kstar))*transpose(T)+QQ;
|
Pstar = T*(Pstar-Pstar(:,mf)*transpose(Kinf)-Pinf(:,mf)*transpose(Kstar))*transpose(T)+QQ;
|
||||||
Pinf = T*(Pinf-Pinf(:,mf)*transpose(Kinf))*transpose(T);
|
Pinf = T*(Pinf-Pinf(:,mf)*transpose(Kinf))*transpose(T);
|
||||||
a = T*(a+Kinf*v);
|
a = T*(a+Kinf*v);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if t == smpl
|
if t == smpl
|
||||||
error(['There isn''t enough information to estimate the initial' ...
|
error(['There isn''t enough information to estimate the initial' ...
|
||||||
' conditions of the nonstationary variables']);
|
' conditions of the nonstationary variables']);
|
||||||
end
|
end
|
||||||
F_singular = 1;
|
F_singular = 1;
|
||||||
while notsteady & t < smpl
|
while notsteady & t < smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
v = Y(:,t)-a(mf)-trend(:,t);
|
v = Y(:,t)-a(mf)-trend(:,t);
|
||||||
F = Pstar(mf,mf);
|
F = Pstar(mf,mf);
|
||||||
oldPstar = Pstar;
|
oldPstar = Pstar;
|
||||||
dF = det(F);
|
dF = det(F);
|
||||||
if rcond(F) < crit
|
if rcond(F) < crit
|
||||||
if ~all(abs(F(:))<crit)
|
if ~all(abs(F(:))<crit)
|
||||||
return
|
return
|
||||||
else
|
else
|
||||||
a = T*a;
|
a = T*a;
|
||||||
Pstar = T*Pstar*transpose(T)+QQ;
|
Pstar = T*Pstar*transpose(T)+QQ;
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
F_singular = 0;
|
F_singular = 0;
|
||||||
iF = inv(F);
|
iF = inv(F);
|
||||||
lik(t) = log(dF)+transpose(v)*iF*v;
|
lik(t) = log(dF)+transpose(v)*iF*v;
|
||||||
K = Pstar(:,mf)*iF; %% premultiplication by the transition matrix T is removed (stephane)
|
K = Pstar(:,mf)*iF; %% premultiplication by the transition matrix T is removed (stephane)
|
||||||
a = T*(a+K*v); %% --> factorization of the transition matrix...
|
a = T*(a+K*v); %% --> factorization of the transition matrix...
|
||||||
Pstar = T*(Pstar-K*Pstar(mf,:))*transpose(T)+QQ; %% ... idem (stephane)
|
Pstar = T*(Pstar-K*Pstar(mf,:))*transpose(T)+QQ; %% ... idem (stephane)
|
||||||
end
|
end
|
||||||
notsteady = ~(max(max(abs(Pstar-oldPstar)))<crit);
|
notsteady = ~(max(max(abs(Pstar-oldPstar)))<crit);
|
||||||
end
|
end
|
||||||
if F_singular == 1
|
if F_singular == 1
|
||||||
error(['The variance of the forecast error remains singular until the' ...
|
error(['The variance of the forecast error remains singular until the' ...
|
||||||
'end of the sample'])
|
'end of the sample'])
|
||||||
end
|
end
|
||||||
if t < smpl
|
if t < smpl
|
||||||
t0 = t+1;
|
t0 = t+1;
|
||||||
while t < smpl
|
while t < smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
v = Y(:,t)-a(mf)-trend(:,t);
|
v = Y(:,t)-a(mf)-trend(:,t);
|
||||||
a = T*(a+K*v);
|
a = T*(a+K*v);
|
||||||
lik(t) = transpose(v)*iF*v;
|
lik(t) = transpose(v)*iF*v;
|
||||||
end
|
end
|
||||||
lik(t0:smpl) = lik(t0:smpl) + log(dF);
|
lik(t0:smpl) = lik(t0:smpl) + log(dF);
|
||||||
end
|
end
|
||||||
% adding log-likelihhod constants
|
% adding log-likelihhod constants
|
||||||
lik = (lik + pp*log(2*pi))/2;
|
lik = (lik + pp*log(2*pi))/2;
|
||||||
|
|
||||||
LIK = sum(lik(start:end)); % Minus the log-likelihood.
|
LIK = sum(lik(start:end)); % Minus the log-likelihood.
|
||||||
|
|
@ -1,130 +1,130 @@
|
||||||
function [LIK, lik] = DiffuseLikelihood1_Z(T,Z,R,Q,Pinf,Pstar,Y,start)
|
function [LIK, lik] = DiffuseLikelihood1_Z(T,Z,R,Q,Pinf,Pstar,Y,start)
|
||||||
|
|
||||||
% function [LIK, lik] = DiffuseLikelihood1_Z(T,Z,R,Q,Pinf,Pstar,Y,start)
|
% function [LIK, lik] = DiffuseLikelihood1_Z(T,Z,R,Q,Pinf,Pstar,Y,start)
|
||||||
% Computes the diffuse likelihood without measurement error, in the case of a non-singular var-cov matrix
|
% Computes the diffuse likelihood without measurement error, in the case of a non-singular var-cov matrix
|
||||||
%
|
%
|
||||||
% INPUTS
|
% INPUTS
|
||||||
% T: mm*mm matrix
|
% T: mm*mm matrix
|
||||||
% Z: pp,mm matrix
|
% Z: pp,mm matrix
|
||||||
% R: mm*rr matrix
|
% R: mm*rr matrix
|
||||||
% Q: rr*rr matrix
|
% Q: rr*rr matrix
|
||||||
% Pinf: mm*mm diagonal matrix with with q ones and m-q zeros
|
% Pinf: mm*mm diagonal matrix with with q ones and m-q zeros
|
||||||
% Pstar: mm*mm variance-covariance matrix with stationary variables
|
% Pstar: mm*mm variance-covariance matrix with stationary variables
|
||||||
% Y: pp*1 vector
|
% Y: pp*1 vector
|
||||||
% start: likelihood evaluation at 'start'
|
% start: likelihood evaluation at 'start'
|
||||||
%
|
%
|
||||||
% OUTPUTS
|
% OUTPUTS
|
||||||
% LIK: likelihood
|
% LIK: likelihood
|
||||||
% lik: density vector in each period
|
% lik: density vector in each period
|
||||||
%
|
%
|
||||||
% SPECIAL REQUIREMENTS
|
% SPECIAL REQUIREMENTS
|
||||||
% See "Filtering and Smoothing of State Vector for Diffuse State Space
|
% See "Filtering and Smoothing of State Vector for Diffuse State Space
|
||||||
% Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
|
% Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
|
||||||
% Analysis, vol. 24(1), pp. 85-98).
|
% Analysis, vol. 24(1), pp. 85-98).
|
||||||
|
|
||||||
% Copyright (C) 2004-2008 Dynare Team
|
% Copyright (C) 2004-2008 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
% Dynare is free software: you can redistribute it and/or modify
|
||||||
% it under the terms of the GNU General Public License as published by
|
% it under the terms of the GNU General Public License as published by
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
% the Free Software Foundation, either version 3 of the License, or
|
||||||
% (at your option) any later version.
|
% (at your option) any later version.
|
||||||
%
|
%
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
% Dynare is distributed in the hope that it will be useful,
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
% M. Ratto added lik in output
|
% M. Ratto added lik in output
|
||||||
|
|
||||||
global bayestopt_ options_
|
global bayestopt_ options_
|
||||||
|
|
||||||
smpl = size(Y,2);
|
smpl = size(Y,2);
|
||||||
mm = size(T,2);
|
mm = size(T,2);
|
||||||
pp = size(Y,1);
|
pp = size(Y,1);
|
||||||
a = zeros(mm,1);
|
a = zeros(mm,1);
|
||||||
dF = 1;
|
dF = 1;
|
||||||
QQ = R*Q*transpose(R);
|
QQ = R*Q*transpose(R);
|
||||||
t = 0;
|
t = 0;
|
||||||
lik = zeros(smpl,1);
|
lik = zeros(smpl,1);
|
||||||
LIK = Inf;
|
LIK = Inf;
|
||||||
notsteady = 1;
|
notsteady = 1;
|
||||||
crit = options_.kalman_tol;
|
crit = options_.kalman_tol;
|
||||||
while rank(Pinf,crit) & t < smpl
|
while rank(Pinf,crit) & t < smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
v = Y(:,t)-Z*a;
|
v = Y(:,t)-Z*a;
|
||||||
Finf = Z*Pinf*Z';
|
Finf = Z*Pinf*Z';
|
||||||
if rcond(Finf) < crit
|
if rcond(Finf) < crit
|
||||||
if ~all(abs(Finf(:)) < crit)
|
if ~all(abs(Finf(:)) < crit)
|
||||||
return
|
return
|
||||||
else
|
else
|
||||||
Fstar = Z*Pstar*Z';
|
Fstar = Z*Pstar*Z';
|
||||||
iFstar = inv(Fstar);
|
iFstar = inv(Fstar);
|
||||||
dFstar = det(Fstar);
|
dFstar = det(Fstar);
|
||||||
Kstar = Pstar*Z'*iFstar;
|
Kstar = Pstar*Z'*iFstar;
|
||||||
lik(t) = log(dFstar) + v'*iFstar*v;
|
lik(t) = log(dFstar) + v'*iFstar*v;
|
||||||
Pinf = T*Pinf*transpose(T);
|
Pinf = T*Pinf*transpose(T);
|
||||||
Pstar = T*(Pstar-Pstar*Z'*Kstar')*T'+QQ;
|
Pstar = T*(Pstar-Pstar*Z'*Kstar')*T'+QQ;
|
||||||
a = T*(a+Kstar*v);
|
a = T*(a+Kstar*v);
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
lik(t) = log(det(Finf));
|
lik(t) = log(det(Finf));
|
||||||
iFinf = inv(Finf);
|
iFinf = inv(Finf);
|
||||||
Kinf = Pinf*Z'*iFinf;
|
Kinf = Pinf*Z'*iFinf;
|
||||||
Fstar = Z*Pstar*Z';
|
Fstar = Z*Pstar*Z';
|
||||||
Kstar = (Pstar*Z'-Kinf*Fstar)*iFinf;
|
Kstar = (Pstar*Z'-Kinf*Fstar)*iFinf;
|
||||||
Pstar = T*(Pstar-Pstar*Z'*Kinf'-Pinf*Z'*Kstar')*T'+QQ;
|
Pstar = T*(Pstar-Pstar*Z'*Kinf'-Pinf*Z'*Kstar')*T'+QQ;
|
||||||
Pinf = T*(Pinf-Pinf*Z'*Kinf')*T';
|
Pinf = T*(Pinf-Pinf*Z'*Kinf')*T';
|
||||||
a = T*(a+Kinf*v);
|
a = T*(a+Kinf*v);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if t == smpl
|
if t == smpl
|
||||||
error(['There isn''t enough information to estimate the initial' ...
|
error(['There isn''t enough information to estimate the initial' ...
|
||||||
' conditions of the nonstationary variables']);
|
' conditions of the nonstationary variables']);
|
||||||
end
|
end
|
||||||
F_singular = 1;
|
F_singular = 1;
|
||||||
while notsteady & t < smpl
|
while notsteady & t < smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
v = Y(:,t)-Z*a;
|
v = Y(:,t)-Z*a;
|
||||||
F = Z*Pstar*Z';
|
F = Z*Pstar*Z';
|
||||||
oldPstar = Pstar;
|
oldPstar = Pstar;
|
||||||
dF = det(F);
|
dF = det(F);
|
||||||
if rcond(F) < crit
|
if rcond(F) < crit
|
||||||
if ~all(abs(F(:))<crit)
|
if ~all(abs(F(:))<crit)
|
||||||
return
|
return
|
||||||
else
|
else
|
||||||
a = T*a;
|
a = T*a;
|
||||||
Pstar = T*Pstar*T'+QQ;
|
Pstar = T*Pstar*T'+QQ;
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
F_singular = 0;
|
F_singular = 0;
|
||||||
iF = inv(F);
|
iF = inv(F);
|
||||||
lik(t) = log(dF)+v'*iF*v;
|
lik(t) = log(dF)+v'*iF*v;
|
||||||
K = Pstar*Z'*iF;
|
K = Pstar*Z'*iF;
|
||||||
a = T*(a+K*v);
|
a = T*(a+K*v);
|
||||||
Pstar = T*(Pstar-K*Z*Pstar)*T'+QQ;
|
Pstar = T*(Pstar-K*Z*Pstar)*T'+QQ;
|
||||||
end
|
end
|
||||||
notsteady = ~(max(max(abs(Pstar-oldPstar)))<crit);
|
notsteady = ~(max(max(abs(Pstar-oldPstar)))<crit);
|
||||||
end
|
end
|
||||||
if F_singular == 1
|
if F_singular == 1
|
||||||
error(['The variance of the forecast error remains singular until the' ...
|
error(['The variance of the forecast error remains singular until the' ...
|
||||||
'end of the sample'])
|
'end of the sample'])
|
||||||
end
|
end
|
||||||
if t < smpl
|
if t < smpl
|
||||||
t0 = t+1;
|
t0 = t+1;
|
||||||
while t < smpl
|
while t < smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
v = Y(:,t)-Z*a;
|
v = Y(:,t)-Z*a;
|
||||||
a = T*(a+K*v);
|
a = T*(a+K*v);
|
||||||
lik(t) = v'*iF*v;
|
lik(t) = v'*iF*v;
|
||||||
end
|
end
|
||||||
lik(t0:smpl) = lik(t0:smpl) + log(dF);
|
lik(t0:smpl) = lik(t0:smpl) + log(dF);
|
||||||
end
|
end
|
||||||
% adding log-likelihhod constants
|
% adding log-likelihhod constants
|
||||||
lik = (lik + pp*log(2*pi))/2;
|
lik = (lik + pp*log(2*pi))/2;
|
||||||
|
|
||||||
LIK = sum(lik(start:end)); % Minus the log-likelihood.
|
LIK = sum(lik(start:end)); % Minus the log-likelihood.
|
||||||
|
|
@ -1,195 +1,195 @@
|
||||||
function [LIK, lik] = DiffuseLikelihood3(T,R,Q,Pinf,Pstar,Y,trend,start)%//Z,T,R,Q,Pinf,Pstar,Y)
|
function [LIK, lik] = DiffuseLikelihood3(T,R,Q,Pinf,Pstar,Y,trend,start)%//Z,T,R,Q,Pinf,Pstar,Y)
|
||||||
|
|
||||||
% function [LIK, lik] = DiffuseLikelihood3(T,R,Q,Pinf,Pstar,Y,trend,start)
|
% function [LIK, lik] = DiffuseLikelihood3(T,R,Q,Pinf,Pstar,Y,trend,start)
|
||||||
% Computes the diffuse likelihood without measurement error, in the case of
|
% Computes the diffuse likelihood without measurement error, in the case of
|
||||||
% a singular var-cov matrix.
|
% a singular var-cov matrix.
|
||||||
% Univariate treatment of multivariate time series.
|
% Univariate treatment of multivariate time series.
|
||||||
%
|
%
|
||||||
% INPUTS
|
% INPUTS
|
||||||
% T: mm*mm matrix
|
% T: mm*mm matrix
|
||||||
% R: mm*rr matrix
|
% R: mm*rr matrix
|
||||||
% Q: rr*rr matrix
|
% Q: rr*rr matrix
|
||||||
% Pinf: mm*mm diagonal matrix with with q ones and m-q zeros
|
% Pinf: mm*mm diagonal matrix with with q ones and m-q zeros
|
||||||
% Pstar: mm*mm variance-covariance matrix with stationary variables
|
% Pstar: mm*mm variance-covariance matrix with stationary variables
|
||||||
% Y: pp*1 vector
|
% Y: pp*1 vector
|
||||||
% trend
|
% trend
|
||||||
% start: likelihood evaluation at 'start'
|
% start: likelihood evaluation at 'start'
|
||||||
%
|
%
|
||||||
% OUTPUTS
|
% OUTPUTS
|
||||||
% LIK: likelihood
|
% LIK: likelihood
|
||||||
% lik: density vector in each period
|
% lik: density vector in each period
|
||||||
%
|
%
|
||||||
% SPECIAL REQUIREMENTS
|
% SPECIAL REQUIREMENTS
|
||||||
% See "Filtering and Smoothing of State Vector for Diffuse State Space
|
% See "Filtering and Smoothing of State Vector for Diffuse State Space
|
||||||
% Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
|
% Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
|
||||||
% Analysis, vol. 24(1), pp. 85-98).
|
% Analysis, vol. 24(1), pp. 85-98).
|
||||||
|
|
||||||
% Copyright (C) 2004-2008 Dynare Team
|
% Copyright (C) 2004-2008 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
% Dynare is free software: you can redistribute it and/or modify
|
||||||
% it under the terms of the GNU General Public License as published by
|
% it under the terms of the GNU General Public License as published by
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
% the Free Software Foundation, either version 3 of the License, or
|
||||||
% (at your option) any later version.
|
% (at your option) any later version.
|
||||||
%
|
%
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
% Dynare is distributed in the hope that it will be useful,
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
% M. Ratto added lik in output [October 2005]
|
% M. Ratto added lik in output [October 2005]
|
||||||
% changes by M. Ratto [April 2005]
|
% changes by M. Ratto [April 2005]
|
||||||
% introduced new options options_.diffuse_d for termination of DKF
|
% introduced new options options_.diffuse_d for termination of DKF
|
||||||
% new icc counter for Finf steps in DKF
|
% new icc counter for Finf steps in DKF
|
||||||
% new termination for DKF
|
% new termination for DKF
|
||||||
% likelihood terms for Fstar must be cumulated in DKF also when Pinf is non
|
% likelihood terms for Fstar must be cumulated in DKF also when Pinf is non
|
||||||
% zero.
|
% zero.
|
||||||
% [4/5/2005] correctyed bug in the modified verson of Ratto for rank of Pinf
|
% [4/5/2005] correctyed bug in the modified verson of Ratto for rank of Pinf
|
||||||
% introduced a specific crit1 for the DKF termination
|
% introduced a specific crit1 for the DKF termination
|
||||||
|
|
||||||
|
|
||||||
global bayestopt_ options_
|
global bayestopt_ options_
|
||||||
|
|
||||||
mf = bayestopt_.mf;
|
mf = bayestopt_.mf;
|
||||||
pp = size(Y,1);
|
pp = size(Y,1);
|
||||||
mm = size(T,1);
|
mm = size(T,1);
|
||||||
smpl = size(Y,2);
|
smpl = size(Y,2);
|
||||||
a = zeros(mm,1);
|
a = zeros(mm,1);
|
||||||
QQ = R*Q*transpose(R);
|
QQ = R*Q*transpose(R);
|
||||||
t = 0;
|
t = 0;
|
||||||
lik = zeros(smpl,1);
|
lik = zeros(smpl,1);
|
||||||
notsteady = 1;
|
notsteady = 1;
|
||||||
crit = options_.kalman_tol;
|
crit = options_.kalman_tol;
|
||||||
crit1 = 1.e-6;
|
crit1 = 1.e-6;
|
||||||
newRank = rank(Pinf,crit1);
|
newRank = rank(Pinf,crit1);
|
||||||
icc=0;
|
icc=0;
|
||||||
while newRank & t < smpl
|
while newRank & t < smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
for i=1:pp
|
for i=1:pp
|
||||||
v(i) = Y(i,t)-a(mf(i))-trend(i,t);
|
v(i) = Y(i,t)-a(mf(i))-trend(i,t);
|
||||||
Fstar = Pstar(mf(i),mf(i));
|
Fstar = Pstar(mf(i),mf(i));
|
||||||
Finf = Pinf(mf(i),mf(i));
|
Finf = Pinf(mf(i),mf(i));
|
||||||
Kstar = Pstar(:,mf(i));
|
Kstar = Pstar(:,mf(i));
|
||||||
if Finf > crit & newRank, %added newRank criterion
|
if Finf > crit & newRank, %added newRank criterion
|
||||||
icc=icc+1;
|
icc=icc+1;
|
||||||
Kinf = Pinf(:,mf(i));
|
Kinf = Pinf(:,mf(i));
|
||||||
a = a + Kinf*v(i)/Finf;
|
a = a + Kinf*v(i)/Finf;
|
||||||
Pstar = Pstar + Kinf*transpose(Kinf)*Fstar/(Finf*Finf) - ...
|
Pstar = Pstar + Kinf*transpose(Kinf)*Fstar/(Finf*Finf) - ...
|
||||||
(Kstar*transpose(Kinf)+Kinf*transpose(Kstar))/Finf;
|
(Kstar*transpose(Kinf)+Kinf*transpose(Kstar))/Finf;
|
||||||
Pinf = Pinf - Kinf*transpose(Kinf)/Finf;
|
Pinf = Pinf - Kinf*transpose(Kinf)/Finf;
|
||||||
lik(t) = lik(t) + log(Finf);
|
lik(t) = lik(t) + log(Finf);
|
||||||
% start new termination criterion for DKF
|
% start new termination criterion for DKF
|
||||||
if ~isempty(options_.diffuse_d),
|
if ~isempty(options_.diffuse_d),
|
||||||
newRank = (icc<options_.diffuse_d);
|
newRank = (icc<options_.diffuse_d);
|
||||||
%if newRank & any(diag(Pinf(mf,mf))>crit)==0; % M. Ratto this line is BUGGY
|
%if newRank & any(diag(Pinf(mf,mf))>crit)==0; % M. Ratto this line is BUGGY
|
||||||
if newRank & (any(diag(Pinf(mf,mf))>crit)==0 & rank(Pinf,crit1)==0);
|
if newRank & (any(diag(Pinf(mf,mf))>crit)==0 & rank(Pinf,crit1)==0);
|
||||||
options_.diffuse_d = icc;
|
options_.diffuse_d = icc;
|
||||||
newRank=0;
|
newRank=0;
|
||||||
disp('WARNING: Change in OPTIONS_.DIFFUSE_D in univariate DKF')
|
disp('WARNING: Change in OPTIONS_.DIFFUSE_D in univariate DKF')
|
||||||
disp(['new OPTIONS_.DIFFUSE_D = ',int2str(icc)])
|
disp(['new OPTIONS_.DIFFUSE_D = ',int2str(icc)])
|
||||||
disp('You may have to reset the optimisation')
|
disp('You may have to reset the optimisation')
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
%newRank = any(diag(Pinf(mf,mf))>crit); % M. Ratto this line is BUGGY
|
%newRank = any(diag(Pinf(mf,mf))>crit); % M. Ratto this line is BUGGY
|
||||||
newRank = (any(diag(Pinf(mf,mf))>crit) | rank(Pinf,crit1));
|
newRank = (any(diag(Pinf(mf,mf))>crit) | rank(Pinf,crit1));
|
||||||
if newRank==0,
|
if newRank==0,
|
||||||
P0= T*Pinf*transpose(T);
|
P0= T*Pinf*transpose(T);
|
||||||
%newRank = any(diag(P0(mf,mf))>crit); % M. Ratto this line is BUGGY
|
%newRank = any(diag(P0(mf,mf))>crit); % M. Ratto this line is BUGGY
|
||||||
newRank = (any(diag(P0(mf,mf))>crit) | rank(P0,crit1)); % M. Ratto 11/10/2005
|
newRank = (any(diag(P0(mf,mf))>crit) | rank(P0,crit1)); % M. Ratto 11/10/2005
|
||||||
if newRank==0,
|
if newRank==0,
|
||||||
options_.diffuse_d = icc;
|
options_.diffuse_d = icc;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end,
|
end,
|
||||||
% end new termination and checks for DKF
|
% end new termination and checks for DKF
|
||||||
elseif Fstar > crit
|
elseif Fstar > crit
|
||||||
%% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition
|
%% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition
|
||||||
%% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that
|
%% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that
|
||||||
%% rank(Pinf)=0. [stéphane,11-03-2004].
|
%% rank(Pinf)=0. [stéphane,11-03-2004].
|
||||||
%if rank(Pinf,crit) == 0
|
%if rank(Pinf,crit) == 0
|
||||||
% the likelihood terms should alwasy be cumulated, not only
|
% the likelihood terms should alwasy be cumulated, not only
|
||||||
% when Pinf=0, otherwise the lik would depend on the ordering
|
% when Pinf=0, otherwise the lik would depend on the ordering
|
||||||
% of observed variables
|
% of observed variables
|
||||||
% presample options can be used to ignore initial time points
|
% presample options can be used to ignore initial time points
|
||||||
lik(t) = lik(t) + log(Fstar) + v(i)*v(i)/Fstar;
|
lik(t) = lik(t) + log(Fstar) + v(i)*v(i)/Fstar;
|
||||||
%end
|
%end
|
||||||
a = a + Kstar*v(i)/Fstar;
|
a = a + Kstar*v(i)/Fstar;
|
||||||
Pstar = Pstar - Kstar*transpose(Kstar)/Fstar;
|
Pstar = Pstar - Kstar*transpose(Kstar)/Fstar;
|
||||||
else
|
else
|
||||||
%disp(['zero F term in DKF for observed ',int2str(i),' ',num2str(Fstar)])
|
%disp(['zero F term in DKF for observed ',int2str(i),' ',num2str(Fstar)])
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
% if all(abs(Pinf(:))<crit),
|
% if all(abs(Pinf(:))<crit),
|
||||||
% oldRank = 0;
|
% oldRank = 0;
|
||||||
% else
|
% else
|
||||||
% oldRank = rank(Pinf,crit);
|
% oldRank = rank(Pinf,crit);
|
||||||
% end
|
% end
|
||||||
if newRank,
|
if newRank,
|
||||||
oldRank = rank(Pinf,crit1);
|
oldRank = rank(Pinf,crit1);
|
||||||
else
|
else
|
||||||
oldRank = 0;
|
oldRank = 0;
|
||||||
end
|
end
|
||||||
a = T*a;
|
a = T*a;
|
||||||
Pstar = T*Pstar*transpose(T)+QQ;
|
Pstar = T*Pstar*transpose(T)+QQ;
|
||||||
Pinf = T*Pinf*transpose(T);
|
Pinf = T*Pinf*transpose(T);
|
||||||
% if all(abs(Pinf(:))<crit),
|
% if all(abs(Pinf(:))<crit),
|
||||||
% newRank = 0;
|
% newRank = 0;
|
||||||
% else
|
% else
|
||||||
% newRank = rank(Pinf,crit);
|
% newRank = rank(Pinf,crit);
|
||||||
% end
|
% end
|
||||||
if newRank,
|
if newRank,
|
||||||
newRank = rank(Pinf,crit1); % new crit1 is used
|
newRank = rank(Pinf,crit1); % new crit1 is used
|
||||||
end
|
end
|
||||||
if oldRank ~= newRank
|
if oldRank ~= newRank
|
||||||
disp('DiffuseLiklihood3 :: T does influence the rank of Pinf!')
|
disp('DiffuseLiklihood3 :: T does influence the rank of Pinf!')
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if t == smpl
|
if t == smpl
|
||||||
error(['There isn''t enough information to estimate the initial' ...
|
error(['There isn''t enough information to estimate the initial' ...
|
||||||
' conditions of the nonstationary variables']);
|
' conditions of the nonstationary variables']);
|
||||||
end
|
end
|
||||||
while notsteady & t < smpl
|
while notsteady & t < smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
oldP = Pstar;
|
oldP = Pstar;
|
||||||
for i=1:pp
|
for i=1:pp
|
||||||
v(i) = Y(i,t) - a(mf(i)) - trend(i,t);
|
v(i) = Y(i,t) - a(mf(i)) - trend(i,t);
|
||||||
Fi = Pstar(mf(i),mf(i));
|
Fi = Pstar(mf(i),mf(i));
|
||||||
if Fi > crit
|
if Fi > crit
|
||||||
Ki = Pstar(:,mf(i));
|
Ki = Pstar(:,mf(i));
|
||||||
a = a + Ki*v(i)/Fi;
|
a = a + Ki*v(i)/Fi;
|
||||||
Pstar = Pstar - Ki*transpose(Ki)/Fi;
|
Pstar = Pstar - Ki*transpose(Ki)/Fi;
|
||||||
lik(t) = lik(t) + log(Fi) + v(i)*v(i)/Fi;
|
lik(t) = lik(t) + log(Fi) + v(i)*v(i)/Fi;
|
||||||
else
|
else
|
||||||
%disp(['zero F term for observed ',int2str(i),' ',num2str(Fi)])
|
%disp(['zero F term for observed ',int2str(i),' ',num2str(Fi)])
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
a = T*a;
|
a = T*a;
|
||||||
Pstar = T*Pstar*transpose(T) + QQ;
|
Pstar = T*Pstar*transpose(T) + QQ;
|
||||||
notsteady = ~(max(max(abs(Pstar-oldP)))<crit);
|
notsteady = ~(max(max(abs(Pstar-oldP)))<crit);
|
||||||
end
|
end
|
||||||
while t < smpl
|
while t < smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
Pstar = oldP;
|
Pstar = oldP;
|
||||||
for i=1:pp
|
for i=1:pp
|
||||||
v(i) = Y(i,t) - a(mf(i)) - trend(i,t);
|
v(i) = Y(i,t) - a(mf(i)) - trend(i,t);
|
||||||
Fi = Pstar(mf(i),mf(i));
|
Fi = Pstar(mf(i),mf(i));
|
||||||
if Fi > crit
|
if Fi > crit
|
||||||
Ki = Pstar(:,mf(i));
|
Ki = Pstar(:,mf(i));
|
||||||
a = a + Ki*v(i)/Fi;
|
a = a + Ki*v(i)/Fi;
|
||||||
Pstar = Pstar - Ki*transpose(Ki)/Fi;
|
Pstar = Pstar - Ki*transpose(Ki)/Fi;
|
||||||
lik(t) = lik(t) + log(Fi) + v(i)*v(i)/Fi;
|
lik(t) = lik(t) + log(Fi) + v(i)*v(i)/Fi;
|
||||||
else
|
else
|
||||||
%disp(['zero F term for observed ',int2str(i),' ',num2str(Fi)])
|
%disp(['zero F term for observed ',int2str(i),' ',num2str(Fi)])
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
a = T*a;
|
a = T*a;
|
||||||
end
|
end
|
||||||
|
|
||||||
% adding log-likelihhod constants
|
% adding log-likelihhod constants
|
||||||
lik = (lik + pp*log(2*pi))/2;
|
lik = (lik + pp*log(2*pi))/2;
|
||||||
|
|
||||||
LIK = sum(lik(start:end)); % Minus the log-likelihood.
|
LIK = sum(lik(start:end)); % Minus the log-likelihood.
|
||||||
|
|
|
||||||
|
|
@ -1,180 +1,180 @@
|
||||||
function [LIK, lik] = DiffuseLikelihood3_Z(T,Z,R,Q,Pinf,Pstar,Y,start)
|
function [LIK, lik] = DiffuseLikelihood3_Z(T,Z,R,Q,Pinf,Pstar,Y,start)
|
||||||
|
|
||||||
% function [LIK, lik] = DiffuseLikelihood3_Z(T,Z,R,Q,Pinf,Pstar,Y,start)
|
% function [LIK, lik] = DiffuseLikelihood3_Z(T,Z,R,Q,Pinf,Pstar,Y,start)
|
||||||
% Computes the diffuse likelihood without measurement error, in the case of
|
% Computes the diffuse likelihood without measurement error, in the case of
|
||||||
% a singular var-cov matrix.
|
% a singular var-cov matrix.
|
||||||
% Univariate treatment of multivariate time series.
|
% Univariate treatment of multivariate time series.
|
||||||
%
|
%
|
||||||
% INPUTS
|
% INPUTS
|
||||||
% T: mm*mm matrix
|
% T: mm*mm matrix
|
||||||
% Z: pp*mm matrix
|
% Z: pp*mm matrix
|
||||||
% R: mm*rr matrix
|
% R: mm*rr matrix
|
||||||
% Q: rr*rr matrix
|
% Q: rr*rr matrix
|
||||||
% Pinf: mm*mm diagonal matrix with with q ones and m-q zeros
|
% Pinf: mm*mm diagonal matrix with with q ones and m-q zeros
|
||||||
% Pstar: mm*mm variance-covariance matrix with stationary variables
|
% Pstar: mm*mm variance-covariance matrix with stationary variables
|
||||||
% Y: pp*1 vector
|
% Y: pp*1 vector
|
||||||
% start: likelihood evaluation at 'start'
|
% start: likelihood evaluation at 'start'
|
||||||
%
|
%
|
||||||
% OUTPUTS
|
% OUTPUTS
|
||||||
% LIK: likelihood
|
% LIK: likelihood
|
||||||
% lik: density vector in each period
|
% lik: density vector in each period
|
||||||
%
|
%
|
||||||
% SPECIAL REQUIREMENTS
|
% SPECIAL REQUIREMENTS
|
||||||
% See "Filtering and Smoothing of State Vector for Diffuse State Space
|
% See "Filtering and Smoothing of State Vector for Diffuse State Space
|
||||||
% Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
|
% Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
|
||||||
% Analysis, vol. 24(1), pp. 85-98).
|
% Analysis, vol. 24(1), pp. 85-98).
|
||||||
|
|
||||||
% Copyright (C) 2004-2008 Dynare Team
|
% Copyright (C) 2004-2008 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
% Dynare is free software: you can redistribute it and/or modify
|
||||||
% it under the terms of the GNU General Public License as published by
|
% it under the terms of the GNU General Public License as published by
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
% the Free Software Foundation, either version 3 of the License, or
|
||||||
% (at your option) any later version.
|
% (at your option) any later version.
|
||||||
%
|
%
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
% Dynare is distributed in the hope that it will be useful,
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
% M. Ratto added lik in output [October 2005]
|
% M. Ratto added lik in output [October 2005]
|
||||||
% changes by M. Ratto [April 2005]
|
% changes by M. Ratto [April 2005]
|
||||||
% introduced new options options_.diffuse_d for termination of DKF
|
% introduced new options options_.diffuse_d for termination of DKF
|
||||||
% new icc counter for Finf steps in DKF
|
% new icc counter for Finf steps in DKF
|
||||||
% new termination for DKF
|
% new termination for DKF
|
||||||
% likelihood terms for Fstar must be cumulated in DKF also when Pinf is non
|
% likelihood terms for Fstar must be cumulated in DKF also when Pinf is non
|
||||||
% zero.
|
% zero.
|
||||||
% [4/5/2005] correctyed bug in the modified verson of Ratto for rank of Pinf
|
% [4/5/2005] correctyed bug in the modified verson of Ratto for rank of Pinf
|
||||||
% introduced a specific crit1 for the DKF termination
|
% introduced a specific crit1 for the DKF termination
|
||||||
|
|
||||||
|
|
||||||
global bayestopt_ options_
|
global bayestopt_ options_
|
||||||
|
|
||||||
pp = size(Y,1);
|
pp = size(Y,1);
|
||||||
mm = size(T,1);
|
mm = size(T,1);
|
||||||
smpl = size(Y,2);
|
smpl = size(Y,2);
|
||||||
a = zeros(mm,1);
|
a = zeros(mm,1);
|
||||||
QQ = R*Q*R';
|
QQ = R*Q*R';
|
||||||
t = 0;
|
t = 0;
|
||||||
lik = zeros(smpl,1);
|
lik = zeros(smpl,1);
|
||||||
notsteady = 1;
|
notsteady = 1;
|
||||||
crit = options_.kalman_tol;
|
crit = options_.kalman_tol;
|
||||||
crit1 = 1.e-6;
|
crit1 = 1.e-6;
|
||||||
newRank = rank(Pinf,crit1);
|
newRank = rank(Pinf,crit1);
|
||||||
icc=0;
|
icc=0;
|
||||||
while newRank & t < smpl
|
while newRank & t < smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
for i=1:pp
|
for i=1:pp
|
||||||
Zi = Z(i,:);
|
Zi = Z(i,:);
|
||||||
v(i) = Y(i,t)-Zi*a;
|
v(i) = Y(i,t)-Zi*a;
|
||||||
Fstar = Zi*Pstar*Zi';
|
Fstar = Zi*Pstar*Zi';
|
||||||
Finf = Zi*Pinf*Zi';
|
Finf = Zi*Pinf*Zi';
|
||||||
Kstar = Pstar*Zi';
|
Kstar = Pstar*Zi';
|
||||||
if Finf > crit & newRank
|
if Finf > crit & newRank
|
||||||
icc=icc+1;
|
icc=icc+1;
|
||||||
Kinf = Pinf*Zi';
|
Kinf = Pinf*Zi';
|
||||||
a = a + Kinf*v(i)/Finf;
|
a = a + Kinf*v(i)/Finf;
|
||||||
Pstar = Pstar + Kinf*Kinf'*Fstar/(Finf*Finf) - ...
|
Pstar = Pstar + Kinf*Kinf'*Fstar/(Finf*Finf) - ...
|
||||||
(Kstar*Kinf'+Kinf*Kstar')/Finf;
|
(Kstar*Kinf'+Kinf*Kstar')/Finf;
|
||||||
Pinf = Pinf - Kinf*Kinf'/Finf;
|
Pinf = Pinf - Kinf*Kinf'/Finf;
|
||||||
lik(t) = lik(t) + log(Finf);
|
lik(t) = lik(t) + log(Finf);
|
||||||
if ~isempty(options_.diffuse_d),
|
if ~isempty(options_.diffuse_d),
|
||||||
newRank = (icc<options_.diffuse_d);
|
newRank = (icc<options_.diffuse_d);
|
||||||
if newRank & (any(diag(Z*Pinf*Z')>crit)==0 & rank(Pinf,crit1)==0);
|
if newRank & (any(diag(Z*Pinf*Z')>crit)==0 & rank(Pinf,crit1)==0);
|
||||||
options_.diffuse_d = icc;
|
options_.diffuse_d = icc;
|
||||||
newRank=0;
|
newRank=0;
|
||||||
disp('WARNING: Change in OPTIONS_.DIFFUSE_D in univariate DKF')
|
disp('WARNING: Change in OPTIONS_.DIFFUSE_D in univariate DKF')
|
||||||
disp(['new OPTIONS_.DIFFUSE_D = ',int2str(icc)])
|
disp(['new OPTIONS_.DIFFUSE_D = ',int2str(icc)])
|
||||||
disp('You may have to reset the optimisation')
|
disp('You may have to reset the optimisation')
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
newRank = (any(diag(Z*Pinf*Z')>crit) | rank(Pinf,crit1));
|
newRank = (any(diag(Z*Pinf*Z')>crit) | rank(Pinf,crit1));
|
||||||
if newRank==0,
|
if newRank==0,
|
||||||
P0= T*Pinf*T';
|
P0= T*Pinf*T';
|
||||||
newRank = (any(diag(Z*P0*Z')>crit) | rank(P0,crit1)); % M. Ratto 11/10/2005
|
newRank = (any(diag(Z*P0*Z')>crit) | rank(P0,crit1)); % M. Ratto 11/10/2005
|
||||||
if newRank==0,
|
if newRank==0,
|
||||||
options_.diffuse_d = icc;
|
options_.diffuse_d = icc;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end,
|
end,
|
||||||
elseif Fstar > crit
|
elseif Fstar > crit
|
||||||
%% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition
|
%% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition
|
||||||
%% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that
|
%% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that
|
||||||
%% rank(Pinf)=0. [stéphane,11-03-2004].
|
%% rank(Pinf)=0. [stéphane,11-03-2004].
|
||||||
%if rank(Pinf,crit) == 0
|
%if rank(Pinf,crit) == 0
|
||||||
% the likelihood terms should alwasy be cumulated, not only
|
% the likelihood terms should alwasy be cumulated, not only
|
||||||
% when Pinf=0, otherwise the lik would depend on the ordering
|
% when Pinf=0, otherwise the lik would depend on the ordering
|
||||||
% of observed variables
|
% of observed variables
|
||||||
% presample options can be used to ignore initial time points
|
% presample options can be used to ignore initial time points
|
||||||
lik(t) = lik(t) + log(Fstar) + v(i)*v(i)/Fstar;
|
lik(t) = lik(t) + log(Fstar) + v(i)*v(i)/Fstar;
|
||||||
a = a + Kstar*v(i)/Fstar;
|
a = a + Kstar*v(i)/Fstar;
|
||||||
Pstar = Pstar - Kstar*(Kstar'/Fstar);
|
Pstar = Pstar - Kstar*(Kstar'/Fstar);
|
||||||
else
|
else
|
||||||
%disp(['zero F term in DKF for observed ',int2str(i),' ',num2str(Fstar)])
|
%disp(['zero F term in DKF for observed ',int2str(i),' ',num2str(Fstar)])
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if newRank,
|
if newRank,
|
||||||
oldRank = rank(Pinf,crit1);
|
oldRank = rank(Pinf,crit1);
|
||||||
else
|
else
|
||||||
oldRank = 0;
|
oldRank = 0;
|
||||||
end
|
end
|
||||||
a = T*a;
|
a = T*a;
|
||||||
Pstar = T*Pstar*T'+QQ;
|
Pstar = T*Pstar*T'+QQ;
|
||||||
Pinf = T*Pinf*T';
|
Pinf = T*Pinf*T';
|
||||||
if newRank,
|
if newRank,
|
||||||
newRank = rank(Pinf,crit1);
|
newRank = rank(Pinf,crit1);
|
||||||
end
|
end
|
||||||
if oldRank ~= newRank
|
if oldRank ~= newRank
|
||||||
disp('DiffuseLiklihood3 :: T does influence the rank of Pinf!')
|
disp('DiffuseLiklihood3 :: T does influence the rank of Pinf!')
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if t == smpl
|
if t == smpl
|
||||||
error(['There isn''t enough information to estimate the initial' ...
|
error(['There isn''t enough information to estimate the initial' ...
|
||||||
' conditions of the nonstationary variables']);
|
' conditions of the nonstationary variables']);
|
||||||
end
|
end
|
||||||
while notsteady & t < smpl
|
while notsteady & t < smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
oldP = Pstar;
|
oldP = Pstar;
|
||||||
for i=1:pp
|
for i=1:pp
|
||||||
Zi = Z(i,:);
|
Zi = Z(i,:);
|
||||||
v(i) = Y(i,t) - Zi*a;
|
v(i) = Y(i,t) - Zi*a;
|
||||||
Fi = Zi*Pstar*Zi';
|
Fi = Zi*Pstar*Zi';
|
||||||
if Fi > crit
|
if Fi > crit
|
||||||
Ki = Pstar*Zi';
|
Ki = Pstar*Zi';
|
||||||
a = a + Ki*v(i)/Fi;
|
a = a + Ki*v(i)/Fi;
|
||||||
Pstar = Pstar - Ki*(Ki'/Fi);
|
Pstar = Pstar - Ki*(Ki'/Fi);
|
||||||
lik(t) = lik(t) + log(Fi) + v(i)*v(i)/Fi;
|
lik(t) = lik(t) + log(Fi) + v(i)*v(i)/Fi;
|
||||||
else
|
else
|
||||||
%disp(['zero F term for observed ',int2str(i),' ',num2str(Fi)])
|
%disp(['zero F term for observed ',int2str(i),' ',num2str(Fi)])
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
a = T*a;
|
a = T*a;
|
||||||
Pstar = T*Pstar*T' + QQ;
|
Pstar = T*Pstar*T' + QQ;
|
||||||
notsteady = ~(max(max(abs(Pstar-oldP)))<crit);
|
notsteady = ~(max(max(abs(Pstar-oldP)))<crit);
|
||||||
end
|
end
|
||||||
while t < smpl
|
while t < smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
Pstar = oldP;
|
Pstar = oldP;
|
||||||
for i=1:pp
|
for i=1:pp
|
||||||
Zi = Z(i,:);
|
Zi = Z(i,:);
|
||||||
v(i) = Y(i,t) - Zi*a;
|
v(i) = Y(i,t) - Zi*a;
|
||||||
Fi = Zi*Pstar*Zi';
|
Fi = Zi*Pstar*Zi';
|
||||||
if Fi > crit
|
if Fi > crit
|
||||||
Ki = Pstar*Zi';
|
Ki = Pstar*Zi';
|
||||||
a = a + Ki*v(i)/Fi;
|
a = a + Ki*v(i)/Fi;
|
||||||
Pstar = Pstar - Ki*Ki'/Fi;
|
Pstar = Pstar - Ki*Ki'/Fi;
|
||||||
lik(t) = lik(t) + log(Fi) + v(i)*v(i)/Fi;
|
lik(t) = lik(t) + log(Fi) + v(i)*v(i)/Fi;
|
||||||
else
|
else
|
||||||
%disp(['zero F term for observed ',int2str(i),' ',num2str(Fi)])
|
%disp(['zero F term for observed ',int2str(i),' ',num2str(Fi)])
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
a = T*a;
|
a = T*a;
|
||||||
end
|
end
|
||||||
% adding log-likelihhod constants
|
% adding log-likelihhod constants
|
||||||
lik = (lik + pp*log(2*pi))/2;
|
lik = (lik + pp*log(2*pi))/2;
|
||||||
|
|
||||||
LIK = sum(lik(start:end)); % Minus the log-likelihood.
|
LIK = sum(lik(start:end)); % Minus the log-likelihood.
|
||||||
|
|
|
||||||
|
|
@ -1,131 +1,131 @@
|
||||||
function [LIK, lik] = DiffuseLikelihoodH1(T,R,Q,H,Pinf,Pstar,Y,trend,start)
|
function [LIK, lik] = DiffuseLikelihoodH1(T,R,Q,H,Pinf,Pstar,Y,trend,start)
|
||||||
|
|
||||||
% function [LIK, lik] = DiffuseLikelihoodH1(T,R,Q,H,Pinf,Pstar,Y,trend,start)
|
% function [LIK, lik] = DiffuseLikelihoodH1(T,R,Q,H,Pinf,Pstar,Y,trend,start)
|
||||||
% Computes the diffuse likelihood (H=measurement error) in the case of a non-singular var-cov matrix
|
% Computes the diffuse likelihood (H=measurement error) in the case of a non-singular var-cov matrix
|
||||||
%
|
%
|
||||||
% INPUTS
|
% INPUTS
|
||||||
% T: mm*mm matrix
|
% T: mm*mm matrix
|
||||||
% R: mm*rr matrix
|
% R: mm*rr matrix
|
||||||
% Q: rr*rr matrix
|
% Q: rr*rr matrix
|
||||||
% H: pp*pp matrix
|
% H: pp*pp matrix
|
||||||
% Pinf: mm*mm diagonal matrix with with q ones and m-q zeros
|
% Pinf: mm*mm diagonal matrix with with q ones and m-q zeros
|
||||||
% Pstar: mm*mm variance-covariance matrix with stationary variables
|
% Pstar: mm*mm variance-covariance matrix with stationary variables
|
||||||
% Y: pp*1 vector
|
% Y: pp*1 vector
|
||||||
% trend
|
% trend
|
||||||
% start: likelihood evaluation at 'start'
|
% start: likelihood evaluation at 'start'
|
||||||
%
|
%
|
||||||
% OUTPUTS
|
% OUTPUTS
|
||||||
% LIK: likelihood
|
% LIK: likelihood
|
||||||
% lik: density vector in each period
|
% lik: density vector in each period
|
||||||
%
|
%
|
||||||
% SPECIAL REQUIREMENTS
|
% SPECIAL REQUIREMENTS
|
||||||
% See "Filtering and Smoothing of State Vector for Diffuse State Space
|
% See "Filtering and Smoothing of State Vector for Diffuse State Space
|
||||||
% Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
|
% Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
|
||||||
% Analysis, vol. 24(1), pp. 85-98).
|
% Analysis, vol. 24(1), pp. 85-98).
|
||||||
|
|
||||||
% Copyright (C) 2005-2008 Dynare Team
|
% Copyright (C) 2005-2008 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
% Dynare is free software: you can redistribute it and/or modify
|
||||||
% it under the terms of the GNU General Public License as published by
|
% it under the terms of the GNU General Public License as published by
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
% the Free Software Foundation, either version 3 of the License, or
|
||||||
% (at your option) any later version.
|
% (at your option) any later version.
|
||||||
%
|
%
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
% Dynare is distributed in the hope that it will be useful,
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
% M. Ratto added lik in output
|
% M. Ratto added lik in output
|
||||||
|
|
||||||
global bayestopt_ options_
|
global bayestopt_ options_
|
||||||
|
|
||||||
mf = bayestopt_.mf;
|
mf = bayestopt_.mf;
|
||||||
smpl = size(Y,2);
|
smpl = size(Y,2);
|
||||||
mm = size(T,2);
|
mm = size(T,2);
|
||||||
pp = size(Y,1);
|
pp = size(Y,1);
|
||||||
a = zeros(mm,1);
|
a = zeros(mm,1);
|
||||||
dF = 1;
|
dF = 1;
|
||||||
QQ = R*Q*transpose(R);
|
QQ = R*Q*transpose(R);
|
||||||
t = 0;
|
t = 0;
|
||||||
lik = zeros(smpl,1);
|
lik = zeros(smpl,1);
|
||||||
LIK = Inf;
|
LIK = Inf;
|
||||||
notsteady = 1;
|
notsteady = 1;
|
||||||
crit = options_.kalman_tol;
|
crit = options_.kalman_tol;
|
||||||
while rank(Pinf,crit) & t < smpl
|
while rank(Pinf,crit) & t < smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
v = Y(:,t)-a(mf)-trend(:,t);
|
v = Y(:,t)-a(mf)-trend(:,t);
|
||||||
Finf = Pinf(mf,mf);
|
Finf = Pinf(mf,mf);
|
||||||
if rcond(Finf) < crit
|
if rcond(Finf) < crit
|
||||||
if ~all(abs(Finf(:))<crit)
|
if ~all(abs(Finf(:))<crit)
|
||||||
return
|
return
|
||||||
else
|
else
|
||||||
iFstar = inv(Pstar(mf,mf)+H);
|
iFstar = inv(Pstar(mf,mf)+H);
|
||||||
dFstar = det(Pstar(mf,mf)+H);
|
dFstar = det(Pstar(mf,mf)+H);
|
||||||
Kstar = Pstar(:,mf)*iFstar;
|
Kstar = Pstar(:,mf)*iFstar;
|
||||||
lik(t) = log(dFstar) + transpose(v)*iFstar*v;
|
lik(t) = log(dFstar) + transpose(v)*iFstar*v;
|
||||||
Pinf = T*Pinf*transpose(T);
|
Pinf = T*Pinf*transpose(T);
|
||||||
Pstar = T*(Pstar-Pstar(:,mf)*transpose(Kstar))*transpose(T)+QQ;
|
Pstar = T*(Pstar-Pstar(:,mf)*transpose(Kstar))*transpose(T)+QQ;
|
||||||
a = T*(a+Kstar*v);
|
a = T*(a+Kstar*v);
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
lik(t) = log(det(Finf));
|
lik(t) = log(det(Finf));
|
||||||
iFinf = inv(Finf);
|
iFinf = inv(Finf);
|
||||||
Kinf = Pinf(:,mf)*iFinf; %% premultiplication by the transition matrix T is removed (stephane)
|
Kinf = Pinf(:,mf)*iFinf; %% premultiplication by the transition matrix T is removed (stephane)
|
||||||
Fstar = Pstar(mf,mf)+H;
|
Fstar = Pstar(mf,mf)+H;
|
||||||
Kstar = (Pstar(:,mf)-Kinf*Fstar)*iFinf; %% premultiplication by the transition matrix T is removed (stephane)
|
Kstar = (Pstar(:,mf)-Kinf*Fstar)*iFinf; %% premultiplication by the transition matrix T is removed (stephane)
|
||||||
Pstar = T*(Pstar-Pstar(:,mf)*transpose(Kinf)-Pinf(:,mf)*transpose(Kstar))*transpose(T)+QQ;
|
Pstar = T*(Pstar-Pstar(:,mf)*transpose(Kinf)-Pinf(:,mf)*transpose(Kstar))*transpose(T)+QQ;
|
||||||
Pinf = T*(Pinf-Pinf(:,mf)*transpose(Kinf))*transpose(T);
|
Pinf = T*(Pinf-Pinf(:,mf)*transpose(Kinf))*transpose(T);
|
||||||
a = T*(a+Kinf*v);
|
a = T*(a+Kinf*v);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if t == smpl
|
if t == smpl
|
||||||
error(['There isn''t enough information to estimate the initial' ...
|
error(['There isn''t enough information to estimate the initial' ...
|
||||||
' conditions of the nonstationary variables']);
|
' conditions of the nonstationary variables']);
|
||||||
end
|
end
|
||||||
F_singular = 1;
|
F_singular = 1;
|
||||||
while notsteady & t < smpl
|
while notsteady & t < smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
v = Y(:,t)-a(mf)-trend(:,t);
|
v = Y(:,t)-a(mf)-trend(:,t);
|
||||||
F = Pstar(mf,mf)+H;
|
F = Pstar(mf,mf)+H;
|
||||||
oldPstar = Pstar;
|
oldPstar = Pstar;
|
||||||
dF = det(F);
|
dF = det(F);
|
||||||
if rcond(F) < crit
|
if rcond(F) < crit
|
||||||
if ~all(abs(F(:))<crit)
|
if ~all(abs(F(:))<crit)
|
||||||
return
|
return
|
||||||
else
|
else
|
||||||
a = T*a;
|
a = T*a;
|
||||||
Pstar = T*Pstar*transpose(T)+QQ;
|
Pstar = T*Pstar*transpose(T)+QQ;
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
F_singular = 0;
|
F_singular = 0;
|
||||||
iF = inv(F);
|
iF = inv(F);
|
||||||
lik(t) = log(dF)+transpose(v)*iF*v;
|
lik(t) = log(dF)+transpose(v)*iF*v;
|
||||||
K = Pstar(:,mf)*iF; %% premultiplication by the transition matrix T is removed (stephane)
|
K = Pstar(:,mf)*iF; %% premultiplication by the transition matrix T is removed (stephane)
|
||||||
a = T*(a+K*v); %% --> factorization of the transition matrix...
|
a = T*(a+K*v); %% --> factorization of the transition matrix...
|
||||||
Pstar = T*(Pstar-K*Pstar(mf,:))*transpose(T)+QQ; %% ... idem (stephane)
|
Pstar = T*(Pstar-K*Pstar(mf,:))*transpose(T)+QQ; %% ... idem (stephane)
|
||||||
end
|
end
|
||||||
notsteady = ~(max(max(abs(Pstar-oldPstar)))<crit);
|
notsteady = ~(max(max(abs(Pstar-oldPstar)))<crit);
|
||||||
end
|
end
|
||||||
if F_singular == 1
|
if F_singular == 1
|
||||||
error(['The variance of the forecast error remains singular until the' ...
|
error(['The variance of the forecast error remains singular until the' ...
|
||||||
'end of the sample'])
|
'end of the sample'])
|
||||||
end
|
end
|
||||||
if t < smpl
|
if t < smpl
|
||||||
t0 = t+1;
|
t0 = t+1;
|
||||||
while t < smpl
|
while t < smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
v = Y(:,t)-a(mf)-trend(:,t);
|
v = Y(:,t)-a(mf)-trend(:,t);
|
||||||
a = T*(a+K*v);
|
a = T*(a+K*v);
|
||||||
lik(t) = transpose(v)*iF*v;
|
lik(t) = transpose(v)*iF*v;
|
||||||
end
|
end
|
||||||
lik(t0:smpl) = lik(t0:smpl) + log(dF);
|
lik(t0:smpl) = lik(t0:smpl) + log(dF);
|
||||||
end
|
end
|
||||||
% adding log-likelihhod constants
|
% adding log-likelihhod constants
|
||||||
lik = (lik + pp*log(2*pi))/2;
|
lik = (lik + pp*log(2*pi))/2;
|
||||||
|
|
||||||
LIK = sum(lik(start:end)); % Minus the log-likelihood.
|
LIK = sum(lik(start:end)); % Minus the log-likelihood.
|
||||||
|
|
|
||||||
|
|
@ -1,132 +1,132 @@
|
||||||
function [LIK, lik] = DiffuseLikelihoodH1_Z(T,Z,R,Q,H,Pinf,Pstar,Y,start)
|
function [LIK, lik] = DiffuseLikelihoodH1_Z(T,Z,R,Q,H,Pinf,Pstar,Y,start)
|
||||||
|
|
||||||
% function [LIK, lik] = DiffuseLikelihoodH1_Z(T,Z,R,Q,H,Pinf,Pstar,Y,start)
|
% function [LIK, lik] = DiffuseLikelihoodH1_Z(T,Z,R,Q,H,Pinf,Pstar,Y,start)
|
||||||
% Computes the diffuse likelihood (H=measurement error) in the case of a non-singular var-cov matrix
|
% Computes the diffuse likelihood (H=measurement error) in the case of a non-singular var-cov matrix
|
||||||
|
|
||||||
%
|
%
|
||||||
% INPUTS
|
% INPUTS
|
||||||
% T: mm*mm matrix
|
% T: mm*mm matrix
|
||||||
% Z: pp,mm matrix
|
% Z: pp,mm matrix
|
||||||
% R: mm*rr matrix
|
% R: mm*rr matrix
|
||||||
% Q: rr*rr matrix
|
% Q: rr*rr matrix
|
||||||
% H: pp*pp matrix
|
% H: pp*pp matrix
|
||||||
% Pinf: mm*mm diagonal matrix with with q ones and m-q zeros
|
% Pinf: mm*mm diagonal matrix with with q ones and m-q zeros
|
||||||
% Pstar: mm*mm variance-covariance matrix with stationary variables
|
% Pstar: mm*mm variance-covariance matrix with stationary variables
|
||||||
% Y: pp*1 vector
|
% Y: pp*1 vector
|
||||||
% start: likelihood evaluation at 'start'
|
% start: likelihood evaluation at 'start'
|
||||||
%
|
%
|
||||||
% OUTPUTS
|
% OUTPUTS
|
||||||
% LIK: likelihood
|
% LIK: likelihood
|
||||||
% lik: density vector in each period
|
% lik: density vector in each period
|
||||||
%
|
%
|
||||||
% SPECIAL REQUIREMENTS
|
% SPECIAL REQUIREMENTS
|
||||||
% See "Filtering and Smoothing of State Vector for Diffuse State Space
|
% See "Filtering and Smoothing of State Vector for Diffuse State Space
|
||||||
% Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
|
% Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
|
||||||
% Analysis, vol. 24(1), pp. 85-98).
|
% Analysis, vol. 24(1), pp. 85-98).
|
||||||
|
|
||||||
% Copyright (C) 2004-2008 Dynare Team
|
% Copyright (C) 2004-2008 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
% Dynare is free software: you can redistribute it and/or modify
|
||||||
% it under the terms of the GNU General Public License as published by
|
% it under the terms of the GNU General Public License as published by
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
% the Free Software Foundation, either version 3 of the License, or
|
||||||
% (at your option) any later version.
|
% (at your option) any later version.
|
||||||
%
|
%
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
% Dynare is distributed in the hope that it will be useful,
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
% M. Ratto added lik in output
|
% M. Ratto added lik in output
|
||||||
|
|
||||||
global bayestopt_ options_
|
global bayestopt_ options_
|
||||||
|
|
||||||
smpl = size(Y,2);
|
smpl = size(Y,2);
|
||||||
mm = size(T,2);
|
mm = size(T,2);
|
||||||
pp = size(Y,1);
|
pp = size(Y,1);
|
||||||
a = zeros(mm,1);
|
a = zeros(mm,1);
|
||||||
dF = 1;
|
dF = 1;
|
||||||
QQ = R*Q*transpose(R);
|
QQ = R*Q*transpose(R);
|
||||||
t = 0;
|
t = 0;
|
||||||
lik = zeros(smpl,1);
|
lik = zeros(smpl,1);
|
||||||
LIK = Inf;
|
LIK = Inf;
|
||||||
notsteady = 1;
|
notsteady = 1;
|
||||||
crit = options_.kalman_tol;
|
crit = options_.kalman_tol;
|
||||||
while rank(Pinf,crit) & t < smpl
|
while rank(Pinf,crit) & t < smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
v = Y(:,t)-Z*a;
|
v = Y(:,t)-Z*a;
|
||||||
Finf = Z*Pinf*Z';
|
Finf = Z*Pinf*Z';
|
||||||
if rcond(Finf) < crit
|
if rcond(Finf) < crit
|
||||||
if ~all(abs(Finf(:)) < crit)
|
if ~all(abs(Finf(:)) < crit)
|
||||||
return
|
return
|
||||||
else
|
else
|
||||||
Fstar = Z*Pstar*Z'+H;
|
Fstar = Z*Pstar*Z'+H;
|
||||||
iFstar = inv(Fstar);
|
iFstar = inv(Fstar);
|
||||||
dFstar = det(Fstar);
|
dFstar = det(Fstar);
|
||||||
Kstar = Pstar*Z'*iFstar;
|
Kstar = Pstar*Z'*iFstar;
|
||||||
lik(t) = log(dFstar) + v'*iFstar*v;
|
lik(t) = log(dFstar) + v'*iFstar*v;
|
||||||
Pinf = T*Pinf*transpose(T);
|
Pinf = T*Pinf*transpose(T);
|
||||||
Pstar = T*(Pstar-Pstar*Z'*Kstar')*T'+QQ;
|
Pstar = T*(Pstar-Pstar*Z'*Kstar')*T'+QQ;
|
||||||
a = T*(a+Kstar*v);
|
a = T*(a+Kstar*v);
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
lik(t) = log(det(Finf));
|
lik(t) = log(det(Finf));
|
||||||
iFinf = inv(Finf);
|
iFinf = inv(Finf);
|
||||||
Kinf = Pinf*Z'*iFinf;
|
Kinf = Pinf*Z'*iFinf;
|
||||||
Fstar = Z*Pstar*Z'+H;
|
Fstar = Z*Pstar*Z'+H;
|
||||||
Kstar = (Pstar*Z'-Kinf*Fstar)*iFinf;
|
Kstar = (Pstar*Z'-Kinf*Fstar)*iFinf;
|
||||||
Pstar = T*(Pstar-Pstar*Z'*Kinf'-Pinf*Z'*Kstar')*T'+QQ;
|
Pstar = T*(Pstar-Pstar*Z'*Kinf'-Pinf*Z'*Kstar')*T'+QQ;
|
||||||
Pinf = T*(Pinf-Pinf*Z'*Kinf')*T';
|
Pinf = T*(Pinf-Pinf*Z'*Kinf')*T';
|
||||||
a = T*(a+Kinf*v);
|
a = T*(a+Kinf*v);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if t == smpl
|
if t == smpl
|
||||||
error(['There isn''t enough information to estimate the initial' ...
|
error(['There isn''t enough information to estimate the initial' ...
|
||||||
' conditions of the nonstationary variables']);
|
' conditions of the nonstationary variables']);
|
||||||
end
|
end
|
||||||
F_singular = 1;
|
F_singular = 1;
|
||||||
while notsteady & t < smpl
|
while notsteady & t < smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
v = Y(:,t)-Z*a;
|
v = Y(:,t)-Z*a;
|
||||||
F = Z*Pstar*Z'+H;
|
F = Z*Pstar*Z'+H;
|
||||||
oldPstar = Pstar;
|
oldPstar = Pstar;
|
||||||
dF = det(F);
|
dF = det(F);
|
||||||
if rcond(F) < crit
|
if rcond(F) < crit
|
||||||
if ~all(abs(F(:))<crit)
|
if ~all(abs(F(:))<crit)
|
||||||
return
|
return
|
||||||
else
|
else
|
||||||
a = T*a;
|
a = T*a;
|
||||||
Pstar = T*Pstar*T'+QQ;
|
Pstar = T*Pstar*T'+QQ;
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
F_singular = 0;
|
F_singular = 0;
|
||||||
iF = inv(F);
|
iF = inv(F);
|
||||||
lik(t) = log(dF)+v'*iF*v;
|
lik(t) = log(dF)+v'*iF*v;
|
||||||
K = Pstar*Z'*iF;
|
K = Pstar*Z'*iF;
|
||||||
a = T*(a+K*v);
|
a = T*(a+K*v);
|
||||||
Pstar = T*(Pstar-K*Z*Pstar)*T'+QQ;
|
Pstar = T*(Pstar-K*Z*Pstar)*T'+QQ;
|
||||||
end
|
end
|
||||||
notsteady = ~(max(max(abs(Pstar-oldPstar)))<crit);
|
notsteady = ~(max(max(abs(Pstar-oldPstar)))<crit);
|
||||||
end
|
end
|
||||||
if F_singular == 1
|
if F_singular == 1
|
||||||
error(['The variance of the forecast error remains singular until the' ...
|
error(['The variance of the forecast error remains singular until the' ...
|
||||||
'end of the sample'])
|
'end of the sample'])
|
||||||
end
|
end
|
||||||
if t < smpl
|
if t < smpl
|
||||||
t0 = t+1;
|
t0 = t+1;
|
||||||
while t < smpl
|
while t < smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
v = Y(:,t)-Z*a;
|
v = Y(:,t)-Z*a;
|
||||||
a = T*(a+K*v);
|
a = T*(a+K*v);
|
||||||
lik(t) = v'*iF*v;
|
lik(t) = v'*iF*v;
|
||||||
end
|
end
|
||||||
lik(t0:smpl) = lik(t0:smpl) + log(dF);
|
lik(t0:smpl) = lik(t0:smpl) + log(dF);
|
||||||
end
|
end
|
||||||
% adding log-likelihhod constants
|
% adding log-likelihhod constants
|
||||||
lik = (lik + pp*log(2*pi))/2;
|
lik = (lik + pp*log(2*pi))/2;
|
||||||
|
|
||||||
LIK = sum(lik(start:end)); % Minus the log-likelihood.
|
LIK = sum(lik(start:end)); % Minus the log-likelihood.
|
||||||
|
|
@ -1,178 +1,178 @@
|
||||||
function [LIK, lik] = DiffuseLikelihoodH3(T,R,Q,H,Pinf,Pstar,Y,trend,start)
|
function [LIK, lik] = DiffuseLikelihoodH3(T,R,Q,H,Pinf,Pstar,Y,trend,start)
|
||||||
|
|
||||||
% function [LIK, lik] = DiffuseLikelihoodH3(T,R,Q,H,Pinf,Pstar,Y,trend,start)
|
% function [LIK, lik] = DiffuseLikelihoodH3(T,R,Q,H,Pinf,Pstar,Y,trend,start)
|
||||||
% Computes the diffuse likelihood with measurement error, in the case of
|
% Computes the diffuse likelihood with measurement error, in the case of
|
||||||
% a singular var-cov matrix.
|
% a singular var-cov matrix.
|
||||||
% Univariate treatment of multivariate time series.
|
% Univariate treatment of multivariate time series.
|
||||||
%
|
%
|
||||||
% INPUTS
|
% INPUTS
|
||||||
% T: mm*mm matrix
|
% T: mm*mm matrix
|
||||||
% R: mm*rr matrix
|
% R: mm*rr matrix
|
||||||
% Q: rr*rr matrix
|
% Q: rr*rr matrix
|
||||||
% H: pp*pp matrix
|
% H: pp*pp matrix
|
||||||
% Pinf: mm*mm diagonal matrix with with q ones and m-q zeros
|
% Pinf: mm*mm diagonal matrix with with q ones and m-q zeros
|
||||||
% Pstar: mm*mm variance-covariance matrix with stationary variables
|
% Pstar: mm*mm variance-covariance matrix with stationary variables
|
||||||
% Y: pp*1 vector
|
% Y: pp*1 vector
|
||||||
% trend
|
% trend
|
||||||
% start: likelihood evaluation at 'start'
|
% start: likelihood evaluation at 'start'
|
||||||
%
|
%
|
||||||
% OUTPUTS
|
% OUTPUTS
|
||||||
% LIK: likelihood
|
% LIK: likelihood
|
||||||
% lik: density vector in each period
|
% lik: density vector in each period
|
||||||
%
|
%
|
||||||
% SPECIAL REQUIREMENTS
|
% SPECIAL REQUIREMENTS
|
||||||
% See "Filtering and Smoothing of State Vector for Diffuse State Space
|
% See "Filtering and Smoothing of State Vector for Diffuse State Space
|
||||||
% Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
|
% Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
|
||||||
% Analysis, vol. 24(1), pp. 85-98).
|
% Analysis, vol. 24(1), pp. 85-98).
|
||||||
|
|
||||||
% Copyright (C) 2005-2008 Dynare Team
|
% Copyright (C) 2005-2008 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
% Dynare is free software: you can redistribute it and/or modify
|
||||||
% it under the terms of the GNU General Public License as published by
|
% it under the terms of the GNU General Public License as published by
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
% the Free Software Foundation, either version 3 of the License, or
|
||||||
% (at your option) any later version.
|
% (at your option) any later version.
|
||||||
%
|
%
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
% Dynare is distributed in the hope that it will be useful,
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
% M. Ratto added lik in output [October 2005]
|
% M. Ratto added lik in output [October 2005]
|
||||||
% changes by M. Ratto
|
% changes by M. Ratto
|
||||||
% introduced new global variable id_ for termination of DKF
|
% introduced new global variable id_ for termination of DKF
|
||||||
% introduced a persistent fmax, in order to keep track the max order of
|
% introduced a persistent fmax, in order to keep track the max order of
|
||||||
% magnitude of the 'zero' values in Pinf at DKF termination
|
% magnitude of the 'zero' values in Pinf at DKF termination
|
||||||
% new icc counter for Finf steps in DKF
|
% new icc counter for Finf steps in DKF
|
||||||
% new termination for DKF
|
% new termination for DKF
|
||||||
% likelihood terms for Fstar must be cumulated in DKF also when Pinf is non
|
% likelihood terms for Fstar must be cumulated in DKF also when Pinf is non
|
||||||
% zero. this bug is fixed.
|
% zero. this bug is fixed.
|
||||||
|
|
||||||
global bayestopt_ options_
|
global bayestopt_ options_
|
||||||
|
|
||||||
mf = bayestopt_.mf;
|
mf = bayestopt_.mf;
|
||||||
pp = size(Y,1);
|
pp = size(Y,1);
|
||||||
mm = size(T,1);
|
mm = size(T,1);
|
||||||
smpl = size(Y,2);
|
smpl = size(Y,2);
|
||||||
a = zeros(mm,1);
|
a = zeros(mm,1);
|
||||||
QQ = R*Q*transpose(R);
|
QQ = R*Q*transpose(R);
|
||||||
t = 0;
|
t = 0;
|
||||||
lik = zeros(smpl,1);
|
lik = zeros(smpl,1);
|
||||||
notsteady = 1;
|
notsteady = 1;
|
||||||
crit = options_.kalman_tol;
|
crit = options_.kalman_tol;
|
||||||
crit1 = 1.e-6;
|
crit1 = 1.e-6;
|
||||||
newRank = rank(Pinf,crit1);
|
newRank = rank(Pinf,crit1);
|
||||||
icc = 0;
|
icc = 0;
|
||||||
while newRank & t < smpl %% Matrix Finf is assumed to be zero
|
while newRank & t < smpl %% Matrix Finf is assumed to be zero
|
||||||
t = t+1;
|
t = t+1;
|
||||||
for i=1:pp
|
for i=1:pp
|
||||||
v(i) = Y(i,t)-a(mf(i))-trend(i,t);
|
v(i) = Y(i,t)-a(mf(i))-trend(i,t);
|
||||||
Fstar = Pstar(mf(i),mf(i))+H(i,i);
|
Fstar = Pstar(mf(i),mf(i))+H(i,i);
|
||||||
Finf = Pinf(mf(i),mf(i));
|
Finf = Pinf(mf(i),mf(i));
|
||||||
Kstar = Pstar(:,mf(i));
|
Kstar = Pstar(:,mf(i));
|
||||||
if Finf > crit & newRank
|
if Finf > crit & newRank
|
||||||
icc = icc + 1;
|
icc = icc + 1;
|
||||||
Kinf = Pinf(:,mf(i));
|
Kinf = Pinf(:,mf(i));
|
||||||
a = a + Kinf*v(i)/Finf;
|
a = a + Kinf*v(i)/Finf;
|
||||||
Pstar = Pstar + Kinf*transpose(Kinf)*Fstar/(Finf*Finf) - ...
|
Pstar = Pstar + Kinf*transpose(Kinf)*Fstar/(Finf*Finf) - ...
|
||||||
(Kstar*transpose(Kinf)+Kinf*transpose(Kstar))/Finf;
|
(Kstar*transpose(Kinf)+Kinf*transpose(Kstar))/Finf;
|
||||||
Pinf = Pinf - Kinf*transpose(Kinf)/Finf;
|
Pinf = Pinf - Kinf*transpose(Kinf)/Finf;
|
||||||
lik(t) = lik(t) + log(Finf);
|
lik(t) = lik(t) + log(Finf);
|
||||||
% start new termination criterion for DKF
|
% start new termination criterion for DKF
|
||||||
if ~isempty(options_.diffuse_d),
|
if ~isempty(options_.diffuse_d),
|
||||||
newRank = (icc<options_.diffuse_d);
|
newRank = (icc<options_.diffuse_d);
|
||||||
%if newRank & any(diag(Pinf(mf,mf))>crit)==0; % M. Ratto this line is BUGGY
|
%if newRank & any(diag(Pinf(mf,mf))>crit)==0; % M. Ratto this line is BUGGY
|
||||||
if newRank & (any(diag(Pinf(mf,mf))>crit)==0 & rank(Pinf,crit1)==0);
|
if newRank & (any(diag(Pinf(mf,mf))>crit)==0 & rank(Pinf,crit1)==0);
|
||||||
options_.diffuse_d = icc;
|
options_.diffuse_d = icc;
|
||||||
newRank=0;
|
newRank=0;
|
||||||
disp('WARNING: Change in OPTIONS_.DIFFUSE_D in univariate DKF')
|
disp('WARNING: Change in OPTIONS_.DIFFUSE_D in univariate DKF')
|
||||||
disp(['new OPTIONS_.DIFFUSE_D = ',int2str(icc)])
|
disp(['new OPTIONS_.DIFFUSE_D = ',int2str(icc)])
|
||||||
disp('You may have to reset the optimisation')
|
disp('You may have to reset the optimisation')
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
%newRank = any(diag(Pinf(mf,mf))>crit); % M. Ratto this line is BUGGY
|
%newRank = any(diag(Pinf(mf,mf))>crit); % M. Ratto this line is BUGGY
|
||||||
newRank = (any(diag(Pinf(mf,mf))>crit) | rank(Pinf,crit1));
|
newRank = (any(diag(Pinf(mf,mf))>crit) | rank(Pinf,crit1));
|
||||||
if newRank==0,
|
if newRank==0,
|
||||||
P0= T*Pinf*transpose(T);
|
P0= T*Pinf*transpose(T);
|
||||||
%newRank = any(diag(P0(mf,mf))>crit); % M. Ratto this line is BUGGY
|
%newRank = any(diag(P0(mf,mf))>crit); % M. Ratto this line is BUGGY
|
||||||
newRank = (any(diag(P0(mf,mf))>crit) | rank(P0,crit1)); % M. Ratto 10 Oct 2005
|
newRank = (any(diag(P0(mf,mf))>crit) | rank(P0,crit1)); % M. Ratto 10 Oct 2005
|
||||||
if newRank==0,
|
if newRank==0,
|
||||||
options_.diffuse_d = icc;
|
options_.diffuse_d = icc;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end,
|
end,
|
||||||
% end new termination and checks for DKF and fmax
|
% end new termination and checks for DKF and fmax
|
||||||
elseif Finf > crit
|
elseif Finf > crit
|
||||||
%% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition
|
%% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition
|
||||||
%% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that
|
%% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that
|
||||||
%% rank(Pinf)=0. [stéphane,11-03-2004].
|
%% rank(Pinf)=0. [stéphane,11-03-2004].
|
||||||
%if rank(Pinf) == 0
|
%if rank(Pinf) == 0
|
||||||
% the likelihood terms should alwasy be cumulated, not only
|
% the likelihood terms should alwasy be cumulated, not only
|
||||||
% when Pinf=0, otherwise the lik would depend on the ordering
|
% when Pinf=0, otherwise the lik would depend on the ordering
|
||||||
% of observed variables
|
% of observed variables
|
||||||
lik(t) = lik(t) + log(Fstar) + v(i)*v(i)/Fstar;
|
lik(t) = lik(t) + log(Fstar) + v(i)*v(i)/Fstar;
|
||||||
%end
|
%end
|
||||||
a = a + Kstar*v(i)/Fstar;
|
a = a + Kstar*v(i)/Fstar;
|
||||||
Pstar = Pstar - Kstar*transpose(Kstar)/Fstar;
|
Pstar = Pstar - Kstar*transpose(Kstar)/Fstar;
|
||||||
else
|
else
|
||||||
% disp(['zero F term in DKF for observed ',int2str(i),' ',num2str(Fi)])
|
% disp(['zero F term in DKF for observed ',int2str(i),' ',num2str(Fi)])
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if newRank
|
if newRank
|
||||||
oldRank = rank(Pinf,crit1);
|
oldRank = rank(Pinf,crit1);
|
||||||
else
|
else
|
||||||
oldRank = 0;
|
oldRank = 0;
|
||||||
end
|
end
|
||||||
a = T*a;
|
a = T*a;
|
||||||
Pstar = T*Pstar*transpose(T)+QQ;
|
Pstar = T*Pstar*transpose(T)+QQ;
|
||||||
Pinf = T*Pinf*transpose(T);
|
Pinf = T*Pinf*transpose(T);
|
||||||
if newRank
|
if newRank
|
||||||
newRank = rank(Pinf,crit1);
|
newRank = rank(Pinf,crit1);
|
||||||
end
|
end
|
||||||
if oldRank ~= newRank
|
if oldRank ~= newRank
|
||||||
disp('DiffuseLiklihoodH3 :: T does influence the rank of Pinf!')
|
disp('DiffuseLiklihoodH3 :: T does influence the rank of Pinf!')
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if t == smpl
|
if t == smpl
|
||||||
error(['There isn''t enough information to estimate the initial' ...
|
error(['There isn''t enough information to estimate the initial' ...
|
||||||
' conditions of the nonstationary variables']);
|
' conditions of the nonstationary variables']);
|
||||||
end
|
end
|
||||||
while notsteady & t < smpl
|
while notsteady & t < smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
for i=1:pp
|
for i=1:pp
|
||||||
v(i) = Y(i,t) - a(mf(i)) - trend(i,t);
|
v(i) = Y(i,t) - a(mf(i)) - trend(i,t);
|
||||||
Fi = Pstar(mf(i),mf(i))+H(i,i);
|
Fi = Pstar(mf(i),mf(i))+H(i,i);
|
||||||
if Fi > crit
|
if Fi > crit
|
||||||
Ki = Pstar(:,mf(i));
|
Ki = Pstar(:,mf(i));
|
||||||
a = a + Ki*v(i)/Fi;
|
a = a + Ki*v(i)/Fi;
|
||||||
Pstar = Pstar - Ki*transpose(Ki)/Fi;
|
Pstar = Pstar - Ki*transpose(Ki)/Fi;
|
||||||
lik(t) = lik(t) + log(Fi) + v(i)*v(i)/Fi;
|
lik(t) = lik(t) + log(Fi) + v(i)*v(i)/Fi;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
oldP = Pstar;
|
oldP = Pstar;
|
||||||
a = T*a;
|
a = T*a;
|
||||||
Pstar = T*Pstar*transpose(T) + QQ;
|
Pstar = T*Pstar*transpose(T) + QQ;
|
||||||
notsteady = ~(max(max(abs(Pstar-oldP)))<crit);
|
notsteady = ~(max(max(abs(Pstar-oldP)))<crit);
|
||||||
end
|
end
|
||||||
while t < smpl
|
while t < smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
for i=1:pp
|
for i=1:pp
|
||||||
v(i) = Y(i,t) - a(mf(i)) - trend(i,t);
|
v(i) = Y(i,t) - a(mf(i)) - trend(i,t);
|
||||||
Fi = Pstar(mf(i),mf(i))+H(i,i);
|
Fi = Pstar(mf(i),mf(i))+H(i,i);
|
||||||
if Fi > crit
|
if Fi > crit
|
||||||
Ki = Pstar(:,mf(i));
|
Ki = Pstar(:,mf(i));
|
||||||
a = a + Ki*v(i)/Fi;
|
a = a + Ki*v(i)/Fi;
|
||||||
Pstar = Pstar - Ki*transpose(Ki)/Fi;
|
Pstar = Pstar - Ki*transpose(Ki)/Fi;
|
||||||
lik(t) = lik(t) + log(Fi) + v(i)*v(i)/Fi;
|
lik(t) = lik(t) + log(Fi) + v(i)*v(i)/Fi;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
a = T*a;
|
a = T*a;
|
||||||
end
|
end
|
||||||
% adding log-likelihhod constants
|
% adding log-likelihhod constants
|
||||||
lik = (lik + pp*log(2*pi))/2;
|
lik = (lik + pp*log(2*pi))/2;
|
||||||
|
|
||||||
LIK = sum(lik(start:end)); % Minus the log-likelihood.
|
LIK = sum(lik(start:end)); % Minus the log-likelihood.
|
||||||
|
|
|
||||||
|
|
@ -1,182 +1,182 @@
|
||||||
function [LIK, lik] = DiffuseLikelihoodH3_Z(T,Z,R,Q,H,Pinf,Pstar,Y,start)
|
function [LIK, lik] = DiffuseLikelihoodH3_Z(T,Z,R,Q,H,Pinf,Pstar,Y,start)
|
||||||
|
|
||||||
% function [LIK, lik] = DiffuseLikelihoodH3_A(T,R,Q,H,Pinf,Pstar,Y,start)
|
% function [LIK, lik] = DiffuseLikelihoodH3_A(T,R,Q,H,Pinf,Pstar,Y,start)
|
||||||
% Computes the diffuse likelihood without measurement error, in the case of
|
% Computes the diffuse likelihood without measurement error, in the case of
|
||||||
% a singular var-cov matrix.
|
% a singular var-cov matrix.
|
||||||
% Univariate treatment of multivariate time series.
|
% Univariate treatment of multivariate time series.
|
||||||
%
|
%
|
||||||
% INPUTS
|
% INPUTS
|
||||||
% T: mm*mm matrix
|
% T: mm*mm matrix
|
||||||
% Z: pp*mm matrix
|
% Z: pp*mm matrix
|
||||||
% R: mm*rr matrix
|
% R: mm*rr matrix
|
||||||
% Q: rr*rr matrix
|
% Q: rr*rr matrix
|
||||||
% H: pp*pp matrix
|
% H: pp*pp matrix
|
||||||
% Pinf: mm*mm diagonal matrix with with q ones and m-q zeros
|
% Pinf: mm*mm diagonal matrix with with q ones and m-q zeros
|
||||||
% Pstar: mm*mm variance-covariance matrix with stationary variables
|
% Pstar: mm*mm variance-covariance matrix with stationary variables
|
||||||
% Y: pp*1 vector
|
% Y: pp*1 vector
|
||||||
% start: likelihood evaluation at 'start'
|
% start: likelihood evaluation at 'start'
|
||||||
%
|
%
|
||||||
% OUTPUTS
|
% OUTPUTS
|
||||||
% LIK: likelihood
|
% LIK: likelihood
|
||||||
% lik: density vector in each period
|
% lik: density vector in each period
|
||||||
%
|
%
|
||||||
% SPECIAL REQUIREMENTS
|
% SPECIAL REQUIREMENTS
|
||||||
% See "Filtering and Smoothing of State Vector for Diffuse State Space
|
% See "Filtering and Smoothing of State Vector for Diffuse State Space
|
||||||
% Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
|
% Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
|
||||||
% Analysis, vol. 24(1), pp. 85-98).
|
% Analysis, vol. 24(1), pp. 85-98).
|
||||||
|
|
||||||
% Copyright (C) 2004-2008 Dynare Team
|
% Copyright (C) 2004-2008 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
% Dynare is free software: you can redistribute it and/or modify
|
||||||
% it under the terms of the GNU General Public License as published by
|
% it under the terms of the GNU General Public License as published by
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
% the Free Software Foundation, either version 3 of the License, or
|
||||||
% (at your option) any later version.
|
% (at your option) any later version.
|
||||||
%
|
%
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
% Dynare is distributed in the hope that it will be useful,
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
% M. Ratto added lik in output [October 2005]
|
% M. Ratto added lik in output [October 2005]
|
||||||
% changes by M. Ratto [April 2005]
|
% changes by M. Ratto [April 2005]
|
||||||
% introduced new options options_.diffuse_d for termination of DKF
|
% introduced new options options_.diffuse_d for termination of DKF
|
||||||
% new icc counter for Finf steps in DKF
|
% new icc counter for Finf steps in DKF
|
||||||
% new termination for DKF
|
% new termination for DKF
|
||||||
% likelihood terms for Fstar must be cumulated in DKF also when Pinf is non
|
% likelihood terms for Fstar must be cumulated in DKF also when Pinf is non
|
||||||
% zero.
|
% zero.
|
||||||
% [4/5/2005] correctyed bug in the modified verson of Ratto for rank of Pinf
|
% [4/5/2005] correctyed bug in the modified verson of Ratto for rank of Pinf
|
||||||
% introduced a specific crit1 for the DKF termination
|
% introduced a specific crit1 for the DKF termination
|
||||||
|
|
||||||
|
|
||||||
global bayestopt_ options_
|
global bayestopt_ options_
|
||||||
|
|
||||||
pp = size(Y,1);
|
pp = size(Y,1);
|
||||||
mm = size(T,1);
|
mm = size(T,1);
|
||||||
smpl = size(Y,2);
|
smpl = size(Y,2);
|
||||||
a = zeros(mm,1);
|
a = zeros(mm,1);
|
||||||
QQ = R*Q*R';
|
QQ = R*Q*R';
|
||||||
t = 0;
|
t = 0;
|
||||||
lik = zeros(smpl,1);
|
lik = zeros(smpl,1);
|
||||||
notsteady = 1;
|
notsteady = 1;
|
||||||
crit = options_.kalman_tol;
|
crit = options_.kalman_tol;
|
||||||
crit1 = 1.e-6;
|
crit1 = 1.e-6;
|
||||||
newRank = rank(Pinf,crit1);
|
newRank = rank(Pinf,crit1);
|
||||||
icc=0;
|
icc=0;
|
||||||
while newRank & t < smpl
|
while newRank & t < smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
for i=1:pp
|
for i=1:pp
|
||||||
Zi = Z(i,:);
|
Zi = Z(i,:);
|
||||||
v(i) = Y(i,t)-Zi*a;
|
v(i) = Y(i,t)-Zi*a;
|
||||||
Fstar = Zi*Pstar*Zi'+H(i,i);
|
Fstar = Zi*Pstar*Zi'+H(i,i);
|
||||||
Finf = Zi*Pinf*Zi';
|
Finf = Zi*Pinf*Zi';
|
||||||
Kstar = Pstar*Zi';
|
Kstar = Pstar*Zi';
|
||||||
if Finf > crit & newRank
|
if Finf > crit & newRank
|
||||||
icc=icc+1;
|
icc=icc+1;
|
||||||
Kinf = Pinf*Zi';
|
Kinf = Pinf*Zi';
|
||||||
a = a + Kinf*v(i)/Finf;
|
a = a + Kinf*v(i)/Finf;
|
||||||
Pstar = Pstar + Kinf*Kinf'*Fstar/(Finf*Finf) - ...
|
Pstar = Pstar + Kinf*Kinf'*Fstar/(Finf*Finf) - ...
|
||||||
(Kstar*Kinf'+Kinf*Kstar')/Finf;
|
(Kstar*Kinf'+Kinf*Kstar')/Finf;
|
||||||
Pinf = Pinf - Kinf*Kinf'/Finf;
|
Pinf = Pinf - Kinf*Kinf'/Finf;
|
||||||
lik(t) = lik(t) + log(Finf);
|
lik(t) = lik(t) + log(Finf);
|
||||||
if ~isempty(options_.diffuse_d),
|
if ~isempty(options_.diffuse_d),
|
||||||
newRank = (icc<options_.diffuse_d);
|
newRank = (icc<options_.diffuse_d);
|
||||||
if newRank & (any(diag(Z*Pinf*Z')>crit)==0 & rank(Pinf,crit1)==0);
|
if newRank & (any(diag(Z*Pinf*Z')>crit)==0 & rank(Pinf,crit1)==0);
|
||||||
options_.diffuse_d = icc;
|
options_.diffuse_d = icc;
|
||||||
newRank=0;
|
newRank=0;
|
||||||
disp('WARNING: Change in OPTIONS_.DIFFUSE_D in univariate DKF')
|
disp('WARNING: Change in OPTIONS_.DIFFUSE_D in univariate DKF')
|
||||||
disp(['new OPTIONS_.DIFFUSE_D = ',int2str(icc)])
|
disp(['new OPTIONS_.DIFFUSE_D = ',int2str(icc)])
|
||||||
disp('You may have to reset the optimisation')
|
disp('You may have to reset the optimisation')
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
newRank = (any(diag(Z*Pinf*Z')>crit) | rank(Pinf,crit1));
|
newRank = (any(diag(Z*Pinf*Z')>crit) | rank(Pinf,crit1));
|
||||||
if newRank==0,
|
if newRank==0,
|
||||||
P0= T*Pinf*T';
|
P0= T*Pinf*T';
|
||||||
newRank = (any(diag(Z*P0*Z')>crit) | rank(P0,crit1)); % M. Ratto 11/10/2005
|
newRank = (any(diag(Z*P0*Z')>crit) | rank(P0,crit1)); % M. Ratto 11/10/2005
|
||||||
if newRank==0,
|
if newRank==0,
|
||||||
options_.diffuse_d = icc;
|
options_.diffuse_d = icc;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end,
|
end,
|
||||||
elseif Fstar > crit
|
elseif Fstar > crit
|
||||||
%% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition
|
%% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition
|
||||||
%% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that
|
%% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that
|
||||||
%% rank(Pinf)=0. [stéphane,11-03-2004].
|
%% rank(Pinf)=0. [stéphane,11-03-2004].
|
||||||
%if rank(Pinf,crit) == 0
|
%if rank(Pinf,crit) == 0
|
||||||
% the likelihood terms should alwasy be cumulated, not only
|
% the likelihood terms should alwasy be cumulated, not only
|
||||||
% when Pinf=0, otherwise the lik would depend on the ordering
|
% when Pinf=0, otherwise the lik would depend on the ordering
|
||||||
% of observed variables
|
% of observed variables
|
||||||
% presample options can be used to ignore initial time points
|
% presample options can be used to ignore initial time points
|
||||||
lik(t) = lik(t) + log(Fstar) + v(i)*v(i)/Fstar;
|
lik(t) = lik(t) + log(Fstar) + v(i)*v(i)/Fstar;
|
||||||
a = a + Kstar*v(i)/Fstar;
|
a = a + Kstar*v(i)/Fstar;
|
||||||
Pstar = Pstar - Kstar*Kstar'/Fstar;
|
Pstar = Pstar - Kstar*Kstar'/Fstar;
|
||||||
else
|
else
|
||||||
%disp(['zero F term in DKF for observed ',int2str(i),' ',num2str(Fstar)])
|
%disp(['zero F term in DKF for observed ',int2str(i),' ',num2str(Fstar)])
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if newRank,
|
if newRank,
|
||||||
oldRank = rank(Pinf,crit1);
|
oldRank = rank(Pinf,crit1);
|
||||||
else
|
else
|
||||||
oldRank = 0;
|
oldRank = 0;
|
||||||
end
|
end
|
||||||
a = T*a;
|
a = T*a;
|
||||||
Pstar = T*Pstar*T'+QQ;
|
Pstar = T*Pstar*T'+QQ;
|
||||||
Pinf = T*Pinf*T';
|
Pinf = T*Pinf*T';
|
||||||
if newRank,
|
if newRank,
|
||||||
newRank = rank(Pinf,crit1);
|
newRank = rank(Pinf,crit1);
|
||||||
end
|
end
|
||||||
if oldRank ~= newRank
|
if oldRank ~= newRank
|
||||||
disp('DiffuseLiklihoodH3 :: T does influence the rank of Pinf!')
|
disp('DiffuseLiklihoodH3 :: T does influence the rank of Pinf!')
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if t == smpl
|
if t == smpl
|
||||||
error(['There isn''t enough information to estimate the initial' ...
|
error(['There isn''t enough information to estimate the initial' ...
|
||||||
' conditions of the nonstationary variables']);
|
' conditions of the nonstationary variables']);
|
||||||
end
|
end
|
||||||
while notsteady & t < smpl
|
while notsteady & t < smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
oldP = Pstar;
|
oldP = Pstar;
|
||||||
for i=1:pp
|
for i=1:pp
|
||||||
Zi = Z(i,:);
|
Zi = Z(i,:);
|
||||||
v(i) = Y(i,t) - Zi*a;
|
v(i) = Y(i,t) - Zi*a;
|
||||||
Fi = Zi*Pstar*Zi'+H(i,i);
|
Fi = Zi*Pstar*Zi'+H(i,i);
|
||||||
if Fi > crit
|
if Fi > crit
|
||||||
Ki = Pstar*Zi';
|
Ki = Pstar*Zi';
|
||||||
a = a + Ki*v(i)/Fi;
|
a = a + Ki*v(i)/Fi;
|
||||||
Pstar = Pstar - Ki*Ki'/Fi;
|
Pstar = Pstar - Ki*Ki'/Fi;
|
||||||
lik(t) = lik(t) + log(Fi) + v(i)*v(i)/Fi;
|
lik(t) = lik(t) + log(Fi) + v(i)*v(i)/Fi;
|
||||||
else
|
else
|
||||||
%disp(['zero F term for observed ',int2str(i),' ',num2str(Fi)])
|
%disp(['zero F term for observed ',int2str(i),' ',num2str(Fi)])
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
a = T*a;
|
a = T*a;
|
||||||
Pstar = T*Pstar*T' + QQ;
|
Pstar = T*Pstar*T' + QQ;
|
||||||
notsteady = ~(max(max(abs(Pstar-oldP)))<crit);
|
notsteady = ~(max(max(abs(Pstar-oldP)))<crit);
|
||||||
end
|
end
|
||||||
while t < smpl
|
while t < smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
Pstar = oldP;
|
Pstar = oldP;
|
||||||
for i=1:pp
|
for i=1:pp
|
||||||
Zi = Z(i,:);
|
Zi = Z(i,:);
|
||||||
v(i) = Y(i,t) - Zi*a;
|
v(i) = Y(i,t) - Zi*a;
|
||||||
Fi = Zi*Pstar*Zi'+H(i,i);
|
Fi = Zi*Pstar*Zi'+H(i,i);
|
||||||
if Fi > crit
|
if Fi > crit
|
||||||
Ki = Pstar*Zi';
|
Ki = Pstar*Zi';
|
||||||
a = a + Ki*v(i)/Fi;
|
a = a + Ki*v(i)/Fi;
|
||||||
Pstar = Pstar - Ki*Ki'/Fi;
|
Pstar = Pstar - Ki*Ki'/Fi;
|
||||||
lik(t) = lik(t) + log(Fi) + v(i)*v(i)/Fi;
|
lik(t) = lik(t) + log(Fi) + v(i)*v(i)/Fi;
|
||||||
else
|
else
|
||||||
%disp(['zero F term for observed ',int2str(i),' ',num2str(Fi)])
|
%disp(['zero F term for observed ',int2str(i),' ',num2str(Fi)])
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
a = T*a;
|
a = T*a;
|
||||||
end
|
end
|
||||||
% adding log-likelihhod constants
|
% adding log-likelihhod constants
|
||||||
lik = (lik + pp*log(2*pi))/2;
|
lik = (lik + pp*log(2*pi))/2;
|
||||||
|
|
||||||
LIK = sum(lik(start:end)); % Minus the log-likelihood.
|
LIK = sum(lik(start:end)); % Minus the log-likelihood.
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -20,7 +20,7 @@ function [LIK lik] = DiffuseLikelihoodH3corr(T,R,Q,H,Pinf,Pstar,Y,trend,start)
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
global bayestopt_ options_
|
global bayestopt_ options_
|
||||||
|
|
||||||
mf = bayestopt_.mf;
|
mf = bayestopt_.mf;
|
||||||
pp = size(Y,1);
|
pp = size(Y,1);
|
||||||
mm = size(T,1);
|
mm = size(T,1);
|
||||||
|
|
@ -30,7 +30,7 @@ T = cat(1,cat(2,T,zeros(mm,pp)),zeros(pp,mm+pp));
|
||||||
R = cat(1,cat(2,R,zeros(mm,pp)),cat(2,zeros(pp,rr),eye(pp)));
|
R = cat(1,cat(2,R,zeros(mm,pp)),cat(2,zeros(pp,rr),eye(pp)));
|
||||||
Q = cat(1,cat(2,Q,zeros(rr,pp)),cat(2,zeros(pp,rr),H));
|
Q = cat(1,cat(2,Q,zeros(rr,pp)),cat(2,zeros(pp,rr),H));
|
||||||
if size(Pinf,1) % Otherwise Pinf = 0 (no unit root)
|
if size(Pinf,1) % Otherwise Pinf = 0 (no unit root)
|
||||||
Pinf = cat(1,cat(2,Pinf,zeros(mm,pp)),zeros(pp,mm+pp));
|
Pinf = cat(1,cat(2,Pinf,zeros(mm,pp)),zeros(pp,mm+pp));
|
||||||
end
|
end
|
||||||
Pstar = cat(1,cat(2,Pstar,zeros(mm,pp)),cat(2,zeros(pp,mm),H));
|
Pstar = cat(1,cat(2,Pstar,zeros(mm,pp)),cat(2,zeros(pp,mm),H));
|
||||||
a = zeros(mm+pp,1);
|
a = zeros(mm+pp,1);
|
||||||
|
|
@ -42,71 +42,71 @@ crit = options_.kalman_tol;
|
||||||
newRank = rank(Pinf,crit);
|
newRank = rank(Pinf,crit);
|
||||||
|
|
||||||
while rank(Pinf,crit) & t < smpl %% Matrix Finf is assumed to be zero
|
while rank(Pinf,crit) & t < smpl %% Matrix Finf is assumed to be zero
|
||||||
t = t+1;
|
t = t+1;
|
||||||
for i=1:pp
|
for i=1:pp
|
||||||
v(i) = Y(i,t)-a(mf(i))-a(mm+i)-trend(i,t);
|
v(i) = Y(i,t)-a(mf(i))-a(mm+i)-trend(i,t);
|
||||||
Fstar = Pstar(mf(i),mf(i))+Pstar(mm+i,mm+i);
|
Fstar = Pstar(mf(i),mf(i))+Pstar(mm+i,mm+i);
|
||||||
Finf = Pinf(mf(i),mf(i));
|
Finf = Pinf(mf(i),mf(i));
|
||||||
Kstar = Pstar(:,mf(i))+Pstar(:,mm+i);
|
Kstar = Pstar(:,mf(i))+Pstar(:,mm+i);
|
||||||
if Finf > crit
|
if Finf > crit
|
||||||
Kinf = Pinf(:,mf(i));
|
Kinf = Pinf(:,mf(i));
|
||||||
a = a + Kinf*v(i)/Finf;
|
a = a + Kinf*v(i)/Finf;
|
||||||
Pstar = Pstar + Kinf*transpose(Kinf)*Fstar/(Finf*Finf) - ...
|
Pstar = Pstar + Kinf*transpose(Kinf)*Fstar/(Finf*Finf) - ...
|
||||||
(Kstar*transpose(Kinf)+Kinf*transpose(Kstar))/Finf;
|
(Kstar*transpose(Kinf)+Kinf*transpose(Kstar))/Finf;
|
||||||
Pinf = Pinf - Kinf*transpose(Kinf)/Finf;
|
Pinf = Pinf - Kinf*transpose(Kinf)/Finf;
|
||||||
lik(t) = lik(t) + log(Finf);
|
lik(t) = lik(t) + log(Finf);
|
||||||
else %% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition
|
else %% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition
|
||||||
%% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that
|
%% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that
|
||||||
%% rank(Pinf)=0. [stéphane,11-03-2004].
|
%% rank(Pinf)=0. [stéphane,11-03-2004].
|
||||||
if rank(Pinf) == 0
|
if rank(Pinf) == 0
|
||||||
lik(t) = lik(t) + log(Fstar) + v(i)*v(i)/Fstar;
|
lik(t) = lik(t) + log(Fstar) + v(i)*v(i)/Fstar;
|
||||||
end
|
end
|
||||||
a = a + Kstar*v(i)/Fstar;
|
a = a + Kstar*v(i)/Fstar;
|
||||||
Pstar = Pstar - Kstar*transpose(Kstar)/Fstar;
|
Pstar = Pstar - Kstar*transpose(Kstar)/Fstar;
|
||||||
end
|
end
|
||||||
oldRank = rank(Pinf,crit);
|
oldRank = rank(Pinf,crit);
|
||||||
a = T*a;
|
a = T*a;
|
||||||
Pstar = T*Pstar*transpose(T)+QQ;
|
Pstar = T*Pstar*transpose(T)+QQ;
|
||||||
Pinf = T*Pinf*transpose(T);
|
Pinf = T*Pinf*transpose(T);
|
||||||
newRank = rank(Pinf,crit);
|
newRank = rank(Pinf,crit);
|
||||||
if oldRank ~= newRank
|
if oldRank ~= newRank
|
||||||
disp('DiffuseLiklihoodH3 :: T does influence the rank of Pinf!')
|
disp('DiffuseLiklihoodH3 :: T does influence the rank of Pinf!')
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if t == smpl
|
if t == smpl
|
||||||
error(['There isn''t enough information to estimate the initial' ...
|
error(['There isn''t enough information to estimate the initial' ...
|
||||||
' conditions of the nonstationary variables']);
|
' conditions of the nonstationary variables']);
|
||||||
end
|
end
|
||||||
while notsteady & t < smpl
|
while notsteady & t < smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
for i=1:pp
|
for i=1:pp
|
||||||
v(i) = Y(i,t) - a(mf(i)) - trend(i,t) -a(mm+i);
|
v(i) = Y(i,t) - a(mf(i)) - trend(i,t) -a(mm+i);
|
||||||
Fi = Pstar(mf(i),mf(i))+Pstar(mm+i,mm+i);
|
Fi = Pstar(mf(i),mf(i))+Pstar(mm+i,mm+i);
|
||||||
if Fi > crit
|
if Fi > crit
|
||||||
Ki = Pstar(:,mf(i))+Pstar(:,mm+i);
|
Ki = Pstar(:,mf(i))+Pstar(:,mm+i);
|
||||||
a = a + Ki*v(i)/Fi;
|
a = a + Ki*v(i)/Fi;
|
||||||
Pstar = Pstar - Ki*transpose(Ki)/Fi;
|
Pstar = Pstar - Ki*transpose(Ki)/Fi;
|
||||||
lik(t) = lik(t) + log(Fi) + v(i)*v(i)/Fi;
|
lik(t) = lik(t) + log(Fi) + v(i)*v(i)/Fi;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
oldP = Pstar;
|
oldP = Pstar;
|
||||||
a = T*a;
|
a = T*a;
|
||||||
Pstar = T*Pstar*transpose(T) + QQ;
|
Pstar = T*Pstar*transpose(T) + QQ;
|
||||||
notsteady = ~(max(max(abs(Pstar-oldP)))<crit);
|
notsteady = ~(max(max(abs(Pstar-oldP)))<crit);
|
||||||
end
|
end
|
||||||
while t < smpl
|
while t < smpl
|
||||||
t = t+1;
|
t = t+1;
|
||||||
for i=1:pp
|
for i=1:pp
|
||||||
v(i) = Y(i,t) - a(mf(i)) - trend(i,t) - a(mm+i);
|
v(i) = Y(i,t) - a(mf(i)) - trend(i,t) - a(mm+i);
|
||||||
Fi = Pstar(mf(i),mf(i))+Pstar(mm+i,mm+i);
|
Fi = Pstar(mf(i),mf(i))+Pstar(mm+i,mm+i);
|
||||||
if Fi > crit
|
if Fi > crit
|
||||||
Ki = Pstar(:,mf(i))+Pstar(:,mm+i);
|
Ki = Pstar(:,mf(i))+Pstar(:,mm+i);
|
||||||
a = a + Ki*v(i)/Fi;
|
a = a + Ki*v(i)/Fi;
|
||||||
Pstar = Pstar - Ki*transpose(Ki)/Fi;
|
Pstar = Pstar - Ki*transpose(Ki)/Fi;
|
||||||
lik(t) = lik(t) + log(Fi) + v(i)*v(i)/Fi;
|
lik(t) = lik(t) + log(Fi) + v(i)*v(i)/Fi;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
a = T*a;
|
a = T*a;
|
||||||
end
|
end
|
||||||
% adding log-likelihhod constants
|
% adding log-likelihhod constants
|
||||||
|
|
|
||||||
|
|
@ -38,290 +38,290 @@ function [fval,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend,data
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
global bayestopt_ estim_params_ options_ trend_coeff_ M_ oo_
|
global bayestopt_ estim_params_ options_ trend_coeff_ M_ oo_
|
||||||
fval = [];
|
fval = [];
|
||||||
ys = [];
|
ys = [];
|
||||||
trend_coeff = [];
|
trend_coeff = [];
|
||||||
cost_flag = 1;
|
cost_flag = 1;
|
||||||
nobs = size(options_.varobs,1);
|
nobs = size(options_.varobs,1);
|
||||||
%------------------------------------------------------------------------------
|
%------------------------------------------------------------------------------
|
||||||
% 1. Get the structural parameters & define penalties
|
% 1. Get the structural parameters & define penalties
|
||||||
%------------------------------------------------------------------------------
|
%------------------------------------------------------------------------------
|
||||||
if options_.mode_compute ~= 1 & any(xparam1 < bayestopt_.lb)
|
if options_.mode_compute ~= 1 & any(xparam1 < bayestopt_.lb)
|
||||||
k = find(xparam1 < bayestopt_.lb);
|
k = find(xparam1 < bayestopt_.lb);
|
||||||
fval = bayestopt_.penalty+sum((bayestopt_.lb(k)-xparam1(k)).^2);
|
fval = bayestopt_.penalty+sum((bayestopt_.lb(k)-xparam1(k)).^2);
|
||||||
cost_flag = 0;
|
cost_flag = 0;
|
||||||
info = 41;
|
info = 41;
|
||||||
return;
|
return;
|
||||||
end
|
end
|
||||||
if options_.mode_compute ~= 1 & any(xparam1 > bayestopt_.ub)
|
if options_.mode_compute ~= 1 & any(xparam1 > bayestopt_.ub)
|
||||||
k = find(xparam1 > bayestopt_.ub);
|
k = find(xparam1 > bayestopt_.ub);
|
||||||
fval = bayestopt_.penalty+sum((xparam1(k)-bayestopt_.ub(k)).^2);
|
fval = bayestopt_.penalty+sum((xparam1(k)-bayestopt_.ub(k)).^2);
|
||||||
cost_flag = 0;
|
cost_flag = 0;
|
||||||
info = 42;
|
info = 42;
|
||||||
return;
|
return;
|
||||||
end
|
end
|
||||||
Q = M_.Sigma_e;
|
Q = M_.Sigma_e;
|
||||||
H = M_.H;
|
H = M_.H;
|
||||||
for i=1:estim_params_.nvx
|
for i=1:estim_params_.nvx
|
||||||
k =estim_params_.var_exo(i,1);
|
k =estim_params_.var_exo(i,1);
|
||||||
Q(k,k) = xparam1(i)*xparam1(i);
|
Q(k,k) = xparam1(i)*xparam1(i);
|
||||||
end
|
end
|
||||||
offset = estim_params_.nvx;
|
offset = estim_params_.nvx;
|
||||||
if estim_params_.nvn
|
if estim_params_.nvn
|
||||||
for i=1:estim_params_.nvn
|
for i=1:estim_params_.nvn
|
||||||
k = estim_params_.var_endo(i,1);
|
k = estim_params_.var_endo(i,1);
|
||||||
H(k,k) = xparam1(i+offset)*xparam1(i+offset);
|
H(k,k) = xparam1(i+offset)*xparam1(i+offset);
|
||||||
end
|
end
|
||||||
offset = offset+estim_params_.nvn;
|
offset = offset+estim_params_.nvn;
|
||||||
end
|
end
|
||||||
if estim_params_.ncx
|
if estim_params_.ncx
|
||||||
for i=1:estim_params_.ncx
|
for i=1:estim_params_.ncx
|
||||||
k1 =estim_params_.corrx(i,1);
|
k1 =estim_params_.corrx(i,1);
|
||||||
k2 =estim_params_.corrx(i,2);
|
k2 =estim_params_.corrx(i,2);
|
||||||
Q(k1,k2) = xparam1(i+offset)*sqrt(Q(k1,k1)*Q(k2,k2));
|
Q(k1,k2) = xparam1(i+offset)*sqrt(Q(k1,k1)*Q(k2,k2));
|
||||||
Q(k2,k1) = Q(k1,k2);
|
Q(k2,k1) = Q(k1,k2);
|
||||||
end
|
end
|
||||||
[CholQ,testQ] = chol(Q);
|
[CholQ,testQ] = chol(Q);
|
||||||
if testQ %% The variance-covariance matrix of the structural innovations is not definite positive.
|
if testQ %% The variance-covariance matrix of the structural innovations is not definite positive.
|
||||||
%% We have to compute the eigenvalues of this matrix in order to build the penalty.
|
%% We have to compute the eigenvalues of this matrix in order to build the penalty.
|
||||||
a = diag(eig(Q));
|
a = diag(eig(Q));
|
||||||
k = find(a < 0);
|
k = find(a < 0);
|
||||||
if k > 0
|
if k > 0
|
||||||
fval = bayestopt_.penalty+sum(-a(k));
|
fval = bayestopt_.penalty+sum(-a(k));
|
||||||
cost_flag = 0;
|
cost_flag = 0;
|
||||||
info = 43;
|
info = 43;
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
offset = offset+estim_params_.ncx;
|
offset = offset+estim_params_.ncx;
|
||||||
end
|
end
|
||||||
if estim_params_.ncn
|
if estim_params_.ncn
|
||||||
for i=1:estim_params_.ncn
|
for i=1:estim_params_.ncn
|
||||||
k1 = options_.lgyidx2varobs(estim_params_.corrn(i,1));
|
k1 = options_.lgyidx2varobs(estim_params_.corrn(i,1));
|
||||||
k2 = options_.lgyidx2varobs(estim_params_.corrn(i,2));
|
k2 = options_.lgyidx2varobs(estim_params_.corrn(i,2));
|
||||||
H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2));
|
H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2));
|
||||||
H(k2,k1) = H(k1,k2);
|
H(k2,k1) = H(k1,k2);
|
||||||
end
|
end
|
||||||
[CholH,testH] = chol(H);
|
[CholH,testH] = chol(H);
|
||||||
if testH
|
if testH
|
||||||
a = diag(eig(H));
|
a = diag(eig(H));
|
||||||
k = find(a < 0);
|
k = find(a < 0);
|
||||||
if k > 0
|
if k > 0
|
||||||
fval = bayestopt_.penalty+sum(-a(k));
|
fval = bayestopt_.penalty+sum(-a(k));
|
||||||
cost_flag = 0;
|
cost_flag = 0;
|
||||||
info = 44;
|
info = 44;
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
offset = offset+estim_params_.ncn;
|
offset = offset+estim_params_.ncn;
|
||||||
end
|
end
|
||||||
if estim_params_.np > 0
|
if estim_params_.np > 0
|
||||||
M_.params(estim_params_.param_vals(:,1)) = xparam1(offset+1:end);
|
M_.params(estim_params_.param_vals(:,1)) = xparam1(offset+1:end);
|
||||||
end
|
end
|
||||||
M_.Sigma_e = Q;
|
M_.Sigma_e = Q;
|
||||||
M_.H = H;
|
M_.H = H;
|
||||||
%------------------------------------------------------------------------------
|
%------------------------------------------------------------------------------
|
||||||
% 2. call model setup & reduction program
|
% 2. call model setup & reduction program
|
||||||
%------------------------------------------------------------------------------
|
%------------------------------------------------------------------------------
|
||||||
[T,R,SteadyState,info] = dynare_resolve(bayestopt_.restrict_var_list,...
|
[T,R,SteadyState,info] = dynare_resolve(bayestopt_.restrict_var_list,...
|
||||||
bayestopt_.restrict_columns,...
|
bayestopt_.restrict_columns,...
|
||||||
bayestopt_.restrict_aux);
|
bayestopt_.restrict_aux);
|
||||||
if info(1) == 1 || info(1) == 2 || info(1) == 5
|
if info(1) == 1 || info(1) == 2 || info(1) == 5
|
||||||
fval = bayestopt_.penalty+1;
|
fval = bayestopt_.penalty+1;
|
||||||
cost_flag = 0;
|
cost_flag = 0;
|
||||||
return
|
return
|
||||||
elseif info(1) == 3 || info(1) == 4 || info(1)==6 ||info(1) == 19 || info(1) == 20 || info(1) == 21
|
elseif info(1) == 3 || info(1) == 4 || info(1)==6 ||info(1) == 19 || info(1) == 20 || info(1) == 21
|
||||||
fval = bayestopt_.penalty+info(2);
|
fval = bayestopt_.penalty+info(2);
|
||||||
cost_flag = 0;
|
cost_flag = 0;
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
bayestopt_.mf = bayestopt_.mf1;
|
bayestopt_.mf = bayestopt_.mf1;
|
||||||
if options_.noconstant
|
if options_.noconstant
|
||||||
constant = zeros(nobs,1);
|
constant = zeros(nobs,1);
|
||||||
else
|
else
|
||||||
if options_.loglinear
|
if options_.loglinear
|
||||||
constant = log(SteadyState(bayestopt_.mfys));
|
constant = log(SteadyState(bayestopt_.mfys));
|
||||||
else
|
else
|
||||||
constant = SteadyState(bayestopt_.mfys);
|
constant = SteadyState(bayestopt_.mfys);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if bayestopt_.with_trend
|
if bayestopt_.with_trend
|
||||||
trend_coeff = zeros(nobs,1);
|
trend_coeff = zeros(nobs,1);
|
||||||
t = options_.trend_coeffs;
|
t = options_.trend_coeffs;
|
||||||
for i=1:length(t)
|
for i=1:length(t)
|
||||||
if ~isempty(t{i})
|
if ~isempty(t{i})
|
||||||
trend_coeff(i) = evalin('base',t{i});
|
trend_coeff(i) = evalin('base',t{i});
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
trend = repmat(constant,1,gend)+trend_coeff*[1:gend];
|
trend = repmat(constant,1,gend)+trend_coeff*[1:gend];
|
||||||
else
|
else
|
||||||
trend = repmat(constant,1,gend);
|
trend = repmat(constant,1,gend);
|
||||||
end
|
end
|
||||||
start = options_.presample+1;
|
start = options_.presample+1;
|
||||||
np = size(T,1);
|
np = size(T,1);
|
||||||
mf = bayestopt_.mf;
|
mf = bayestopt_.mf;
|
||||||
no_missing_data_flag = (number_of_observations==gend*nobs);
|
no_missing_data_flag = (number_of_observations==gend*nobs);
|
||||||
%------------------------------------------------------------------------------
|
%------------------------------------------------------------------------------
|
||||||
% 3. Initial condition of the Kalman filter
|
% 3. Initial condition of the Kalman filter
|
||||||
%------------------------------------------------------------------------------
|
%------------------------------------------------------------------------------
|
||||||
kalman_algo = options_.kalman_algo;
|
kalman_algo = options_.kalman_algo;
|
||||||
if options_.lik_init == 1 % Kalman filter
|
if options_.lik_init == 1 % Kalman filter
|
||||||
if kalman_algo ~= 2
|
if kalman_algo ~= 2
|
||||||
kalman_algo = 1;
|
kalman_algo = 1;
|
||||||
end
|
end
|
||||||
Pstar = lyapunov_symm(T,R*Q*R',options_.qz_criterium,options_.lyapunov_complex_threshold);
|
Pstar = lyapunov_symm(T,R*Q*R',options_.qz_criterium,options_.lyapunov_complex_threshold);
|
||||||
Pinf = [];
|
Pinf = [];
|
||||||
elseif options_.lik_init == 2 % Old Diffuse Kalman filter
|
elseif options_.lik_init == 2 % Old Diffuse Kalman filter
|
||||||
if kalman_algo ~= 2
|
if kalman_algo ~= 2
|
||||||
kalman_algo = 1;
|
kalman_algo = 1;
|
||||||
end
|
end
|
||||||
Pstar = options_.Harvey_scale_factor*eye(np);
|
Pstar = options_.Harvey_scale_factor*eye(np);
|
||||||
Pinf = [];
|
Pinf = [];
|
||||||
elseif options_.lik_init == 3 % Diffuse Kalman filter
|
elseif options_.lik_init == 3 % Diffuse Kalman filter
|
||||||
if kalman_algo ~= 4
|
if kalman_algo ~= 4
|
||||||
kalman_algo = 3;
|
kalman_algo = 3;
|
||||||
end
|
end
|
||||||
[QT,ST] = schur(T);
|
[QT,ST] = schur(T);
|
||||||
e1 = abs(ordeig(ST)) > 2-options_.qz_criterium;
|
e1 = abs(ordeig(ST)) > 2-options_.qz_criterium;
|
||||||
[QT,ST] = ordschur(QT,ST,e1);
|
[QT,ST] = ordschur(QT,ST,e1);
|
||||||
k = find(abs(ordeig(ST)) > 2-options_.qz_criterium);
|
k = find(abs(ordeig(ST)) > 2-options_.qz_criterium);
|
||||||
nk = length(k);
|
nk = length(k);
|
||||||
nk1 = nk+1;
|
nk1 = nk+1;
|
||||||
Pinf = zeros(np,np);
|
Pinf = zeros(np,np);
|
||||||
Pinf(1:nk,1:nk) = eye(nk);
|
Pinf(1:nk,1:nk) = eye(nk);
|
||||||
Pstar = zeros(np,np);
|
Pstar = zeros(np,np);
|
||||||
B = QT'*R*Q*R'*QT;
|
B = QT'*R*Q*R'*QT;
|
||||||
for i=np:-1:nk+2
|
for i=np:-1:nk+2
|
||||||
if ST(i,i-1) == 0
|
if ST(i,i-1) == 0
|
||||||
if i == np
|
if i == np
|
||||||
c = zeros(np-nk,1);
|
c = zeros(np-nk,1);
|
||||||
else
|
else
|
||||||
c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
|
c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
|
||||||
ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
|
ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
|
||||||
end
|
end
|
||||||
q = eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i);
|
q = eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i);
|
||||||
Pstar(nk1:i,i) = q\(B(nk1:i,i)+c);
|
Pstar(nk1:i,i) = q\(B(nk1:i,i)+c);
|
||||||
Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
|
Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
|
||||||
else
|
else
|
||||||
if i == np
|
if i == np
|
||||||
c = zeros(np-nk,1);
|
c = zeros(np-nk,1);
|
||||||
c1 = zeros(np-nk,1);
|
c1 = zeros(np-nk,1);
|
||||||
else
|
else
|
||||||
c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
|
c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
|
||||||
ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i)+...
|
ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i)+...
|
||||||
ST(i,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1);
|
ST(i,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1);
|
||||||
c1 = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i-1,i+1:end)')+...
|
c1 = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i-1,i+1:end)')+...
|
||||||
ST(i-1,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1)+...
|
ST(i-1,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1)+...
|
||||||
ST(i-1,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
|
ST(i-1,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
|
||||||
end
|
end
|
||||||
q = [eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i) -ST(nk1:i,nk1:i)*ST(i,i-1);...
|
q = [eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i) -ST(nk1:i,nk1:i)*ST(i,i-1);...
|
||||||
-ST(nk1:i,nk1:i)*ST(i-1,i) eye(i-nk)-ST(nk1:i,nk1:i)*ST(i-1,i-1)];
|
-ST(nk1:i,nk1:i)*ST(i-1,i) eye(i-nk)-ST(nk1:i,nk1:i)*ST(i-1,i-1)];
|
||||||
z = q\[B(nk1:i,i)+c;B(nk1:i,i-1)+c1];
|
z = q\[B(nk1:i,i)+c;B(nk1:i,i-1)+c1];
|
||||||
Pstar(nk1:i,i) = z(1:(i-nk));
|
Pstar(nk1:i,i) = z(1:(i-nk));
|
||||||
Pstar(nk1:i,i-1) = z(i-nk+1:end);
|
Pstar(nk1:i,i-1) = z(i-nk+1:end);
|
||||||
Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
|
Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
|
||||||
Pstar(i-1,nk1:i-2) = Pstar(nk1:i-2,i-1)';
|
Pstar(i-1,nk1:i-2) = Pstar(nk1:i-2,i-1)';
|
||||||
i = i - 1;
|
i = i - 1;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if i == nk+2
|
if i == nk+2
|
||||||
c = ST(nk+1,:)*(Pstar(:,nk+2:end)*ST(nk1,nk+2:end)')+ST(nk1,nk1)*ST(nk1,nk+2:end)*Pstar(nk+2:end,nk1);
|
c = ST(nk+1,:)*(Pstar(:,nk+2:end)*ST(nk1,nk+2:end)')+ST(nk1,nk1)*ST(nk1,nk+2:end)*Pstar(nk+2:end,nk1);
|
||||||
Pstar(nk1,nk1)=(B(nk1,nk1)+c)/(1-ST(nk1,nk1)*ST(nk1,nk1));
|
Pstar(nk1,nk1)=(B(nk1,nk1)+c)/(1-ST(nk1,nk1)*ST(nk1,nk1));
|
||||||
end
|
end
|
||||||
Z = QT(mf,:);
|
Z = QT(mf,:);
|
||||||
R1 = QT'*R;
|
R1 = QT'*R;
|
||||||
[QQ,RR,EE] = qr(Z*ST(:,1:nk),0);
|
[QQ,RR,EE] = qr(Z*ST(:,1:nk),0);
|
||||||
k = find(abs(diag([RR; zeros(nk-size(Z,1),size(RR,2))])) < 1e-8);
|
k = find(abs(diag([RR; zeros(nk-size(Z,1),size(RR,2))])) < 1e-8);
|
||||||
if length(k) > 0
|
if length(k) > 0
|
||||||
k1 = EE(:,k);
|
k1 = EE(:,k);
|
||||||
dd =ones(nk,1);
|
dd =ones(nk,1);
|
||||||
dd(k1) = zeros(length(k1),1);
|
dd(k1) = zeros(length(k1),1);
|
||||||
Pinf(1:nk,1:nk) = diag(dd);
|
Pinf(1:nk,1:nk) = diag(dd);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if kalman_algo == 2
|
if kalman_algo == 2
|
||||||
end
|
end
|
||||||
kalman_tol = options_.kalman_tol;
|
kalman_tol = options_.kalman_tol;
|
||||||
riccati_tol = options_.riccati_tol;
|
riccati_tol = options_.riccati_tol;
|
||||||
mf = bayestopt_.mf1;
|
mf = bayestopt_.mf1;
|
||||||
Y = data-trend;
|
Y = data-trend;
|
||||||
%------------------------------------------------------------------------------
|
%------------------------------------------------------------------------------
|
||||||
% 4. Likelihood evaluation
|
% 4. Likelihood evaluation
|
||||||
%------------------------------------------------------------------------------
|
%------------------------------------------------------------------------------
|
||||||
if (kalman_algo==1)% Multivariate Kalman Filter
|
if (kalman_algo==1)% Multivariate Kalman Filter
|
||||||
if no_missing_data_flag
|
if no_missing_data_flag
|
||||||
LIK = kalman_filter(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol);
|
LIK = kalman_filter(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol);
|
||||||
else
|
else
|
||||||
LIK = ...
|
LIK = ...
|
||||||
missing_observations_kalman_filter(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol, ...
|
missing_observations_kalman_filter(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol, ...
|
||||||
data_index,number_of_observations,no_more_missing_observations);
|
data_index,number_of_observations,no_more_missing_observations);
|
||||||
end
|
end
|
||||||
if isinf(LIK)
|
if isinf(LIK)
|
||||||
kalman_algo = 2;
|
kalman_algo = 2;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if (kalman_algo==2)% Univariate Kalman Filter
|
if (kalman_algo==2)% Univariate Kalman Filter
|
||||||
no_correlation_flag = 1;
|
no_correlation_flag = 1;
|
||||||
if length(H)==1 & H == 0
|
if length(H)==1 & H == 0
|
||||||
H = zeros(nobs,1);
|
H = zeros(nobs,1);
|
||||||
else
|
else
|
||||||
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
|
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
|
||||||
H = diag(H);
|
H = diag(H);
|
||||||
else
|
else
|
||||||
no_correlation_flag = 0;
|
no_correlation_flag = 0;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if no_correlation_flag
|
if no_correlation_flag
|
||||||
LIK = univariate_kalman_filter(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol,data_index,number_of_observations,no_more_missing_observations);
|
LIK = univariate_kalman_filter(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol,data_index,number_of_observations,no_more_missing_observations);
|
||||||
else
|
else
|
||||||
LIK = univariate_kalman_filter_corr(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol,data_index,number_of_observations,no_more_missing_observations);
|
LIK = univariate_kalman_filter_corr(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol,data_index,number_of_observations,no_more_missing_observations);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if (kalman_algo==3)% Multivariate Diffuse Kalman Filter
|
if (kalman_algo==3)% Multivariate Diffuse Kalman Filter
|
||||||
if no_missing_data_flag
|
if no_missing_data_flag
|
||||||
LIK = diffuse_kalman_filter(ST,R1,Q,H,Pinf,Pstar,Y,start,Z,kalman_tol, ...
|
LIK = diffuse_kalman_filter(ST,R1,Q,H,Pinf,Pstar,Y,start,Z,kalman_tol, ...
|
||||||
riccati_tol);
|
riccati_tol);
|
||||||
else
|
else
|
||||||
LIK = missing_observations_diffuse_kalman_filter(ST,R1,Q,H,Pinf, ...
|
LIK = missing_observations_diffuse_kalman_filter(ST,R1,Q,H,Pinf, ...
|
||||||
Pstar,Y,start,Z,kalman_tol,riccati_tol,...
|
Pstar,Y,start,Z,kalman_tol,riccati_tol,...
|
||||||
data_index,number_of_observations,...
|
data_index,number_of_observations,...
|
||||||
no_more_missing_observations);
|
no_more_missing_observations);
|
||||||
end
|
end
|
||||||
if isinf(LIK)
|
if isinf(LIK)
|
||||||
kalman_algo = 4;
|
kalman_algo = 4;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if (kalman_algo==4)% Univariate Diffuse Kalman Filter
|
if (kalman_algo==4)% Univariate Diffuse Kalman Filter
|
||||||
no_correlation_flag = 1;
|
no_correlation_flag = 1;
|
||||||
if length(H)==1 & H == 0
|
if length(H)==1 & H == 0
|
||||||
H = zeros(nobs,1);
|
H = zeros(nobs,1);
|
||||||
else
|
else
|
||||||
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
|
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
|
||||||
H = diag(H);
|
H = diag(H);
|
||||||
else
|
else
|
||||||
no_correlation_flag = 0;
|
no_correlation_flag = 0;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if no_correlation_flag
|
if no_correlation_flag
|
||||||
LIK = univariate_diffuse_kalman_filter(ST,R1,Q,H,Pinf,Pstar,Y, ...
|
LIK = univariate_diffuse_kalman_filter(ST,R1,Q,H,Pinf,Pstar,Y, ...
|
||||||
start,Z,kalman_tol,riccati_tol,data_index,...
|
start,Z,kalman_tol,riccati_tol,data_index,...
|
||||||
number_of_observations,no_more_missing_observations);
|
number_of_observations,no_more_missing_observations);
|
||||||
else
|
else
|
||||||
LIK = univariate_diffuse_kalman_filter_corr(ST,R1,Q,H,Pinf,Pstar, ...
|
LIK = univariate_diffuse_kalman_filter_corr(ST,R1,Q,H,Pinf,Pstar, ...
|
||||||
Y,start,Z,kalman_tol,riccati_tol,...
|
Y,start,Z,kalman_tol,riccati_tol,...
|
||||||
data_index,number_of_observations,...
|
data_index,number_of_observations,...
|
||||||
no_more_missing_observations);
|
no_more_missing_observations);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if imag(LIK) ~= 0
|
if imag(LIK) ~= 0
|
||||||
likelihood = bayestopt_.penalty;
|
likelihood = bayestopt_.penalty;
|
||||||
else
|
else
|
||||||
likelihood = LIK;
|
likelihood = LIK;
|
||||||
end
|
end
|
||||||
% ------------------------------------------------------------------------------
|
% ------------------------------------------------------------------------------
|
||||||
% Adds prior if necessary
|
% Adds prior if necessary
|
||||||
% ------------------------------------------------------------------------------
|
% ------------------------------------------------------------------------------
|
||||||
lnprior = priordens(xparam1,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
|
lnprior = priordens(xparam1,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
|
||||||
fval = (likelihood-lnprior);
|
fval = (likelihood-lnprior);
|
||||||
options_.kalman_algo = kalman_algo;
|
options_.kalman_algo = kalman_algo;
|
||||||
|
|
@ -38,284 +38,283 @@ function [fval,llik,cost_flag,ys,trend_coeff,info] = DsgeLikelihood_hh(xparam1,g
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
global bayestopt_ estim_params_ options_ trend_coeff_ M_ oo_
|
global bayestopt_ estim_params_ options_ trend_coeff_ M_ oo_
|
||||||
fval = [];
|
fval = [];
|
||||||
ys = [];
|
ys = [];
|
||||||
trend_coeff = [];
|
trend_coeff = [];
|
||||||
llik = NaN;
|
llik = NaN;
|
||||||
cost_flag = 1;
|
cost_flag = 1;
|
||||||
nobs = size(options_.varobs,1);
|
nobs = size(options_.varobs,1);
|
||||||
%------------------------------------------------------------------------------
|
%------------------------------------------------------------------------------
|
||||||
% 1. Get the structural parameters & define penalties
|
% 1. Get the structural parameters & define penalties
|
||||||
%------------------------------------------------------------------------------
|
%------------------------------------------------------------------------------
|
||||||
if options_.mode_compute ~= 1 & any(xparam1 < bayestopt_.lb)
|
if options_.mode_compute ~= 1 & any(xparam1 < bayestopt_.lb)
|
||||||
k = find(xparam1 < bayestopt_.lb);
|
k = find(xparam1 < bayestopt_.lb);
|
||||||
fval = bayestopt_.penalty+sum((bayestopt_.lb(k)-xparam1(k)).^2);
|
fval = bayestopt_.penalty+sum((bayestopt_.lb(k)-xparam1(k)).^2);
|
||||||
cost_flag = 0;
|
cost_flag = 0;
|
||||||
info = 41;
|
info = 41;
|
||||||
return;
|
return;
|
||||||
end
|
end
|
||||||
if options_.mode_compute ~= 1 & any(xparam1 > bayestopt_.ub)
|
if options_.mode_compute ~= 1 & any(xparam1 > bayestopt_.ub)
|
||||||
k = find(xparam1 > bayestopt_.ub);
|
k = find(xparam1 > bayestopt_.ub);
|
||||||
fval = bayestopt_.penalty+sum((xparam1(k)-bayestopt_.ub(k)).^2);
|
fval = bayestopt_.penalty+sum((xparam1(k)-bayestopt_.ub(k)).^2);
|
||||||
cost_flag = 0;
|
cost_flag = 0;
|
||||||
info = 42;
|
info = 42;
|
||||||
return;
|
return;
|
||||||
end
|
end
|
||||||
Q = M_.Sigma_e;
|
Q = M_.Sigma_e;
|
||||||
H = M_.H;
|
H = M_.H;
|
||||||
for i=1:estim_params_.nvx
|
for i=1:estim_params_.nvx
|
||||||
k =estim_params_.var_exo(i,1);
|
k =estim_params_.var_exo(i,1);
|
||||||
Q(k,k) = xparam1(i)*xparam1(i);
|
Q(k,k) = xparam1(i)*xparam1(i);
|
||||||
end
|
end
|
||||||
offset = estim_params_.nvx;
|
offset = estim_params_.nvx;
|
||||||
if estim_params_.nvn
|
if estim_params_.nvn
|
||||||
for i=1:estim_params_.nvn
|
for i=1:estim_params_.nvn
|
||||||
k = estim_params_.var_endo(i,1);
|
k = estim_params_.var_endo(i,1);
|
||||||
H(k,k) = xparam1(i+offset)*xparam1(i+offset);
|
H(k,k) = xparam1(i+offset)*xparam1(i+offset);
|
||||||
end
|
end
|
||||||
offset = offset+estim_params_.nvn;
|
offset = offset+estim_params_.nvn;
|
||||||
end
|
end
|
||||||
if estim_params_.ncx
|
if estim_params_.ncx
|
||||||
for i=1:estim_params_.ncx
|
for i=1:estim_params_.ncx
|
||||||
k1 =estim_params_.corrx(i,1);
|
k1 =estim_params_.corrx(i,1);
|
||||||
k2 =estim_params_.corrx(i,2);
|
k2 =estim_params_.corrx(i,2);
|
||||||
Q(k1,k2) = xparam1(i+offset)*sqrt(Q(k1,k1)*Q(k2,k2));
|
Q(k1,k2) = xparam1(i+offset)*sqrt(Q(k1,k1)*Q(k2,k2));
|
||||||
Q(k2,k1) = Q(k1,k2);
|
Q(k2,k1) = Q(k1,k2);
|
||||||
end
|
end
|
||||||
[CholQ,testQ] = chol(Q);
|
[CholQ,testQ] = chol(Q);
|
||||||
if testQ %% The variance-covariance matrix of the structural innovations is not definite positive.
|
if testQ %% The variance-covariance matrix of the structural innovations is not definite positive.
|
||||||
%% We have to compute the eigenvalues of this matrix in order to build the penalty.
|
%% We have to compute the eigenvalues of this matrix in order to build the penalty.
|
||||||
a = diag(eig(Q));
|
a = diag(eig(Q));
|
||||||
k = find(a < 0);
|
k = find(a < 0);
|
||||||
if k > 0
|
if k > 0
|
||||||
fval = bayestopt_.penalty+sum(-a(k));
|
fval = bayestopt_.penalty+sum(-a(k));
|
||||||
cost_flag = 0;
|
cost_flag = 0;
|
||||||
info = 43;
|
info = 43;
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
offset = offset+estim_params_.ncx;
|
offset = offset+estim_params_.ncx;
|
||||||
end
|
end
|
||||||
if estim_params_.ncn
|
if estim_params_.ncn
|
||||||
for i=1:estim_params_.ncn
|
for i=1:estim_params_.ncn
|
||||||
k1 = options_.lgyidx2varobs(estim_params_.corrn(i,1));
|
k1 = options_.lgyidx2varobs(estim_params_.corrn(i,1));
|
||||||
k2 = options_.lgyidx2varobs(estim_params_.corrn(i,2));
|
k2 = options_.lgyidx2varobs(estim_params_.corrn(i,2));
|
||||||
H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2));
|
H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2));
|
||||||
H(k2,k1) = H(k1,k2);
|
H(k2,k1) = H(k1,k2);
|
||||||
end
|
end
|
||||||
[CholH,testH] = chol(H);
|
[CholH,testH] = chol(H);
|
||||||
if testH
|
if testH
|
||||||
a = diag(eig(H));
|
a = diag(eig(H));
|
||||||
k = find(a < 0);
|
k = find(a < 0);
|
||||||
if k > 0
|
if k > 0
|
||||||
fval = bayestopt_.penalty+sum(-a(k));
|
fval = bayestopt_.penalty+sum(-a(k));
|
||||||
cost_flag = 0;
|
cost_flag = 0;
|
||||||
info = 44;
|
info = 44;
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
offset = offset+estim_params_.ncn;
|
offset = offset+estim_params_.ncn;
|
||||||
end
|
end
|
||||||
if estim_params_.np > 0
|
if estim_params_.np > 0
|
||||||
M_.params(estim_params_.param_vals(:,1)) = xparam1(offset+1:end);
|
M_.params(estim_params_.param_vals(:,1)) = xparam1(offset+1:end);
|
||||||
end
|
end
|
||||||
M_.Sigma_e = Q;
|
M_.Sigma_e = Q;
|
||||||
M_.H = H;
|
M_.H = H;
|
||||||
%------------------------------------------------------------------------------
|
%------------------------------------------------------------------------------
|
||||||
% 2. call model setup & reduction program
|
% 2. call model setup & reduction program
|
||||||
%------------------------------------------------------------------------------
|
%------------------------------------------------------------------------------
|
||||||
[T,R,SteadyState,info] = dynare_resolve(bayestopt_.restrict_var_list,...
|
[T,R,SteadyState,info] = dynare_resolve(bayestopt_.restrict_var_list,...
|
||||||
bayestopt_.restrict_columns,...
|
bayestopt_.restrict_columns,...
|
||||||
bayestopt_.restrict_aux);
|
bayestopt_.restrict_aux);
|
||||||
if info(1) == 1 | info(1) == 2 | info(1) == 5
|
if info(1) == 1 | info(1) == 2 | info(1) == 5
|
||||||
fval = bayestopt_.penalty+1;
|
fval = bayestopt_.penalty+1;
|
||||||
cost_flag = 0;
|
cost_flag = 0;
|
||||||
return
|
return
|
||||||
elseif info(1) == 3 | info(1) == 4 | info(1) == 20
|
elseif info(1) == 3 | info(1) == 4 | info(1) == 20
|
||||||
fval = bayestopt_.penalty+info(2);%^2; % penalty power raised in DR1.m and resol already. GP July'08
|
fval = bayestopt_.penalty+info(2);%^2; % penalty power raised in DR1.m and resol already. GP July'08
|
||||||
cost_flag = 0;
|
cost_flag = 0;
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
bayestopt_.mf = bayestopt_.mf1;
|
bayestopt_.mf = bayestopt_.mf1;
|
||||||
if ~options_.noconstant
|
if ~options_.noconstant
|
||||||
if options_.loglinear == 1
|
if options_.loglinear == 1
|
||||||
constant = log(SteadyState(bayestopt_.mfys));
|
constant = log(SteadyState(bayestopt_.mfys));
|
||||||
else
|
else
|
||||||
constant = SteadyState(bayestopt_.mfys);
|
constant = SteadyState(bayestopt_.mfys);
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
constant = zeros(nobs,1);
|
constant = zeros(nobs,1);
|
||||||
end
|
end
|
||||||
if bayestopt_.with_trend == 1
|
if bayestopt_.with_trend == 1
|
||||||
trend_coeff = zeros(nobs,1);
|
trend_coeff = zeros(nobs,1);
|
||||||
t = options_.trend_coeffs;
|
t = options_.trend_coeffs;
|
||||||
for i=1:length(t)
|
for i=1:length(t)
|
||||||
if ~isempty(t{i})
|
if ~isempty(t{i})
|
||||||
trend_coeff(i) = evalin('base',t{i});
|
trend_coeff(i) = evalin('base',t{i});
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
trend = repmat(constant,1,gend)+trend_coeff*[1:gend];
|
trend = repmat(constant,1,gend)+trend_coeff*[1:gend];
|
||||||
else
|
else
|
||||||
trend = repmat(constant,1,gend);
|
trend = repmat(constant,1,gend);
|
||||||
end
|
end
|
||||||
start = options_.presample+1;
|
start = options_.presample+1;
|
||||||
np = size(T,1);
|
np = size(T,1);
|
||||||
mf = bayestopt_.mf;
|
mf = bayestopt_.mf;
|
||||||
no_missing_data_flag = (number_of_observations==gend*nobs);
|
no_missing_data_flag = (number_of_observations==gend*nobs);
|
||||||
%------------------------------------------------------------------------------
|
%------------------------------------------------------------------------------
|
||||||
% 3. Initial condition of the Kalman filter
|
% 3. Initial condition of the Kalman filter
|
||||||
%------------------------------------------------------------------------------
|
%------------------------------------------------------------------------------
|
||||||
kalman_algo = options_.kalman_algo;
|
kalman_algo = options_.kalman_algo;
|
||||||
if options_.lik_init == 1 % Kalman filter
|
if options_.lik_init == 1 % Kalman filter
|
||||||
if kalman_algo ~= 2
|
if kalman_algo ~= 2
|
||||||
kalman_algo = 1;
|
kalman_algo = 1;
|
||||||
end
|
end
|
||||||
Pstar = lyapunov_symm(T,R*Q*R',options_.qz_criterium,options_.lyapunov_complex_threshold);
|
Pstar = lyapunov_symm(T,R*Q*R',options_.qz_criterium,options_.lyapunov_complex_threshold);
|
||||||
Pinf = [];
|
Pinf = [];
|
||||||
elseif options_.lik_init == 2 % Old Diffuse Kalman filter
|
elseif options_.lik_init == 2 % Old Diffuse Kalman filter
|
||||||
if kalman_algo ~= 2
|
if kalman_algo ~= 2
|
||||||
kalman_algo = 1;
|
kalman_algo = 1;
|
||||||
end
|
end
|
||||||
Pstar = options_.Harvey_scale_factor*eye(np);
|
Pstar = options_.Harvey_scale_factor*eye(np);
|
||||||
Pinf = [];
|
Pinf = [];
|
||||||
elseif options_.lik_init == 3 % Diffuse Kalman filter
|
elseif options_.lik_init == 3 % Diffuse Kalman filter
|
||||||
if kalman_algo ~= 4
|
if kalman_algo ~= 4
|
||||||
kalman_algo = 3;
|
kalman_algo = 3;
|
||||||
end
|
end
|
||||||
[QT,ST] = schur(T);
|
[QT,ST] = schur(T);
|
||||||
e1 = abs(ordeig(ST)) > 2-options_.qz_criterium;
|
e1 = abs(ordeig(ST)) > 2-options_.qz_criterium;
|
||||||
[QT,ST] = ordschur(QT,ST,e1);
|
[QT,ST] = ordschur(QT,ST,e1);
|
||||||
k = find(abs(ordeig(ST)) > 2-options_.qz_criterium);
|
k = find(abs(ordeig(ST)) > 2-options_.qz_criterium);
|
||||||
nk = length(k);
|
nk = length(k);
|
||||||
nk1 = nk+1;
|
nk1 = nk+1;
|
||||||
Pinf = zeros(np,np);
|
Pinf = zeros(np,np);
|
||||||
Pinf(1:nk,1:nk) = eye(nk);
|
Pinf(1:nk,1:nk) = eye(nk);
|
||||||
Pstar = zeros(np,np);
|
Pstar = zeros(np,np);
|
||||||
B = QT'*R*Q*R'*QT;
|
B = QT'*R*Q*R'*QT;
|
||||||
for i=np:-1:nk+2
|
for i=np:-1:nk+2
|
||||||
if ST(i,i-1) == 0
|
if ST(i,i-1) == 0
|
||||||
if i == np
|
if i == np
|
||||||
c = zeros(np-nk,1);
|
c = zeros(np-nk,1);
|
||||||
else
|
else
|
||||||
c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
|
c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
|
||||||
ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
|
ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
|
||||||
end
|
end
|
||||||
q = eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i);
|
q = eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i);
|
||||||
Pstar(nk1:i,i) = q\(B(nk1:i,i)+c);
|
Pstar(nk1:i,i) = q\(B(nk1:i,i)+c);
|
||||||
Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
|
Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
|
||||||
else
|
else
|
||||||
if i == np
|
if i == np
|
||||||
c = zeros(np-nk,1);
|
c = zeros(np-nk,1);
|
||||||
c1 = zeros(np-nk,1);
|
c1 = zeros(np-nk,1);
|
||||||
else
|
else
|
||||||
c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
|
c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
|
||||||
ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i)+...
|
ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i)+...
|
||||||
ST(i,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1);
|
ST(i,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1);
|
||||||
c1 = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i-1,i+1:end)')+...
|
c1 = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i-1,i+1:end)')+...
|
||||||
ST(i-1,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1)+...
|
ST(i-1,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1)+...
|
||||||
ST(i-1,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
|
ST(i-1,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
|
||||||
end
|
end
|
||||||
q = [eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i) -ST(nk1:i,nk1:i)*ST(i,i-1);...
|
q = [eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i) -ST(nk1:i,nk1:i)*ST(i,i-1);...
|
||||||
-ST(nk1:i,nk1:i)*ST(i-1,i) eye(i-nk)-ST(nk1:i,nk1:i)*ST(i-1,i-1)];
|
-ST(nk1:i,nk1:i)*ST(i-1,i) eye(i-nk)-ST(nk1:i,nk1:i)*ST(i-1,i-1)];
|
||||||
z = q\[B(nk1:i,i)+c;B(nk1:i,i-1)+c1];
|
z = q\[B(nk1:i,i)+c;B(nk1:i,i-1)+c1];
|
||||||
Pstar(nk1:i,i) = z(1:(i-nk));
|
Pstar(nk1:i,i) = z(1:(i-nk));
|
||||||
Pstar(nk1:i,i-1) = z(i-nk+1:end);
|
Pstar(nk1:i,i-1) = z(i-nk+1:end);
|
||||||
Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
|
Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
|
||||||
Pstar(i-1,nk1:i-2) = Pstar(nk1:i-2,i-1)';
|
Pstar(i-1,nk1:i-2) = Pstar(nk1:i-2,i-1)';
|
||||||
i = i - 1;
|
i = i - 1;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if i == nk+2
|
if i == nk+2
|
||||||
c = ST(nk+1,:)*(Pstar(:,nk+2:end)*ST(nk1,nk+2:end)')+ST(nk1,nk1)*ST(nk1,nk+2:end)*Pstar(nk+2:end,nk1);
|
c = ST(nk+1,:)*(Pstar(:,nk+2:end)*ST(nk1,nk+2:end)')+ST(nk1,nk1)*ST(nk1,nk+2:end)*Pstar(nk+2:end,nk1);
|
||||||
Pstar(nk1,nk1)=(B(nk1,nk1)+c)/(1-ST(nk1,nk1)*ST(nk1,nk1));
|
Pstar(nk1,nk1)=(B(nk1,nk1)+c)/(1-ST(nk1,nk1)*ST(nk1,nk1));
|
||||||
end
|
end
|
||||||
Z = QT(mf,:);
|
Z = QT(mf,:);
|
||||||
R1 = QT'*R;
|
R1 = QT'*R;
|
||||||
[QQ,RR,EE] = qr(Z*ST(:,1:nk),0);
|
[QQ,RR,EE] = qr(Z*ST(:,1:nk),0);
|
||||||
k = find(abs(diag([RR; zeros(nk-size(Z,1),size(RR,2))])) < 1e-8);
|
k = find(abs(diag([RR; zeros(nk-size(Z,1),size(RR,2))])) < 1e-8);
|
||||||
if length(k) > 0
|
if length(k) > 0
|
||||||
k1 = EE(:,k);
|
k1 = EE(:,k);
|
||||||
dd =ones(nk,1);
|
dd =ones(nk,1);
|
||||||
dd(k1) = zeros(length(k1),1);
|
dd(k1) = zeros(length(k1),1);
|
||||||
Pinf(1:nk,1:nk) = diag(dd);
|
Pinf(1:nk,1:nk) = diag(dd);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if kalman_algo == 2
|
if kalman_algo == 2
|
||||||
no_correlation_flag = 1;
|
no_correlation_flag = 1;
|
||||||
if length(H)==1
|
if length(H)==1
|
||||||
H = zeros(nobs,1);
|
H = zeros(nobs,1);
|
||||||
else
|
else
|
||||||
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
|
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
|
||||||
H = diag(H);
|
H = diag(H);
|
||||||
else
|
else
|
||||||
no_correlation_flag = 1;
|
no_correlation_flag = 1;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
kalman_tol = options_.kalman_tol;
|
kalman_tol = options_.kalman_tol;
|
||||||
riccati_tol = options_.riccati_tol;
|
riccati_tol = options_.riccati_tol;
|
||||||
mf = bayestopt_.mf1;
|
mf = bayestopt_.mf1;
|
||||||
Y = data-trend;
|
Y = data-trend;
|
||||||
%------------------------------------------------------------------------------
|
%------------------------------------------------------------------------------
|
||||||
% 4. Likelihood evaluation
|
% 4. Likelihood evaluation
|
||||||
%------------------------------------------------------------------------------
|
%------------------------------------------------------------------------------
|
||||||
if (kalman_algo==1)% Multivariate Kalman Filter
|
if (kalman_algo==1)% Multivariate Kalman Filter
|
||||||
if no_missing_data_flag
|
if no_missing_data_flag
|
||||||
[LIK, lik] = kalman_filter(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol);
|
[LIK, lik] = kalman_filter(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol);
|
||||||
else
|
else
|
||||||
[LIK, lik] = ...
|
[LIK, lik] = ...
|
||||||
missing_observations_kalman_filter(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol, ...
|
missing_observations_kalman_filter(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol, ...
|
||||||
data_index,number_of_observations,no_more_missing_observations);
|
data_index,number_of_observations,no_more_missing_observations);
|
||||||
end
|
end
|
||||||
if isinf(LIK)
|
if isinf(LIK)
|
||||||
kalman_algo = 2;
|
kalman_algo = 2;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if (kalman_algo==2)% Univariate Kalman Filter
|
if (kalman_algo==2)% Univariate Kalman Filter
|
||||||
if no_correlation_flag
|
if no_correlation_flag
|
||||||
[LIK, lik] = univariate_kalman_filter(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol,data_index,number_of_observations,no_more_missing_observations);
|
[LIK, lik] = univariate_kalman_filter(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol,data_index,number_of_observations,no_more_missing_observations);
|
||||||
else
|
else
|
||||||
[LIK, lik] = univariate_kalman_filter_corr(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol,data_index,number_of_observations,no_more_missing_observations);
|
[LIK, lik] = univariate_kalman_filter_corr(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol,data_index,number_of_observations,no_more_missing_observations);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if (kalman_algo==3)% Multivariate Diffuse Kalman Filter
|
if (kalman_algo==3)% Multivariate Diffuse Kalman Filter
|
||||||
if no_missing_data_flag
|
if no_missing_data_flag
|
||||||
data1 = data - trend;
|
data1 = data - trend;
|
||||||
if any(any(H ~= 0))
|
if any(any(H ~= 0))
|
||||||
[LIK, lik] = DiffuseLikelihoodH1_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1,start);
|
[LIK, lik] = DiffuseLikelihoodH1_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1,start);
|
||||||
else
|
else
|
||||||
[LIK, lik] = DiffuseLikelihood1_Z(ST,Z,R1,Q,Pinf,Pstar,data1,start);
|
[LIK, lik] = DiffuseLikelihood1_Z(ST,Z,R1,Q,Pinf,Pstar,data1,start);
|
||||||
end
|
end
|
||||||
if isinf(LIK)
|
if isinf(LIK)
|
||||||
kalman_algo = 4;
|
kalman_algo = 4;
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
error(['The diffuse filter is not yet implemented for models with missing observations'])
|
error(['The diffuse filter is not yet implemented for models with missing observations'])
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if (kalman_algo==4)% Univariate Diffuse Kalman Filter
|
if (kalman_algo==4)% Univariate Diffuse Kalman Filter
|
||||||
data1 = data - trend;
|
data1 = data - trend;
|
||||||
if any(any(H ~= 0))
|
if any(any(H ~= 0))
|
||||||
if ~estim_params_.ncn
|
if ~estim_params_.ncn
|
||||||
[LIK, lik] = DiffuseLikelihoodH3_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1,trend,start);
|
[LIK, lik] = DiffuseLikelihoodH3_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1,trend,start);
|
||||||
else
|
else
|
||||||
[LIK, lik] = DiffuseLikelihoodH3corr_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1,trend,start);
|
[LIK, lik] = DiffuseLikelihoodH3corr_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1,trend,start);
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
[LIK, lik] = DiffuseLikelihood3_Z(ST,Z,R1,Q,Pinf,Pstar,data1,start);
|
[LIK, lik] = DiffuseLikelihood3_Z(ST,Z,R1,Q,Pinf,Pstar,data1,start);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if imag(LIK) ~= 0
|
if imag(LIK) ~= 0
|
||||||
likelihood = bayestopt_.penalty;
|
likelihood = bayestopt_.penalty;
|
||||||
else
|
else
|
||||||
likelihood = LIK;
|
likelihood = LIK;
|
||||||
end
|
end
|
||||||
% ------------------------------------------------------------------------------
|
% ------------------------------------------------------------------------------
|
||||||
% Adds prior if necessary
|
% Adds prior if necessary
|
||||||
% ------------------------------------------------------------------------------
|
% ------------------------------------------------------------------------------
|
||||||
lnprior = priordens(xparam1,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
|
lnprior = priordens(xparam1,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
|
||||||
fval = (likelihood-lnprior);
|
fval = (likelihood-lnprior);
|
||||||
options_.kalman_algo = kalman_algo;
|
options_.kalman_algo = kalman_algo;
|
||||||
llik=[-lnprior; lik(start:end)];
|
llik=[-lnprior; lik(start:end)];
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -47,281 +47,281 @@ function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK,T,R,P,PK,d,
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
global bayestopt_ M_ oo_ estim_params_ options_
|
global bayestopt_ M_ oo_ estim_params_ options_
|
||||||
|
|
||||||
alphahat = [];
|
alphahat = [];
|
||||||
etahat = [];
|
etahat = [];
|
||||||
epsilonhat = [];
|
epsilonhat = [];
|
||||||
ahat = [];
|
ahat = [];
|
||||||
SteadyState = [];
|
SteadyState = [];
|
||||||
trend_coeff = [];
|
trend_coeff = [];
|
||||||
aK = [];
|
aK = [];
|
||||||
T = [];
|
T = [];
|
||||||
R = [];
|
R = [];
|
||||||
P = [];
|
P = [];
|
||||||
PK = [];
|
PK = [];
|
||||||
d = [];
|
d = [];
|
||||||
decomp = [];
|
decomp = [];
|
||||||
nobs = size(options_.varobs,1);
|
nobs = size(options_.varobs,1);
|
||||||
smpl = size(Y,2);
|
smpl = size(Y,2);
|
||||||
|
|
||||||
set_all_parameters(xparam1);
|
set_all_parameters(xparam1);
|
||||||
|
|
||||||
%------------------------------------------------------------------------------
|
%------------------------------------------------------------------------------
|
||||||
% 2. call model setup & reduction program
|
% 2. call model setup & reduction program
|
||||||
%------------------------------------------------------------------------------
|
%------------------------------------------------------------------------------
|
||||||
[T,R,SteadyState] = dynare_resolve;
|
[T,R,SteadyState] = dynare_resolve;
|
||||||
bayestopt_.mf = bayestopt_.mf2;
|
bayestopt_.mf = bayestopt_.mf2;
|
||||||
if options_.noconstant
|
if options_.noconstant
|
||||||
constant = zeros(nobs,1);
|
constant = zeros(nobs,1);
|
||||||
else
|
else
|
||||||
if options_.loglinear == 1
|
if options_.loglinear == 1
|
||||||
constant = log(SteadyState(bayestopt_.mfys));
|
constant = log(SteadyState(bayestopt_.mfys));
|
||||||
else
|
else
|
||||||
constant = SteadyState(bayestopt_.mfys);
|
constant = SteadyState(bayestopt_.mfys);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
trend_coeff = zeros(nobs,1);
|
trend_coeff = zeros(nobs,1);
|
||||||
if bayestopt_.with_trend == 1
|
if bayestopt_.with_trend == 1
|
||||||
trend_coeff = zeros(nobs,1);
|
trend_coeff = zeros(nobs,1);
|
||||||
t = options_.trend_coeffs;
|
t = options_.trend_coeffs;
|
||||||
for i=1:length(t)
|
for i=1:length(t)
|
||||||
if ~isempty(t{i})
|
if ~isempty(t{i})
|
||||||
trend_coeff(i) = evalin('base',t{i});
|
trend_coeff(i) = evalin('base',t{i});
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
trend = constant*ones(1,gend)+trend_coeff*(1:gend);
|
trend = constant*ones(1,gend)+trend_coeff*(1:gend);
|
||||||
else
|
else
|
||||||
trend = constant*ones(1,gend);
|
trend = constant*ones(1,gend);
|
||||||
end
|
end
|
||||||
start = options_.presample+1;
|
start = options_.presample+1;
|
||||||
np = size(T,1);
|
np = size(T,1);
|
||||||
mf = bayestopt_.mf;
|
mf = bayestopt_.mf;
|
||||||
% ------------------------------------------------------------------------------
|
% ------------------------------------------------------------------------------
|
||||||
% 3. Initial condition of the Kalman filter
|
% 3. Initial condition of the Kalman filter
|
||||||
% ------------------------------------------------------------------------------
|
% ------------------------------------------------------------------------------
|
||||||
%
|
%
|
||||||
% C'est ici qu'il faut déterminer Pinf et Pstar. Si le modèle est stationnaire,
|
% C'est ici qu'il faut déterminer Pinf et Pstar. Si le modèle est stationnaire,
|
||||||
% alors il suffit de poser Pstar comme la solution de l'éuation de Lyapounov et
|
% alors il suffit de poser Pstar comme la solution de l'éuation de Lyapounov et
|
||||||
% Pinf=[].
|
% Pinf=[].
|
||||||
%
|
%
|
||||||
Q = M_.Sigma_e;
|
Q = M_.Sigma_e;
|
||||||
H = M_.H;
|
H = M_.H;
|
||||||
|
|
||||||
kalman_algo = options_.kalman_algo;
|
kalman_algo = options_.kalman_algo;
|
||||||
if options_.lik_init == 1 % Kalman filter
|
if options_.lik_init == 1 % Kalman filter
|
||||||
if kalman_algo ~= 2
|
if kalman_algo ~= 2
|
||||||
kalman_algo = 1;
|
kalman_algo = 1;
|
||||||
end
|
end
|
||||||
Pstar = lyapunov_symm(T,R*Q*transpose(R),options_.qz_criterium,options_.lyapunov_complex_threshold);
|
Pstar = lyapunov_symm(T,R*Q*transpose(R),options_.qz_criterium,options_.lyapunov_complex_threshold);
|
||||||
Pinf = [];
|
Pinf = [];
|
||||||
elseif options_.lik_init == 2 % Old Diffuse Kalman filter
|
elseif options_.lik_init == 2 % Old Diffuse Kalman filter
|
||||||
if kalman_algo ~= 2
|
if kalman_algo ~= 2
|
||||||
kalman_algo = 1;
|
kalman_algo = 1;
|
||||||
end
|
end
|
||||||
Pstar = options_.Harvey_scale_factor*eye(np);
|
Pstar = options_.Harvey_scale_factor*eye(np);
|
||||||
Pinf = [];
|
Pinf = [];
|
||||||
elseif options_.lik_init == 3 % Diffuse Kalman filter
|
elseif options_.lik_init == 3 % Diffuse Kalman filter
|
||||||
if kalman_algo ~= 4
|
if kalman_algo ~= 4
|
||||||
kalman_algo = 3;
|
kalman_algo = 3;
|
||||||
end
|
end
|
||||||
[QT,ST] = schur(T);
|
[QT,ST] = schur(T);
|
||||||
e1 = abs(ordeig(ST)) > 2-options_.qz_criterium;
|
e1 = abs(ordeig(ST)) > 2-options_.qz_criterium;
|
||||||
[QT,ST] = ordschur(QT,ST,e1);
|
[QT,ST] = ordschur(QT,ST,e1);
|
||||||
k = find(abs(ordeig(ST)) > 2-options_.qz_criterium);
|
k = find(abs(ordeig(ST)) > 2-options_.qz_criterium);
|
||||||
nk = length(k);
|
nk = length(k);
|
||||||
nk1 = nk+1;
|
nk1 = nk+1;
|
||||||
Pinf = zeros(np,np);
|
Pinf = zeros(np,np);
|
||||||
Pinf(1:nk,1:nk) = eye(nk);
|
Pinf(1:nk,1:nk) = eye(nk);
|
||||||
Pstar = zeros(np,np);
|
Pstar = zeros(np,np);
|
||||||
B = QT'*R*Q*R'*QT;
|
B = QT'*R*Q*R'*QT;
|
||||||
for i=np:-1:nk+2
|
for i=np:-1:nk+2
|
||||||
if ST(i,i-1) == 0
|
if ST(i,i-1) == 0
|
||||||
if i == np
|
if i == np
|
||||||
c = zeros(np-nk,1);
|
c = zeros(np-nk,1);
|
||||||
else
|
else
|
||||||
c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
|
c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
|
||||||
ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
|
ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
|
||||||
end
|
end
|
||||||
q = eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i);
|
q = eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i);
|
||||||
Pstar(nk1:i,i) = q\(B(nk1:i,i)+c);
|
Pstar(nk1:i,i) = q\(B(nk1:i,i)+c);
|
||||||
Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
|
Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
|
||||||
else
|
else
|
||||||
if i == np
|
if i == np
|
||||||
c = zeros(np-nk,1);
|
c = zeros(np-nk,1);
|
||||||
c1 = zeros(np-nk,1);
|
c1 = zeros(np-nk,1);
|
||||||
else
|
else
|
||||||
c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
|
c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
|
||||||
ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i)+...
|
ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i)+...
|
||||||
ST(i,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1);
|
ST(i,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1);
|
||||||
c1 = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i-1,i+1:end)')+...
|
c1 = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i-1,i+1:end)')+...
|
||||||
ST(i-1,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1)+...
|
ST(i-1,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1)+...
|
||||||
ST(i-1,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
|
ST(i-1,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
|
||||||
end
|
end
|
||||||
q = [eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i) -ST(nk1:i,nk1:i)*ST(i,i-1);...
|
q = [eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i) -ST(nk1:i,nk1:i)*ST(i,i-1);...
|
||||||
-ST(nk1:i,nk1:i)*ST(i-1,i) eye(i-nk)-ST(nk1:i,nk1:i)*ST(i-1,i-1)];
|
-ST(nk1:i,nk1:i)*ST(i-1,i) eye(i-nk)-ST(nk1:i,nk1:i)*ST(i-1,i-1)];
|
||||||
z = q\[B(nk1:i,i)+c;B(nk1:i,i-1)+c1];
|
z = q\[B(nk1:i,i)+c;B(nk1:i,i-1)+c1];
|
||||||
Pstar(nk1:i,i) = z(1:(i-nk));
|
Pstar(nk1:i,i) = z(1:(i-nk));
|
||||||
Pstar(nk1:i,i-1) = z(i-nk+1:end);
|
Pstar(nk1:i,i-1) = z(i-nk+1:end);
|
||||||
Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
|
Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
|
||||||
Pstar(i-1,nk1:i-2) = Pstar(nk1:i-2,i-1)';
|
Pstar(i-1,nk1:i-2) = Pstar(nk1:i-2,i-1)';
|
||||||
i = i - 1;
|
i = i - 1;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if i == nk+2
|
if i == nk+2
|
||||||
c = ST(nk+1,:)*(Pstar(:,nk+2:end)*ST(nk1,nk+2:end)')+ST(nk1,nk1)*ST(nk1,nk+2:end)*Pstar(nk+2:end,nk1);
|
c = ST(nk+1,:)*(Pstar(:,nk+2:end)*ST(nk1,nk+2:end)')+ST(nk1,nk1)*ST(nk1,nk+2:end)*Pstar(nk+2:end,nk1);
|
||||||
Pstar(nk1,nk1)=(B(nk1,nk1)+c)/(1-ST(nk1,nk1)*ST(nk1,nk1));
|
Pstar(nk1,nk1)=(B(nk1,nk1)+c)/(1-ST(nk1,nk1)*ST(nk1,nk1));
|
||||||
end
|
end
|
||||||
|
|
||||||
Z = QT(mf,:);
|
Z = QT(mf,:);
|
||||||
R1 = QT'*R;
|
R1 = QT'*R;
|
||||||
[QQ,RR,EE] = qr(Z*ST(:,1:nk),0);
|
[QQ,RR,EE] = qr(Z*ST(:,1:nk),0);
|
||||||
k = find(abs(diag([RR; zeros(nk-size(Z,1),size(RR,2))])) < 1e-8);
|
k = find(abs(diag([RR; zeros(nk-size(Z,1),size(RR,2))])) < 1e-8);
|
||||||
if length(k) > 0
|
if length(k) > 0
|
||||||
k1 = EE(:,k);
|
k1 = EE(:,k);
|
||||||
dd =ones(nk,1);
|
dd =ones(nk,1);
|
||||||
dd(k1) = zeros(length(k1),1);
|
dd(k1) = zeros(length(k1),1);
|
||||||
Pinf(1:nk,1:nk) = diag(dd);
|
Pinf(1:nk,1:nk) = diag(dd);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
% -----------------------------------------------------------------------------
|
% -----------------------------------------------------------------------------
|
||||||
% 4. Kalman smoother
|
% 4. Kalman smoother
|
||||||
% -----------------------------------------------------------------------------
|
% -----------------------------------------------------------------------------
|
||||||
if any(any(H ~= 0)) % should be replaced by a flag
|
if any(any(H ~= 0)) % should be replaced by a flag
|
||||||
if kalman_algo == 1
|
if kalman_algo == 1
|
||||||
[alphahat,epsilonhat,etahat,ahat,aK] = ...
|
[alphahat,epsilonhat,etahat,ahat,aK] = ...
|
||||||
DiffuseKalmanSmootherH1(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
|
DiffuseKalmanSmootherH1(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
|
||||||
if all(alphahat(:)==0)
|
if all(alphahat(:)==0)
|
||||||
kalman_algo = 2;
|
kalman_algo = 2;
|
||||||
if ~estim_params_.ncn
|
if ~estim_params_.ncn
|
||||||
[alphahat,epsilonhat,etahat,ahat,aK] = ...
|
[alphahat,epsilonhat,etahat,ahat,aK] = ...
|
||||||
DiffuseKalmanSmootherH3(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
|
DiffuseKalmanSmootherH3(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
|
||||||
else
|
else
|
||||||
[alphahat,epsilonhat,etahat,ahat,aK] = ...
|
[alphahat,epsilonhat,etahat,ahat,aK] = ...
|
||||||
DiffuseKalmanSmootherH3corr(T,R,Q,H,Pinf,Pstar,Y,trend, ...
|
DiffuseKalmanSmootherH3corr(T,R,Q,H,Pinf,Pstar,Y,trend, ...
|
||||||
nobs,np,smpl,mf);
|
nobs,np,smpl,mf);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
elseif options_.kalman_algo == 2
|
elseif options_.kalman_algo == 2
|
||||||
if ~estim_params_.ncn
|
if ~estim_params_.ncn
|
||||||
[alphahat,epsilonhat,etahat,ahat,aK] = ...
|
[alphahat,epsilonhat,etahat,ahat,aK] = ...
|
||||||
DiffuseKalmanSmootherH3(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
|
DiffuseKalmanSmootherH3(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
|
||||||
else
|
else
|
||||||
[alphahat,epsilonhat,etahat,ahat,aK] = ...
|
[alphahat,epsilonhat,etahat,ahat,aK] = ...
|
||||||
DiffuseKalmanSmootherH3corr(T,R,Q,H,Pinf,Pstar,Y,trend, ...
|
DiffuseKalmanSmootherH3corr(T,R,Q,H,Pinf,Pstar,Y,trend, ...
|
||||||
nobs,np,smpl,mf);
|
nobs,np,smpl,mf);
|
||||||
end
|
end
|
||||||
elseif kalman_algo == 3 | kalman_algo == 4
|
elseif kalman_algo == 3 | kalman_algo == 4
|
||||||
data1 = Y - trend;
|
data1 = Y - trend;
|
||||||
if kalman_algo == 3
|
if kalman_algo == 3
|
||||||
[alphahat,epsilonhat,etahat,ahat,P,aK,PK,d,decomp] = ...
|
[alphahat,epsilonhat,etahat,ahat,P,aK,PK,d,decomp] = ...
|
||||||
DiffuseKalmanSmootherH1_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1,nobs,np,smpl);
|
DiffuseKalmanSmootherH1_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1,nobs,np,smpl);
|
||||||
if all(alphahat(:)==0)
|
if all(alphahat(:)==0)
|
||||||
kalman_algo = 4;
|
kalman_algo = 4;
|
||||||
if ~estim_params_.ncn
|
if ~estim_params_.ncn
|
||||||
[alphahat,epsilonhat,etahat,ahat,P,aK,PK,d,decomp] = ...
|
[alphahat,epsilonhat,etahat,ahat,P,aK,PK,d,decomp] = ...
|
||||||
DiffuseKalmanSmootherH3_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1,nobs,np,smpl);
|
DiffuseKalmanSmootherH3_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1,nobs,np,smpl);
|
||||||
else
|
else
|
||||||
[alphahat,epsilonhat,etahat,ahat,P,aK,PK,d,decomp] = ...
|
[alphahat,epsilonhat,etahat,ahat,P,aK,PK,d,decomp] = ...
|
||||||
DiffuseKalmanSmootherH3corr_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1, ...
|
DiffuseKalmanSmootherH3corr_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1, ...
|
||||||
nobs,np,smpl);
|
nobs,np,smpl);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
if ~estim_params_.ncn
|
if ~estim_params_.ncn
|
||||||
[alphahat,epsilonhat,etahat,ahat,P,aK,PK,d,decomp] = ...
|
[alphahat,epsilonhat,etahat,ahat,P,aK,PK,d,decomp] = ...
|
||||||
DiffuseKalmanSmootherH3_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1, ...
|
DiffuseKalmanSmootherH3_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1, ...
|
||||||
nobs,np,smpl);
|
nobs,np,smpl);
|
||||||
else
|
else
|
||||||
[alphahat,epsilonhat,etahat,ahat,P,aK,PK,d,decomp] = ...
|
[alphahat,epsilonhat,etahat,ahat,P,aK,PK,d,decomp] = ...
|
||||||
DiffuseKalmanSmootherH3corr_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1, ...
|
DiffuseKalmanSmootherH3corr_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1, ...
|
||||||
nobs,np,smpl);
|
nobs,np,smpl);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
alphahat = QT*alphahat;
|
alphahat = QT*alphahat;
|
||||||
ahat = QT*ahat;
|
ahat = QT*ahat;
|
||||||
nk = options_.nk;
|
nk = options_.nk;
|
||||||
for jnk=1:nk
|
for jnk=1:nk
|
||||||
aK(jnk,:,:) = QT*squeeze(aK(jnk,:,:));
|
aK(jnk,:,:) = QT*squeeze(aK(jnk,:,:));
|
||||||
for i=1:size(PK,4)
|
for i=1:size(PK,4)
|
||||||
PK(jnk,:,:,i) = QT*squeeze(PK(jnk,:,:,i))*QT';
|
PK(jnk,:,:,i) = QT*squeeze(PK(jnk,:,:,i))*QT';
|
||||||
end
|
end
|
||||||
for i=1:size(decomp,4)
|
for i=1:size(decomp,4)
|
||||||
decomp(jnk,:,:,i) = QT*squeeze(decomp(jnk,:,:,i));
|
decomp(jnk,:,:,i) = QT*squeeze(decomp(jnk,:,:,i));
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
for i=1:size(P,4)
|
for i=1:size(P,4)
|
||||||
P(:,:,i) = QT*squeeze(P(:,:,i))*QT';
|
P(:,:,i) = QT*squeeze(P(:,:,i))*QT';
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
if kalman_algo == 1
|
if kalman_algo == 1
|
||||||
if missing_value
|
if missing_value
|
||||||
[alphahat,etahat,ahat,aK] = missing_DiffuseKalmanSmoother1(T,R,Q, ...
|
[alphahat,etahat,ahat,aK] = missing_DiffuseKalmanSmoother1(T,R,Q, ...
|
||||||
Pinf,Pstar,Y,trend,nobs,np,smpl,mf,data_index);
|
Pinf,Pstar,Y,trend,nobs,np,smpl,mf,data_index);
|
||||||
else
|
else
|
||||||
[alphahat,etahat,ahat,aK] = DiffuseKalmanSmoother1(T,R,Q, ...
|
[alphahat,etahat,ahat,aK] = DiffuseKalmanSmoother1(T,R,Q, ...
|
||||||
Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
|
Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
|
||||||
end
|
end
|
||||||
if all(alphahat(:)==0)
|
if all(alphahat(:)==0)
|
||||||
kalman_algo = 2;
|
kalman_algo = 2;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if kalman_algo == 2
|
if kalman_algo == 2
|
||||||
if missing_value
|
if missing_value
|
||||||
[alphahat,etahat,ahat,P,aK,PK,d,decomp] = missing_DiffuseKalmanSmoother3(T,R,Q, ...
|
[alphahat,etahat,ahat,P,aK,PK,d,decomp] = missing_DiffuseKalmanSmoother3(T,R,Q, ...
|
||||||
Pinf,Pstar,Y,trend,nobs,np,smpl,mf,data_index);
|
Pinf,Pstar,Y,trend,nobs,np,smpl,mf,data_index);
|
||||||
else
|
else
|
||||||
[alphahat,etahat,ahat,P,aK,PK,d,decomp] = DiffuseKalmanSmoother3(T,R,Q, ...
|
[alphahat,etahat,ahat,P,aK,PK,d,decomp] = DiffuseKalmanSmoother3(T,R,Q, ...
|
||||||
Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
|
Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if kalman_algo == 3
|
if kalman_algo == 3
|
||||||
data1 = Y - trend;
|
data1 = Y - trend;
|
||||||
if missing_value
|
if missing_value
|
||||||
[alphahat,etahat,ahat,P,aK,PK,d,decomp] = missing_DiffuseKalmanSmoother1_Z(ST, ...
|
[alphahat,etahat,ahat,P,aK,PK,d,decomp] = missing_DiffuseKalmanSmoother1_Z(ST, ...
|
||||||
Z,R1,Q,Pinf,Pstar,data1,nobs,np,smpl,data_index);
|
Z,R1,Q,Pinf,Pstar,data1,nobs,np,smpl,data_index);
|
||||||
else
|
else
|
||||||
[alphahat,etahat,ahat,P,aK,PK,d,decomp] = DiffuseKalmanSmoother1_Z(ST, ...
|
[alphahat,etahat,ahat,P,aK,PK,d,decomp] = DiffuseKalmanSmoother1_Z(ST, ...
|
||||||
Z,R1,Q,Pinf,Pstar, ...
|
Z,R1,Q,Pinf,Pstar, ...
|
||||||
data1,nobs,np,smpl);
|
data1,nobs,np,smpl);
|
||||||
end
|
end
|
||||||
if all(alphahat(:)==0)
|
if all(alphahat(:)==0)
|
||||||
options_.kalman_algo = 4;
|
options_.kalman_algo = 4;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if kalman_algo == 4
|
if kalman_algo == 4
|
||||||
data1 = Y - trend;
|
data1 = Y - trend;
|
||||||
if missing_value
|
if missing_value
|
||||||
[alphahat,etahat,ahat,P,aK,PK,d,decomp] = missing_DiffuseKalmanSmoother3_Z(ST, ...
|
[alphahat,etahat,ahat,P,aK,PK,d,decomp] = missing_DiffuseKalmanSmoother3_Z(ST, ...
|
||||||
Z,R1,Q,Pinf,Pstar,data1,nobs,np,smpl,data_index);
|
Z,R1,Q,Pinf,Pstar,data1,nobs,np,smpl,data_index);
|
||||||
else
|
else
|
||||||
[alphahat,etahat,ahat,P,aK,PK,d,decomp] = DiffuseKalmanSmoother3_Z(ST, ...
|
[alphahat,etahat,ahat,P,aK,PK,d,decomp] = DiffuseKalmanSmoother3_Z(ST, ...
|
||||||
Z,R1,Q,Pinf,Pstar, ...
|
Z,R1,Q,Pinf,Pstar, ...
|
||||||
data1,nobs,np,smpl);
|
data1,nobs,np,smpl);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if kalman_algo == 3 | kalman_algo == 4
|
if kalman_algo == 3 | kalman_algo == 4
|
||||||
alphahat = QT*alphahat;
|
alphahat = QT*alphahat;
|
||||||
ahat = QT*ahat;
|
ahat = QT*ahat;
|
||||||
nk = options_.nk;
|
nk = options_.nk;
|
||||||
% $$$ if M_.exo_nbr<2 % Fix the crash of Dynare when the estimated model has only one structural shock (problem with
|
% $$$ if M_.exo_nbr<2 % Fix the crash of Dynare when the estimated model has only one structural shock (problem with
|
||||||
% $$$ % the squeeze function, that does not affect 2D arrays).
|
% $$$ % the squeeze function, that does not affect 2D arrays).
|
||||||
% $$$ size_decomp = 0;
|
% $$$ size_decomp = 0;
|
||||||
% $$$ else
|
% $$$ else
|
||||||
% $$$ size_decomp = size(decomp,4);
|
% $$$ size_decomp = size(decomp,4);
|
||||||
% $$$ end
|
% $$$ end
|
||||||
for jnk=1:nk
|
for jnk=1:nk
|
||||||
aK(jnk,:,:) = QT*squeeze(aK(jnk,:,:));
|
aK(jnk,:,:) = QT*squeeze(aK(jnk,:,:));
|
||||||
for i=1:size(PK,4)
|
for i=1:size(PK,4)
|
||||||
PK(jnk,:,:,i) = QT*dynare_squeeze(PK(jnk,:,:,i))*QT';
|
PK(jnk,:,:,i) = QT*dynare_squeeze(PK(jnk,:,:,i))*QT';
|
||||||
end
|
end
|
||||||
for i=1:size(decomp,4)
|
for i=1:size(decomp,4)
|
||||||
decomp(jnk,:,:,i) = QT*dynare_squeeze(decomp(jnk,:,:,i));
|
decomp(jnk,:,:,i) = QT*dynare_squeeze(decomp(jnk,:,:,i));
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
for i=1:size(P,4)
|
for i=1:size(P,4)
|
||||||
P(:,:,i) = QT*dynare_squeeze(P(:,:,i))*QT';
|
P(:,:,i) = QT*dynare_squeeze(P(:,:,i))*QT';
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
|
||||||
|
|
@ -146,15 +146,15 @@ TheoreticalAutoCovarianceOfTheObservedVariables = ...
|
||||||
zeros(NumberOfObservedVariables,NumberOfObservedVariables,NumberOfLags+1);
|
zeros(NumberOfObservedVariables,NumberOfObservedVariables,NumberOfLags+1);
|
||||||
TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1) = tmp0(mf,mf)+constant'*constant;
|
TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1) = tmp0(mf,mf)+constant'*constant;
|
||||||
for lag = 1:NumberOfLags
|
for lag = 1:NumberOfLags
|
||||||
tmp0 = T*tmp0;
|
tmp0 = T*tmp0;
|
||||||
TheoreticalAutoCovarianceOfTheObservedVariables(:,:,lag+1) = tmp0(mf,mf) ...
|
TheoreticalAutoCovarianceOfTheObservedVariables(:,:,lag+1) = tmp0(mf,mf) ...
|
||||||
+ constant'*constant;
|
+ constant'*constant;
|
||||||
end
|
end
|
||||||
% Build the theoretical "covariance" between Y and X
|
% Build the theoretical "covariance" between Y and X
|
||||||
GYX = zeros(NumberOfObservedVariables,NumberOfParameters);
|
GYX = zeros(NumberOfObservedVariables,NumberOfParameters);
|
||||||
for i=1:NumberOfLags
|
for i=1:NumberOfLags
|
||||||
GYX(:,(i-1)*NumberOfObservedVariables+1:i*NumberOfObservedVariables) = ...
|
GYX(:,(i-1)*NumberOfObservedVariables+1:i*NumberOfObservedVariables) = ...
|
||||||
TheoreticalAutoCovarianceOfTheObservedVariables(:,:,i+1);
|
TheoreticalAutoCovarianceOfTheObservedVariables(:,:,i+1);
|
||||||
end
|
end
|
||||||
if ~options_.noconstant
|
if ~options_.noconstant
|
||||||
GYX(:,end) = constant';
|
GYX(:,end) = constant';
|
||||||
|
|
@ -181,41 +181,41 @@ assignin('base','GXX',GXX);
|
||||||
assignin('base','GYX',GYX);
|
assignin('base','GYX',GYX);
|
||||||
|
|
||||||
if ~isinf(dsge_prior_weight)
|
if ~isinf(dsge_prior_weight)
|
||||||
tmp0 = dsge_prior_weight*gend*TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1) + mYY ;
|
tmp0 = dsge_prior_weight*gend*TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1) + mYY ;
|
||||||
tmp1 = dsge_prior_weight*gend*GYX + mYX;
|
tmp1 = dsge_prior_weight*gend*GYX + mYX;
|
||||||
tmp2 = inv(dsge_prior_weight*gend*GXX+mXX);
|
tmp2 = inv(dsge_prior_weight*gend*GXX+mXX);
|
||||||
SIGMAu = tmp0 - tmp1*tmp2*tmp1'; clear('tmp0');
|
SIGMAu = tmp0 - tmp1*tmp2*tmp1'; clear('tmp0');
|
||||||
if ~ispd(SIGMAu)
|
if ~ispd(SIGMAu)
|
||||||
v = diag(SIGMAu);
|
v = diag(SIGMAu);
|
||||||
k = find(v<0);
|
k = find(v<0);
|
||||||
fval = bayestopt_.penalty + sum(v(k).^2);
|
fval = bayestopt_.penalty + sum(v(k).^2);
|
||||||
info = 52;
|
info = 52;
|
||||||
cost_flag = 0;
|
cost_flag = 0;
|
||||||
return;
|
return;
|
||||||
end
|
end
|
||||||
SIGMAu = SIGMAu / (gend*(1+dsge_prior_weight));
|
SIGMAu = SIGMAu / (gend*(1+dsge_prior_weight));
|
||||||
PHI = tmp2*tmp1'; clear('tmp1');
|
PHI = tmp2*tmp1'; clear('tmp1');
|
||||||
prodlng1 = sum(gammaln(.5*((1+dsge_prior_weight)*gend- ...
|
prodlng1 = sum(gammaln(.5*((1+dsge_prior_weight)*gend- ...
|
||||||
NumberOfObservedVariables*NumberOfLags ...
|
NumberOfObservedVariables*NumberOfLags ...
|
||||||
+1-(1:NumberOfObservedVariables)')));
|
+1-(1:NumberOfObservedVariables)')));
|
||||||
prodlng2 = sum(gammaln(.5*(dsge_prior_weight*gend- ...
|
prodlng2 = sum(gammaln(.5*(dsge_prior_weight*gend- ...
|
||||||
NumberOfObservedVariables*NumberOfLags ...
|
NumberOfObservedVariables*NumberOfLags ...
|
||||||
+1-(1:NumberOfObservedVariables)')));
|
+1-(1:NumberOfObservedVariables)')));
|
||||||
lik = .5*NumberOfObservedVariables*log(det(dsge_prior_weight*gend*GXX+mXX)) ...
|
lik = .5*NumberOfObservedVariables*log(det(dsge_prior_weight*gend*GXX+mXX)) ...
|
||||||
+ .5*((dsge_prior_weight+1)*gend-NumberOfParameters)*log(det((dsge_prior_weight+1)*gend*SIGMAu)) ...
|
+ .5*((dsge_prior_weight+1)*gend-NumberOfParameters)*log(det((dsge_prior_weight+1)*gend*SIGMAu)) ...
|
||||||
- .5*NumberOfObservedVariables*log(det(dsge_prior_weight*gend*GXX)) ...
|
- .5*NumberOfObservedVariables*log(det(dsge_prior_weight*gend*GXX)) ...
|
||||||
- .5*(dsge_prior_weight*gend-NumberOfParameters)*log(det(dsge_prior_weight*gend*(GYY-GYX*inv(GXX)*GYX'))) ...
|
- .5*(dsge_prior_weight*gend-NumberOfParameters)*log(det(dsge_prior_weight*gend*(GYY-GYX*inv(GXX)*GYX'))) ...
|
||||||
+ .5*NumberOfObservedVariables*gend*log(2*pi) ...
|
+ .5*NumberOfObservedVariables*gend*log(2*pi) ...
|
||||||
- .5*log(2)*NumberOfObservedVariables*((dsge_prior_weight+1)*gend-NumberOfParameters) ...
|
- .5*log(2)*NumberOfObservedVariables*((dsge_prior_weight+1)*gend-NumberOfParameters) ...
|
||||||
+ .5*log(2)*NumberOfObservedVariables*(dsge_prior_weight*gend-NumberOfParameters) ...
|
+ .5*log(2)*NumberOfObservedVariables*(dsge_prior_weight*gend-NumberOfParameters) ...
|
||||||
- prodlng1 + prodlng2;
|
- prodlng1 + prodlng2;
|
||||||
else
|
else
|
||||||
iGXX = inv(GXX);
|
iGXX = inv(GXX);
|
||||||
SIGMAu = GYY - GYX*iGXX*transpose(GYX);
|
SIGMAu = GYY - GYX*iGXX*transpose(GYX);
|
||||||
PHI = iGXX*transpose(GYX);
|
PHI = iGXX*transpose(GYX);
|
||||||
lik = gend * ( log(det(SIGMAu)) + NumberOfObservedVariables*log(2*pi) + ...
|
lik = gend * ( log(det(SIGMAu)) + NumberOfObservedVariables*log(2*pi) + ...
|
||||||
trace(inv(SIGMAu)*(mYY - transpose(mYX*PHI) - mYX*PHI + transpose(PHI)*mXX*PHI)/gend));
|
trace(inv(SIGMAu)*(mYY - transpose(mYX*PHI) - mYX*PHI + transpose(PHI)*mXX*PHI)/gend));
|
||||||
lik = .5*lik;% Minus likelihood
|
lik = .5*lik;% Minus likelihood
|
||||||
end
|
end
|
||||||
|
|
||||||
lnprior = priordens(xparam1,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
|
lnprior = priordens(xparam1,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
|
||||||
|
|
|
||||||
|
|
@ -1,91 +1,91 @@
|
||||||
function Draws = GetAllPosteriorDraws(column, FirstMhFile, FirstLine, TotalNumberOfMhFile, NumberOfDraws, blck)
|
function Draws = GetAllPosteriorDraws(column, FirstMhFile, FirstLine, TotalNumberOfMhFile, NumberOfDraws, blck)
|
||||||
|
|
||||||
% function Draws = GetAllPosteriorDraws(column,FirstMhFile,FirstLine,TotalNumberOfMhFile,NumberOfDraws)
|
% function Draws = GetAllPosteriorDraws(column,FirstMhFile,FirstLine,TotalNumberOfMhFile,NumberOfDraws)
|
||||||
% Gets all posterior draws
|
% Gets all posterior draws
|
||||||
%
|
%
|
||||||
% INPUTS
|
% INPUTS
|
||||||
% column: column
|
% column: column
|
||||||
% FirstMhFile: first mh file
|
% FirstMhFile: first mh file
|
||||||
% FirstLine: first line
|
% FirstLine: first line
|
||||||
% TotalNumberOfMhFile: total number of mh file
|
% TotalNumberOfMhFile: total number of mh file
|
||||||
% NumberOfDraws: number of draws
|
% NumberOfDraws: number of draws
|
||||||
|
|
||||||
% OUTPUTS
|
% OUTPUTS
|
||||||
% Draws: draws from posterior distribution
|
% Draws: draws from posterior distribution
|
||||||
%
|
%
|
||||||
% SPECIAL REQUIREMENTS
|
% SPECIAL REQUIREMENTS
|
||||||
% none
|
% none
|
||||||
|
|
||||||
% Copyright (C) 2005-2008 Dynare Team
|
% Copyright (C) 2005-2008 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
% Dynare is free software: you can redistribute it and/or modify
|
||||||
% it under the terms of the GNU General Public License as published by
|
% it under the terms of the GNU General Public License as published by
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
% the Free Software Foundation, either version 3 of the License, or
|
||||||
% (at your option) any later version.
|
% (at your option) any later version.
|
||||||
%
|
%
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
% Dynare is distributed in the hope that it will be useful,
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
global M_ options_
|
global M_ options_
|
||||||
|
|
||||||
nblck = options_.mh_nblck;
|
nblck = options_.mh_nblck;
|
||||||
|
|
||||||
iline = FirstLine;
|
iline = FirstLine;
|
||||||
linee = 1;
|
linee = 1;
|
||||||
DirectoryName = CheckPath('metropolis');
|
DirectoryName = CheckPath('metropolis');
|
||||||
|
|
||||||
if nblck>1 && nargin<6
|
if nblck>1 && nargin<6
|
||||||
Draws = zeros(NumberOfDraws*nblck,1);
|
Draws = zeros(NumberOfDraws*nblck,1);
|
||||||
iline0=iline;
|
iline0=iline;
|
||||||
if column>0
|
if column>0
|
||||||
for blck = 1:nblck
|
for blck = 1:nblck
|
||||||
iline=iline0;
|
iline=iline0;
|
||||||
for file = FirstMhFile:TotalNumberOfMhFile
|
for file = FirstMhFile:TotalNumberOfMhFile
|
||||||
load([DirectoryName '/' M_.fname '_mh' int2str(file) '_blck' int2str(blck)],'x2')
|
load([DirectoryName '/' M_.fname '_mh' int2str(file) '_blck' int2str(blck)],'x2')
|
||||||
NumberOfLines = size(x2(iline:end,:),1);
|
NumberOfLines = size(x2(iline:end,:),1);
|
||||||
Draws(linee:linee+NumberOfLines-1) = x2(iline:end,column);
|
Draws(linee:linee+NumberOfLines-1) = x2(iline:end,column);
|
||||||
linee = linee+NumberOfLines;
|
linee = linee+NumberOfLines;
|
||||||
iline = 1;
|
iline = 1;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
for blck = 1:nblck
|
for blck = 1:nblck
|
||||||
iline=iline0;
|
iline=iline0;
|
||||||
for file = FirstMhFile:TotalNumberOfMhFile
|
for file = FirstMhFile:TotalNumberOfMhFile
|
||||||
load([DirectoryName '/' M_.fname '_mh' int2str(file) '_blck' int2str(blck)],'logpo2')
|
load([DirectoryName '/' M_.fname '_mh' int2str(file) '_blck' int2str(blck)],'logpo2')
|
||||||
NumberOfLines = size(logpo2(iline:end),1);
|
NumberOfLines = size(logpo2(iline:end),1);
|
||||||
Draws(linee:linee+NumberOfLines-1) = logpo2(iline:end);
|
Draws(linee:linee+NumberOfLines-1) = logpo2(iline:end);
|
||||||
linee = linee+NumberOfLines;
|
linee = linee+NumberOfLines;
|
||||||
iline = 1;
|
iline = 1;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
if nblck==1
|
if nblck==1
|
||||||
blck=1;
|
blck=1;
|
||||||
end
|
end
|
||||||
if column>0
|
if column>0
|
||||||
for file = FirstMhFile:TotalNumberOfMhFile
|
for file = FirstMhFile:TotalNumberOfMhFile
|
||||||
load([DirectoryName '/' M_.fname '_mh' int2str(file) '_blck' int2str(blck)],'x2')
|
load([DirectoryName '/' M_.fname '_mh' int2str(file) '_blck' int2str(blck)],'x2')
|
||||||
NumberOfLines = size(x2(iline:end,:),1);
|
NumberOfLines = size(x2(iline:end,:),1);
|
||||||
Draws(linee:linee+NumberOfLines-1) = x2(iline:end,column);
|
Draws(linee:linee+NumberOfLines-1) = x2(iline:end,column);
|
||||||
linee = linee+NumberOfLines;
|
linee = linee+NumberOfLines;
|
||||||
iline = 1;
|
iline = 1;
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
for file = FirstMhFile:TotalNumberOfMhFile
|
for file = FirstMhFile:TotalNumberOfMhFile
|
||||||
load([DirectoryName '/' M_.fname '_mh' int2str(file) '_blck' int2str(blck)],'logpo2')
|
load([DirectoryName '/' M_.fname '_mh' int2str(file) '_blck' int2str(blck)],'logpo2')
|
||||||
NumberOfLines = size(logpo2(iline:end,:),1);
|
NumberOfLines = size(logpo2(iline:end,:),1);
|
||||||
Draws(linee:linee+NumberOfLines-1) = logpo2(iline:end);
|
Draws(linee:linee+NumberOfLines-1) = logpo2(iline:end);
|
||||||
linee = linee+NumberOfLines;
|
linee = linee+NumberOfLines;
|
||||||
iline = 1;
|
iline = 1;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
@ -1,39 +1,39 @@
|
||||||
function [xparams, logpost] = GetOneDraw(type)
|
function [xparams, logpost] = GetOneDraw(type)
|
||||||
|
|
||||||
% function [xparams, logpost] = GetOneDraw(type)
|
% function [xparams, logpost] = GetOneDraw(type)
|
||||||
% draws one row from metropolis
|
% draws one row from metropolis
|
||||||
%
|
%
|
||||||
% INPUTS
|
% INPUTS
|
||||||
% type: posterior
|
% type: posterior
|
||||||
% prior
|
% prior
|
||||||
%
|
%
|
||||||
% OUTPUTS
|
% OUTPUTS
|
||||||
% xparams: vector of estimated parameters (drawn from posterior distribution)
|
% xparams: vector of estimated parameters (drawn from posterior distribution)
|
||||||
% logpost: log of the posterior density relative to this row
|
% logpost: log of the posterior density relative to this row
|
||||||
%
|
%
|
||||||
% SPECIAL REQUIREMENTS
|
% SPECIAL REQUIREMENTS
|
||||||
% none
|
% none
|
||||||
|
|
||||||
% Copyright (C) 2005-2008 Dynare Team
|
% Copyright (C) 2005-2008 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
% Dynare is free software: you can redistribute it and/or modify
|
||||||
% it under the terms of the GNU General Public License as published by
|
% it under the terms of the GNU General Public License as published by
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
% the Free Software Foundation, either version 3 of the License, or
|
||||||
% (at your option) any later version.
|
% (at your option) any later version.
|
||||||
%
|
%
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
% Dynare is distributed in the hope that it will be useful,
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
switch type
|
switch type
|
||||||
case 'posterior'
|
case 'posterior'
|
||||||
[xparams, logpost] = metropolis_draw(0);
|
[xparams, logpost] = metropolis_draw(0);
|
||||||
case 'prior'
|
case 'prior'
|
||||||
xparams = prior_draw(0);
|
xparams = prior_draw(0);
|
||||||
end
|
end
|
||||||
|
|
@ -307,57 +307,57 @@ end
|
||||||
%% subfunctions:
|
%% subfunctions:
|
||||||
%
|
%
|
||||||
function fid = TeXBegin(directory,fname,fnum,title)
|
function fid = TeXBegin(directory,fname,fnum,title)
|
||||||
TeXfile = [directory '/' fname '_Posterior_Mean_' int2str(fnum) '.TeX'];
|
TeXfile = [directory '/' fname '_Posterior_Mean_' int2str(fnum) '.TeX'];
|
||||||
fidTeX = fopen(TeXfile,'w');
|
fidTeX = fopen(TeXfile,'w');
|
||||||
fprintf(fidTeX,'%% TeX-table generated by Dynare.\n');
|
fprintf(fidTeX,'%% TeX-table generated by Dynare.\n');
|
||||||
fprintf(fidTeX,['%% RESULTS FROM METROPOLIS HASTINGS (' title ')\n']);
|
fprintf(fidTeX,['%% RESULTS FROM METROPOLIS HASTINGS (' title ')\n']);
|
||||||
fprintf(fidTeX,['%% ' datestr(now,0)]);
|
fprintf(fidTeX,['%% ' datestr(now,0)]);
|
||||||
fprintf(fidTeX,' \n');
|
fprintf(fidTeX,' \n');
|
||||||
fprintf(fidTeX,' \n');
|
fprintf(fidTeX,' \n');
|
||||||
fprintf(fidTeX,'\\begin{table}\n');
|
fprintf(fidTeX,'\\begin{table}\n');
|
||||||
fprintf(fidTeX,'\\centering\n');
|
fprintf(fidTeX,'\\centering\n');
|
||||||
fprintf(fidTeX,'\\begin{tabular}{l|lcccccc} \n');
|
fprintf(fidTeX,'\\begin{tabular}{l|lcccccc} \n');
|
||||||
fprintf(fidTeX,'\\hline\\hline \\\\ \n');
|
fprintf(fidTeX,'\\hline\\hline \\\\ \n');
|
||||||
fprintf(fidTeX,[' & Prior distribution & Prior mean & Prior ' ...
|
fprintf(fidTeX,[' & Prior distribution & Prior mean & Prior ' ...
|
||||||
's.d. & Posterior mean & Posterior s.d. & HPD inf & HPD sup\\\\ \n']);
|
's.d. & Posterior mean & Posterior s.d. & HPD inf & HPD sup\\\\ \n']);
|
||||||
fprintf(fidTeX,'\\hline \\\\ \n');
|
fprintf(fidTeX,'\\hline \\\\ \n');
|
||||||
fid = fidTeX;
|
fid = fidTeX;
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
function TeXCore(fid,name,shape,priormean,priorstd,postmean,poststd,hpd)
|
function TeXCore(fid,name,shape,priormean,priorstd,postmean,poststd,hpd)
|
||||||
fprintf(fid,['$%s$ & %s & %7.3f & %6.4f & %7.3f& %6.4f & %7.4f & %7.4f \\\\ \n'],...
|
fprintf(fid,['$%s$ & %s & %7.3f & %6.4f & %7.3f& %6.4f & %7.4f & %7.4f \\\\ \n'],...
|
||||||
name,...
|
name,...
|
||||||
shape,...
|
shape,...
|
||||||
priormean,...
|
priormean,...
|
||||||
priorstd,...
|
priorstd,...
|
||||||
postmean,...
|
postmean,...
|
||||||
poststd,...
|
poststd,...
|
||||||
hpd(1),...
|
hpd(1),...
|
||||||
hpd(2));
|
hpd(2));
|
||||||
|
|
||||||
|
|
||||||
function TeXEnd(fid,fnum,title)
|
function TeXEnd(fid,fnum,title)
|
||||||
fprintf(fid,'\\hline\\hline \n');
|
fprintf(fid,'\\hline\\hline \n');
|
||||||
fprintf(fid,'\\end{tabular}\n ');
|
fprintf(fid,'\\end{tabular}\n ');
|
||||||
fprintf(fid,['\\caption{Results from Metropolis-Hastings (' title ')}\n ']);
|
fprintf(fid,['\\caption{Results from Metropolis-Hastings (' title ')}\n ']);
|
||||||
fprintf(fid,['\\label{Table:MHPosterior:' int2str(fnum) '}\n']);
|
fprintf(fid,['\\label{Table:MHPosterior:' int2str(fnum) '}\n']);
|
||||||
fprintf(fid,'\\end{table}\n');
|
fprintf(fid,'\\end{table}\n');
|
||||||
fprintf(fid,'%% End of TeX file.\n');
|
fprintf(fid,'%% End of TeX file.\n');
|
||||||
fclose(fid);
|
fclose(fid);
|
||||||
|
|
||||||
|
|
||||||
function oo = Filloo(oo,name,type,postmean,hpdinterval,postmedian,postvar,postdecile,density)
|
function oo = Filloo(oo,name,type,postmean,hpdinterval,postmedian,postvar,postdecile,density)
|
||||||
eval(['oo.posterior_mean.' type '.' name ' = postmean;']);
|
eval(['oo.posterior_mean.' type '.' name ' = postmean;']);
|
||||||
eval(['oo.posterior_hpdinf.' type '.' name ' = hpdinterval(1);']);
|
eval(['oo.posterior_hpdinf.' type '.' name ' = hpdinterval(1);']);
|
||||||
eval(['oo.posterior_hpdsup.' type '.' name ' = hpdinterval(2);']);
|
eval(['oo.posterior_hpdsup.' type '.' name ' = hpdinterval(2);']);
|
||||||
eval(['oo.posterior_median.' type '.' name ' = postmedian;']);
|
eval(['oo.posterior_median.' type '.' name ' = postmedian;']);
|
||||||
eval(['oo.posterior_variance.' type '.' name ' = postvar;']);
|
eval(['oo.posterior_variance.' type '.' name ' = postvar;']);
|
||||||
eval(['oo.posterior_deciles.' type '.' name ' = postdecile;']);
|
eval(['oo.posterior_deciles.' type '.' name ' = postdecile;']);
|
||||||
eval(['oo.posterior_density.' type '.' name ' = density;']);
|
eval(['oo.posterior_density.' type '.' name ' = density;']);
|
||||||
|
|
||||||
function [post_mean,hpd_interval,post_var] = Extractoo(oo,name,type)
|
function [post_mean,hpd_interval,post_var] = Extractoo(oo,name,type)
|
||||||
hpd_interval = zeros(2,1);
|
hpd_interval = zeros(2,1);
|
||||||
eval(['post_mean = oo.posterior_mean.' type '.' name ';']);
|
eval(['post_mean = oo.posterior_mean.' type '.' name ';']);
|
||||||
eval(['hpd_interval(1) = oo.posterior_hpdinf.' type '.' name ';']);
|
eval(['hpd_interval(1) = oo.posterior_hpdinf.' type '.' name ';']);
|
||||||
eval(['hpd_interval(2) = oo.posterior_hpdsup.' type '.' name ';']);
|
eval(['hpd_interval(2) = oo.posterior_hpdsup.' type '.' name ';']);
|
||||||
eval(['post_var = oo.posterior_variance.' type '.' name ';']);
|
eval(['post_var = oo.posterior_variance.' type '.' name ';']);
|
||||||
|
|
@ -1,169 +1,169 @@
|
||||||
function MakeAllFigures(NumberOfPlots,Caption,FigureProperties,Info)
|
function MakeAllFigures(NumberOfPlots,Caption,FigureProperties,Info)
|
||||||
|
|
||||||
% Copyright (C) 2005 Dynare Team
|
% Copyright (C) 2005 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
% Dynare is free software: you can redistribute it and/or modify
|
||||||
% it under the terms of the GNU General Public License as published by
|
% it under the terms of the GNU General Public License as published by
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
% the Free Software Foundation, either version 3 of the License, or
|
||||||
% (at your option) any later version.
|
% (at your option) any later version.
|
||||||
%
|
%
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
% Dynare is distributed in the hope that it will be useful,
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
global M_ options_
|
global M_ options_
|
||||||
|
|
||||||
FigHandle = figure('Name',FigureProperties.Name);
|
FigHandle = figure('Name',FigureProperties.Name);
|
||||||
|
|
||||||
NAMES = cell(NumberOfPlots,1);
|
NAMES = cell(NumberOfPlots,1);
|
||||||
if options_.TeX
|
if options_.TeX
|
||||||
TeXNAMES = cell(NumberOfPlots,1);
|
TeXNAMES = cell(NumberOfPlots,1);
|
||||||
end
|
end
|
||||||
|
|
||||||
if NumberOfPlots == 9
|
if NumberOfPlots == 9
|
||||||
nr = 3;
|
nr = 3;
|
||||||
nc = 3;
|
nc = 3;
|
||||||
elseif NumberOfPlots == 8
|
elseif NumberOfPlots == 8
|
||||||
nr = 3;
|
nr = 3;
|
||||||
nc = 3;
|
nc = 3;
|
||||||
elseif NumberOfPlots == 7
|
elseif NumberOfPlots == 7
|
||||||
nr = 3;
|
nr = 3;
|
||||||
nc = 3;
|
nc = 3;
|
||||||
elseif NumberOfPlots == 6
|
elseif NumberOfPlots == 6
|
||||||
nr = 2;
|
nr = 2;
|
||||||
nc = 3;
|
nc = 3;
|
||||||
elseif NumberOfPlots == 5
|
elseif NumberOfPlots == 5
|
||||||
nr = 3;
|
nr = 3;
|
||||||
nc = 2;
|
nc = 2;
|
||||||
elseif NumberOfPlots == 4
|
elseif NumberOfPlots == 4
|
||||||
nr = 2;
|
nr = 2;
|
||||||
nc = 2;
|
nc = 2;
|
||||||
elseif NumberOfPlots == 3
|
elseif NumberOfPlots == 3
|
||||||
nr = 2;
|
nr = 2;
|
||||||
nc = 2;
|
nc = 2;
|
||||||
elseif NumberOfPlots == 2
|
elseif NumberOfPlots == 2
|
||||||
nr = 1;
|
nr = 1;
|
||||||
nc = 2;
|
nc = 2;
|
||||||
elseif NumberOfPlots == 1
|
elseif NumberOfPlots == 1
|
||||||
nr = 1;
|
nr = 1;
|
||||||
nc = 1;
|
nc = 1;
|
||||||
end
|
end
|
||||||
|
|
||||||
for plt = 1:NumberOfPlots
|
for plt = 1:NumberOfPlots
|
||||||
eval(['NumberOfCurves = Info.Box' int2str(plt) '.Number;'])
|
eval(['NumberOfCurves = Info.Box' int2str(plt) '.Number;'])
|
||||||
NumberOfObservations = zeros(2,1);
|
NumberOfObservations = zeros(2,1);
|
||||||
x = cell(NumberOfCurves,1);
|
x = cell(NumberOfCurves,1);
|
||||||
y = cell(NumberOfCurves,1);
|
y = cell(NumberOfCurves,1);
|
||||||
PltType = cell(NumberofCurves,1);
|
PltType = cell(NumberofCurves,1);
|
||||||
top = NaN(NumberOfCurves,1);
|
top = NaN(NumberOfCurves,1);
|
||||||
bottom = NaN(NumberOfCurves,1);
|
bottom = NaN(NumberOfCurves,1);
|
||||||
binf = NaN(NumberOfCurves,1);
|
binf = NaN(NumberOfCurves,1);
|
||||||
bsup = NaN(NumberOfCurves,1);
|
bsup = NaN(NumberOfCurves,1);
|
||||||
for curve = 1:NumberOfCurves
|
for curve = 1:NumberOfCurves
|
||||||
eval(['x{' curve '} = Info.Box' int2str(plt) '.Curve' int2str(curve) '.xdata;'])
|
eval(['x{' curve '} = Info.Box' int2str(plt) '.Curve' int2str(curve) '.xdata;'])
|
||||||
eval(['y{' curve '} = Info.Box' int2str(plt) '.Curve' int2str(curve) '.ydata;'])
|
eval(['y{' curve '} = Info.Box' int2str(plt) '.Curve' int2str(curve) '.ydata;'])
|
||||||
eval(['name = Info.Box' int2str(plt) '.Curve' int2str(curve) '.variablename;'])
|
eval(['name = Info.Box' int2str(plt) '.Curve' int2str(curve) '.variablename;'])
|
||||||
eval(['PltType{' curve '} = Info.Box' int2str(plt) '.Curve' int2str(curve) '.type']);
|
eval(['PltType{' curve '} = Info.Box' int2str(plt) '.Curve' int2str(curve) '.type']);
|
||||||
if length(x{curve})-length(y{curve})
|
if length(x{curve})-length(y{curve})
|
||||||
disp('MakeFigure :: The number of observations in x doesn''t match with ')
|
disp('MakeFigure :: The number of observations in x doesn''t match with ')
|
||||||
disp(['the number of observation in y for ' name ])
|
disp(['the number of observation in y for ' name ])
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
if Info.PlotProperties.CutTop
|
if Info.PlotProperties.CutTop
|
||||||
top(curve) = max(y{curve});
|
top(curve) = max(y{curve});
|
||||||
else Info.PlotProperties.CutBottom
|
else Info.PlotProperties.CutBottom
|
||||||
bottom(curve) = min(y{curve});
|
bottom(curve) = min(y{curve});
|
||||||
end
|
end
|
||||||
binf(curve) = min(x{curve});
|
binf(curve) = min(x{curve});
|
||||||
bsup(curve) = max(x{curve});
|
bsup(curve) = max(x{curve});
|
||||||
end
|
end
|
||||||
ymax = max(top);
|
ymax = max(top);
|
||||||
ymin = min(bottom);
|
ymin = min(bottom);
|
||||||
xmin = min(binf);
|
xmin = min(binf);
|
||||||
xmax = max(bsup);
|
xmax = max(bsup);
|
||||||
if isnan(ymin(plt))
|
if isnan(ymin(plt))
|
||||||
ymin = 0;
|
ymin = 0;
|
||||||
end
|
end
|
||||||
eval(['NAMES{' int2str(plt) '} = Info.Box' int2str(plt) '.name;'])
|
eval(['NAMES{' int2str(plt) '} = Info.Box' int2str(plt) '.name;'])
|
||||||
if options_.TeX
|
if options_.TeX
|
||||||
eval(['TeXNAMES{' int2str(plt) '} = Info.Box' int2str(plt) '.texname;'])
|
eval(['TeXNAMES{' int2str(plt) '} = Info.Box' int2str(plt) '.texname;'])
|
||||||
end
|
end
|
||||||
subplot(nr,nc,plt)
|
subplot(nr,nc,plt)
|
||||||
hold on
|
hold on
|
||||||
for curve = 1:NumberOfCurves
|
for curve = 1:NumberOfCurves
|
||||||
hh = plot(x{curve},y{curve});
|
hh = plot(x{curve},y{curve});
|
||||||
if strcmpi(PltType{curve},'PriorDensity')
|
if strcmpi(PltType{curve},'PriorDensity')
|
||||||
set(hh,'Color',[0.7 0.7 0.7],'LineStyle','-','LineWidth',2)
|
set(hh,'Color',[0.7 0.7 0.7],'LineStyle','-','LineWidth',2)
|
||||||
%
|
%
|
||||||
%
|
%
|
||||||
elseif strcmpi(PltType{curve},'DensityEstimate')
|
elseif strcmpi(PltType{curve},'DensityEstimate')
|
||||||
set(hh,'Color','k','LineStyle','-','LineWidth',2)
|
set(hh,'Color','k','LineStyle','-','LineWidth',2)
|
||||||
%
|
%
|
||||||
%
|
%
|
||||||
elseif strcmpi(PltType{curve},'ModeEstimate')
|
elseif strcmpi(PltType{curve},'ModeEstimate')
|
||||||
set(hh,'Color','g','LineStyle','--','LineWidth',2)
|
set(hh,'Color','g','LineStyle','--','LineWidth',2)
|
||||||
%
|
%
|
||||||
%
|
%
|
||||||
elseif strcmpi(PltType{curve},'SmoothVariable')
|
elseif strcmpi(PltType{curve},'SmoothVariable')
|
||||||
set(hh,'Color','k','LineStyle','-','LineWidth',2)
|
set(hh,'Color','k','LineStyle','-','LineWidth',2)
|
||||||
%
|
%
|
||||||
%
|
%
|
||||||
elseif strcmpi(PltType{curve},'Deciles')
|
elseif strcmpi(PltType{curve},'Deciles')
|
||||||
set(hh,'Color','g','LineStyle','-','LineWidth',1)
|
set(hh,'Color','g','LineStyle','-','LineWidth',1)
|
||||||
%
|
%
|
||||||
%
|
%
|
||||||
elseif strcmpi(PltType{curve},'Forecasts')
|
elseif strcmpi(PltType{curve},'Forecasts')
|
||||||
set(hh,'Color','','LineStyle','-','LineWidth',2)
|
set(hh,'Color','','LineStyle','-','LineWidth',2)
|
||||||
%
|
%
|
||||||
%
|
%
|
||||||
elseif strcmpi(PltType{curve},'ForecastsHPD')
|
elseif strcmpi(PltType{curve},'ForecastsHPD')
|
||||||
set(hh,'Color','k','LineStyle','-','LineWidth',1)
|
set(hh,'Color','k','LineStyle','-','LineWidth',1)
|
||||||
%
|
%
|
||||||
%
|
%
|
||||||
elseif strcmpi(PltType{curve},'ForecastsDeciles')
|
elseif strcmpi(PltType{curve},'ForecastsDeciles')
|
||||||
set(hh,'Color','g','LineStyle','-','LineWidth',1)
|
set(hh,'Color','g','LineStyle','-','LineWidth',1)
|
||||||
%
|
%
|
||||||
%
|
%
|
||||||
elseif strcmpi(PltType{curve},'DiagnosticWithin')
|
elseif strcmpi(PltType{curve},'DiagnosticWithin')
|
||||||
set(hh,'Color','b','LineStyle','-','LineWidth',2)
|
set(hh,'Color','b','LineStyle','-','LineWidth',2)
|
||||||
%
|
%
|
||||||
%
|
%
|
||||||
elseif strcmpi(PltType{curve},'DiagnosticPooled')
|
elseif strcmpi(PltType{curve},'DiagnosticPooled')
|
||||||
set(hh,'Color','r','LineStyle','-','LineWidth',2)
|
set(hh,'Color','r','LineStyle','-','LineWidth',2)
|
||||||
%
|
%
|
||||||
%
|
%
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
axis([xmin xmax ymin ymax])
|
axis([xmin xmax ymin ymax])
|
||||||
title(NAMES{plt})
|
title(NAMES{plt})
|
||||||
drawnow
|
drawnow
|
||||||
hold off
|
hold off
|
||||||
end
|
end
|
||||||
|
|
||||||
if Info.SaveFormat.Eps
|
if Info.SaveFormat.Eps
|
||||||
if isempty(Info.SaveFormat.Name)
|
if isempty(Info.SaveFormat.Name)
|
||||||
eval(['print -depsc2 ' M_.fname Info.SaveFormat.GenericName int2str(Info.SaveFormat.Number) '.eps']);
|
eval(['print -depsc2 ' M_.fname Info.SaveFormat.GenericName int2str(Info.SaveFormat.Number) '.eps']);
|
||||||
else
|
else
|
||||||
eval(['print -depsc2 ' M_.fname Info.SaveFormat.GenericName Info.SaveFormat.Name '.eps']);
|
eval(['print -depsc2 ' M_.fname Info.SaveFormat.GenericName Info.SaveFormat.Name '.eps']);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if Info.SaveFormat.Pdf && ~exist('OCTAVE_VERSION')
|
if Info.SaveFormat.Pdf && ~exist('OCTAVE_VERSION')
|
||||||
if isempty(Info.SaveFormat.Name)
|
if isempty(Info.SaveFormat.Name)
|
||||||
eval(['print -dpdf ' M_.fname Info.SaveFormat.GenericName int2str(Info.SaveFormat.Number)]);
|
eval(['print -dpdf ' M_.fname Info.SaveFormat.GenericName int2str(Info.SaveFormat.Number)]);
|
||||||
else
|
else
|
||||||
eval(['print -dpdf ' M_.fname Info.SaveFormat.GenericName Info.SaveFormat.Name]);
|
eval(['print -dpdf ' M_.fname Info.SaveFormat.GenericName Info.SaveFormat.Name]);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if Info.SaveFormat.Fig && ~exist('OCTAVE_VERSION')
|
if Info.SaveFormat.Fig && ~exist('OCTAVE_VERSION')
|
||||||
if isempty(Info.SaveFormat.Name)
|
if isempty(Info.SaveFormat.Name)
|
||||||
saveas(FigHandle,[M_.fname Info.SaveFormat.GenericName int2str(Info.SaveFormat.Number) '.fig']);
|
saveas(FigHandle,[M_.fname Info.SaveFormat.GenericName int2str(Info.SaveFormat.Number) '.fig']);
|
||||||
else
|
else
|
||||||
saveas(FigHandle,[M_.fname Info.SaveFormat.GenericName Info.SaveFormat.Name '.fig']);
|
saveas(FigHandle,[M_.fname Info.SaveFormat.GenericName Info.SaveFormat.Name '.fig']);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
@ -1,94 +1,94 @@
|
||||||
function myoutput = McMCDiagnostics_core(myinputs,fpar,npar,whoiam, ThisMatlab)
|
function myoutput = McMCDiagnostics_core(myinputs,fpar,npar,whoiam, ThisMatlab)
|
||||||
% Core functionality for MCMC Diagnostics, which can be parallelized
|
% Core functionality for MCMC Diagnostics, which can be parallelized
|
||||||
|
|
||||||
% Copyright (C) 2005-2009 Dynare Team
|
% Copyright (C) 2005-2009 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
% Dynare is free software: you can redistribute it and/or modify
|
||||||
% it under the terms of the GNU General Public License as published by
|
% it under the terms of the GNU General Public License as published by
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
% the Free Software Foundation, either version 3 of the License, or
|
||||||
% (at your option) any later version.
|
% (at your option) any later version.
|
||||||
%
|
%
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
% Dynare is distributed in the hope that it will be useful,
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
if nargin<4,
|
if nargin<4,
|
||||||
whoiam=0;
|
whoiam=0;
|
||||||
end
|
end
|
||||||
struct2local(myinputs);
|
struct2local(myinputs);
|
||||||
|
|
||||||
if ~exist('MhDirectoryName'),
|
if ~exist('MhDirectoryName'),
|
||||||
MhDirectoryName = CheckPath('metropolis');
|
MhDirectoryName = CheckPath('metropolis');
|
||||||
end
|
end
|
||||||
|
|
||||||
ALPHA = 0.2; % increase too much with the number of simulations.
|
ALPHA = 0.2; % increase too much with the number of simulations.
|
||||||
tmp = zeros(NumberOfDraws*nblck,3);
|
tmp = zeros(NumberOfDraws*nblck,3);
|
||||||
UDIAG = zeros(NumberOfLines,6,npar-fpar+1);
|
UDIAG = zeros(NumberOfLines,6,npar-fpar+1);
|
||||||
|
|
||||||
if whoiam
|
if whoiam
|
||||||
waitbarString = ['Please wait... McMCDiagnostics (' int2str(fpar) 'of' int2str(npar) ')...'];
|
waitbarString = ['Please wait... McMCDiagnostics (' int2str(fpar) 'of' int2str(npar) ')...'];
|
||||||
if Parallel(ThisMatlab).Local,
|
if Parallel(ThisMatlab).Local,
|
||||||
waitbarTitle=['Local '];
|
waitbarTitle=['Local '];
|
||||||
else
|
else
|
||||||
waitbarTitle=[Parallel(ThisMatlab).PcName];
|
waitbarTitle=[Parallel(ThisMatlab).PcName];
|
||||||
end
|
end
|
||||||
fMessageStatus(0,whoiam,waitbarString, waitbarTitle, Parallel(ThisMatlab), MasterName, DyMo);
|
fMessageStatus(0,whoiam,waitbarString, waitbarTitle, Parallel(ThisMatlab), MasterName, DyMo);
|
||||||
end
|
end
|
||||||
for j=fpar:npar,
|
for j=fpar:npar,
|
||||||
fprintf(' Parameter %d... ',j);
|
fprintf(' Parameter %d... ',j);
|
||||||
for b = 1:nblck
|
for b = 1:nblck
|
||||||
startline = 0;
|
startline = 0;
|
||||||
for n = 1:NumberOfMcFilesPerBlock
|
for n = 1:NumberOfMcFilesPerBlock
|
||||||
%load([MhDirectoryName '/' mcfiles(n,1,b).name],'x2');
|
%load([MhDirectoryName '/' mcfiles(n,1,b).name],'x2');
|
||||||
load([MhDirectoryName '/' M_.fname '_mh',int2str(n),'_blck' int2str(b) ...
|
load([MhDirectoryName '/' M_.fname '_mh',int2str(n),'_blck' int2str(b) ...
|
||||||
'.mat'],'x2');
|
'.mat'],'x2');
|
||||||
nx2 = size(x2,1);
|
nx2 = size(x2,1);
|
||||||
tmp((b-1)*NumberOfDraws+startline+(1:nx2),1) = x2(:,j);
|
tmp((b-1)*NumberOfDraws+startline+(1:nx2),1) = x2(:,j);
|
||||||
% clear x2;
|
% clear x2;
|
||||||
startline = startline + nx2;
|
startline = startline + nx2;
|
||||||
end
|
end
|
||||||
% $$$ %load([MhDirectoryName '/' mcfiles(NumberOfMcFilesPerBlock,1,b).name],'x2');
|
% $$$ %load([MhDirectoryName '/' mcfiles(NumberOfMcFilesPerBlock,1,b).name],'x2');
|
||||||
% $$$ load([MhDirectoryName '/' M_.fname '_mh',int2str(NumberOfMcFilesPerBlock),'_blck' int2str(b) '.mat'],'x2');
|
% $$$ load([MhDirectoryName '/' M_.fname '_mh',int2str(NumberOfMcFilesPerBlock),'_blck' int2str(b) '.mat'],'x2');
|
||||||
% $$$ tmp((b-1)*NumberOfDraws+startline+1:(b-1)*NumberOfDraws+MAX_nruns*(LastFileNumber-1)+LastLineNumber,1) = x2(:,j);
|
% $$$ tmp((b-1)*NumberOfDraws+startline+1:(b-1)*NumberOfDraws+MAX_nruns*(LastFileNumber-1)+LastLineNumber,1) = x2(:,j);
|
||||||
% $$$ clear x2;
|
% $$$ clear x2;
|
||||||
% $$$ startline = startline + LastLineNumber;
|
% $$$ startline = startline + LastLineNumber;
|
||||||
end
|
end
|
||||||
tmp(:,2) = kron(transpose(1:nblck),ones(NumberOfDraws,1));
|
tmp(:,2) = kron(transpose(1:nblck),ones(NumberOfDraws,1));
|
||||||
tmp(:,3) = kron(ones(nblck,1),time');
|
tmp(:,3) = kron(ones(nblck,1),time');
|
||||||
tmp = sortrows(tmp,1);
|
tmp = sortrows(tmp,1);
|
||||||
ligne = 0;
|
ligne = 0;
|
||||||
for iter = Origin:StepSize:NumberOfDraws
|
for iter = Origin:StepSize:NumberOfDraws
|
||||||
ligne = ligne+1;
|
ligne = ligne+1;
|
||||||
linea = ceil(0.5*iter);
|
linea = ceil(0.5*iter);
|
||||||
n = iter-linea+1;
|
n = iter-linea+1;
|
||||||
cinf = round(n*ALPHA/2);
|
cinf = round(n*ALPHA/2);
|
||||||
csup = round(n*(1-ALPHA/2));
|
csup = round(n*(1-ALPHA/2));
|
||||||
CINF = round(nblck*n*ALPHA/2);
|
CINF = round(nblck*n*ALPHA/2);
|
||||||
CSUP = round(nblck*n*(1-ALPHA/2));
|
CSUP = round(nblck*n*(1-ALPHA/2));
|
||||||
temp = tmp(find((tmp(:,3)>=linea) & (tmp(:,3)<=iter)),1:2);
|
temp = tmp(find((tmp(:,3)>=linea) & (tmp(:,3)<=iter)),1:2);
|
||||||
UDIAG(ligne,1,j-fpar+1) = temp(CSUP,1)-temp(CINF,1);
|
UDIAG(ligne,1,j-fpar+1) = temp(CSUP,1)-temp(CINF,1);
|
||||||
moyenne = mean(temp(:,1));%% Pooled mean.
|
moyenne = mean(temp(:,1));%% Pooled mean.
|
||||||
UDIAG(ligne,3,j-fpar+1) = sum((temp(:,1)-moyenne).^2)/(nblck*n-1);
|
UDIAG(ligne,3,j-fpar+1) = sum((temp(:,1)-moyenne).^2)/(nblck*n-1);
|
||||||
UDIAG(ligne,5,j-fpar+1) = sum(abs(temp(:,1)-moyenne).^3)/(nblck*n-1);
|
UDIAG(ligne,5,j-fpar+1) = sum(abs(temp(:,1)-moyenne).^3)/(nblck*n-1);
|
||||||
for i=1:nblck
|
for i=1:nblck
|
||||||
pmet = temp(find(temp(:,2)==i));
|
pmet = temp(find(temp(:,2)==i));
|
||||||
UDIAG(ligne,2,j-fpar+1) = UDIAG(ligne,2,j-fpar+1) + pmet(csup,1)-pmet(cinf,1);
|
UDIAG(ligne,2,j-fpar+1) = UDIAG(ligne,2,j-fpar+1) + pmet(csup,1)-pmet(cinf,1);
|
||||||
moyenne = mean(pmet,1); %% Within mean.
|
moyenne = mean(pmet,1); %% Within mean.
|
||||||
UDIAG(ligne,4,j-fpar+1) = UDIAG(ligne,4,j-fpar+1) + sum((pmet(:,1)-moyenne).^2)/(n-1);
|
UDIAG(ligne,4,j-fpar+1) = UDIAG(ligne,4,j-fpar+1) + sum((pmet(:,1)-moyenne).^2)/(n-1);
|
||||||
UDIAG(ligne,6,j-fpar+1) = UDIAG(ligne,6,j-fpar+1) + sum(abs(pmet(:,1)-moyenne).^3)/(n-1);
|
UDIAG(ligne,6,j-fpar+1) = UDIAG(ligne,6,j-fpar+1) + sum(abs(pmet(:,1)-moyenne).^3)/(n-1);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
fprintf('Done! \n');
|
fprintf('Done! \n');
|
||||||
if whoiam,
|
if whoiam,
|
||||||
waitbarString = [ 'Parameter ' int2str(j) '/' int2str(npar) ' done.'];
|
waitbarString = [ 'Parameter ' int2str(j) '/' int2str(npar) ' done.'];
|
||||||
fMessageStatus((j-fpar+1)/(npar-fpar+1),whoiam,waitbarString, waitbarTitle, Parallel(ThisMatlab), MasterName, DyMo)
|
fMessageStatus((j-fpar+1)/(npar-fpar+1),whoiam,waitbarString, waitbarTitle, Parallel(ThisMatlab), MasterName, DyMo)
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
||||||
myoutput.UDIAG = UDIAG;
|
myoutput.UDIAG = UDIAG;
|
||||||
|
|
@ -1,189 +1,189 @@
|
||||||
function oo_ = PlotPosteriorDistributions(estim_params_, M_, options_, bayestopt_, oo_)
|
function oo_ = PlotPosteriorDistributions(estim_params_, M_, options_, bayestopt_, oo_)
|
||||||
|
|
||||||
% function PlotPosteriorDistributions()
|
% function PlotPosteriorDistributions()
|
||||||
% plots posterior distributions
|
% plots posterior distributions
|
||||||
%
|
%
|
||||||
% INPUTS
|
% INPUTS
|
||||||
% estim_params_ [structure]
|
% estim_params_ [structure]
|
||||||
% M_ [structure]
|
% M_ [structure]
|
||||||
% options_ [structure]
|
% options_ [structure]
|
||||||
% bayestopt_ [structure]
|
% bayestopt_ [structure]
|
||||||
% oo_ [structure]
|
% oo_ [structure]
|
||||||
%
|
%
|
||||||
% OUTPUTS
|
% OUTPUTS
|
||||||
% oo_ [structure]
|
% oo_ [structure]
|
||||||
%
|
%
|
||||||
% SPECIAL REQUIREMENTS
|
% SPECIAL REQUIREMENTS
|
||||||
% none
|
% none
|
||||||
|
|
||||||
% Copyright (C) 2005-2008 Dynare Team
|
% Copyright (C) 2005-2008 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
% Dynare is free software: you can redistribute it and/or modify
|
||||||
% it under the terms of the GNU General Public License as published by
|
% it under the terms of the GNU General Public License as published by
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
% the Free Software Foundation, either version 3 of the License, or
|
||||||
% (at your option) any later version.
|
% (at your option) any later version.
|
||||||
%
|
%
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
% Dynare is distributed in the hope that it will be useful,
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
OutputDirectoryName = CheckPath('Output');
|
OutputDirectoryName = CheckPath('Output');
|
||||||
|
|
||||||
TeX = options_.TeX;
|
TeX = options_.TeX;
|
||||||
nblck = options_.mh_nblck;
|
nblck = options_.mh_nblck;
|
||||||
nvx = estim_params_.nvx;
|
nvx = estim_params_.nvx;
|
||||||
nvn = estim_params_.nvn;
|
nvn = estim_params_.nvn;
|
||||||
ncx = estim_params_.ncx;
|
ncx = estim_params_.ncx;
|
||||||
ncn = estim_params_.ncn;
|
ncn = estim_params_.ncn;
|
||||||
np = estim_params_.np ;
|
np = estim_params_.np ;
|
||||||
npar = nvx+nvn+ncx+ncn+np;
|
npar = nvx+nvn+ncx+ncn+np;
|
||||||
|
|
||||||
MaxNumberOfPlotPerFigure = 9;% The square root must be an integer!
|
MaxNumberOfPlotPerFigure = 9;% The square root must be an integer!
|
||||||
nn = sqrt(MaxNumberOfPlotPerFigure);
|
nn = sqrt(MaxNumberOfPlotPerFigure);
|
||||||
|
|
||||||
figurename = 'Priors and posteriors';
|
figurename = 'Priors and posteriors';
|
||||||
|
|
||||||
if TeX
|
if TeX
|
||||||
fidTeX = fopen([OutputDirectoryName '/' M_.fname '_PriorsAndPosteriors.TeX'],'w');
|
fidTeX = fopen([OutputDirectoryName '/' M_.fname '_PriorsAndPosteriors.TeX'],'w');
|
||||||
fprintf(fidTeX,'%% TeX eps-loader file generated by PlotPosteriorDistributions.m (Dynare).\n');
|
fprintf(fidTeX,'%% TeX eps-loader file generated by PlotPosteriorDistributions.m (Dynare).\n');
|
||||||
fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
|
fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
|
||||||
fprintf(fidTeX,' \n');
|
fprintf(fidTeX,' \n');
|
||||||
end
|
end
|
||||||
|
|
||||||
figunumber = 0;
|
figunumber = 0;
|
||||||
subplotnum = 0;
|
subplotnum = 0;
|
||||||
|
|
||||||
for i=1:npar
|
for i=1:npar
|
||||||
subplotnum = subplotnum+1;
|
subplotnum = subplotnum+1;
|
||||||
if subplotnum == 1
|
if subplotnum == 1
|
||||||
figunumber = figunumber+1;
|
figunumber = figunumber+1;
|
||||||
if options_.nograph
|
if options_.nograph
|
||||||
hfig = figure('Name',figurename,'Visible','off');
|
hfig = figure('Name',figurename,'Visible','off');
|
||||||
else
|
else
|
||||||
hfig = figure('Name',figurename);
|
hfig = figure('Name',figurename);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if subplotnum == 1
|
if subplotnum == 1
|
||||||
if TeX
|
if TeX
|
||||||
TeXNAMES = [];
|
TeXNAMES = [];
|
||||||
end
|
end
|
||||||
NAMES = [];
|
NAMES = [];
|
||||||
end
|
end
|
||||||
[nam,texnam] = get_the_name(i,TeX);
|
[nam,texnam] = get_the_name(i,TeX);
|
||||||
NAMES = strvcat(NAMES,nam);
|
NAMES = strvcat(NAMES,nam);
|
||||||
if TeX
|
if TeX
|
||||||
TeXNAMES = strvcat(TeXNAMES,texnam);
|
TeXNAMES = strvcat(TeXNAMES,texnam);
|
||||||
end
|
end
|
||||||
[x2,f2,abscissa,dens,binf2,bsup2] = draw_prior_density(i,bayestopt_);
|
[x2,f2,abscissa,dens,binf2,bsup2] = draw_prior_density(i,bayestopt_);
|
||||||
top2 = max(f2);
|
top2 = max(f2);
|
||||||
if i <= nvx
|
if i <= nvx
|
||||||
name = deblank(M_.exo_names(estim_params_.var_exo(i,1),:));
|
name = deblank(M_.exo_names(estim_params_.var_exo(i,1),:));
|
||||||
eval(['x1 = oo_.posterior_density.shocks_std.' name '(:,1);'])
|
eval(['x1 = oo_.posterior_density.shocks_std.' name '(:,1);'])
|
||||||
eval(['f1 = oo_.posterior_density.shocks_std.' name '(:,2);'])
|
eval(['f1 = oo_.posterior_density.shocks_std.' name '(:,2);'])
|
||||||
eval(['oo_.prior_density.shocks_std.' name '(:,1) = x2;'])
|
eval(['oo_.prior_density.shocks_std.' name '(:,1) = x2;'])
|
||||||
eval(['oo_.prior_density.shocks_std.' name '(:,2) = f2;'])
|
eval(['oo_.prior_density.shocks_std.' name '(:,2) = f2;'])
|
||||||
if options_.posterior_mode_estimation
|
if options_.posterior_mode_estimation
|
||||||
eval(['pmod = oo_.posterior_mode.shocks_std.' name ';'])
|
eval(['pmod = oo_.posterior_mode.shocks_std.' name ';'])
|
||||||
end
|
end
|
||||||
elseif i <= nvx+nvn
|
elseif i <= nvx+nvn
|
||||||
name = deblank(options_.varobs(estim_params_.var_endo(i-nvx,1),:));
|
name = deblank(options_.varobs(estim_params_.var_endo(i-nvx,1),:));
|
||||||
eval(['x1 = oo_.posterior_density.measurement_errors_std.' name '(:,1);'])
|
eval(['x1 = oo_.posterior_density.measurement_errors_std.' name '(:,1);'])
|
||||||
eval(['f1 = oo_.posterior_density.measurement_errors_std.' name '(:,2);'])
|
eval(['f1 = oo_.posterior_density.measurement_errors_std.' name '(:,2);'])
|
||||||
eval(['oo_.prior_density.mearsurement_errors_std.' name '(:,1) = x2;'])
|
eval(['oo_.prior_density.mearsurement_errors_std.' name '(:,1) = x2;'])
|
||||||
eval(['oo_.prior_density.measurement_errors_std.' name '(:,2) = f2;'])
|
eval(['oo_.prior_density.measurement_errors_std.' name '(:,2) = f2;'])
|
||||||
if options_.posterior_mode_estimation
|
if options_.posterior_mode_estimation
|
||||||
eval(['pmod = oo_.posterior_mode.measurement_errors_std.' name ';'])
|
eval(['pmod = oo_.posterior_mode.measurement_errors_std.' name ';'])
|
||||||
end
|
end
|
||||||
elseif i <= nvx+nvn+ncx
|
elseif i <= nvx+nvn+ncx
|
||||||
j = i - (nvx+nvn);
|
j = i - (nvx+nvn);
|
||||||
k1 = estim_params_.corrx(j,1);
|
k1 = estim_params_.corrx(j,1);
|
||||||
k2 = estim_params_.corrx(j,2);
|
k2 = estim_params_.corrx(j,2);
|
||||||
name = [deblank(M_.exo_names(k1,:)) '_' deblank(M_.exo_names(k2,:))];
|
name = [deblank(M_.exo_names(k1,:)) '_' deblank(M_.exo_names(k2,:))];
|
||||||
eval(['x1 = oo_.posterior_density.shocks_corr.' name '(:,1);'])
|
eval(['x1 = oo_.posterior_density.shocks_corr.' name '(:,1);'])
|
||||||
eval(['f1 = oo_.posterior_density.shocks_corr.' name '(:,2);'])
|
eval(['f1 = oo_.posterior_density.shocks_corr.' name '(:,2);'])
|
||||||
eval(['oo_.prior_density.shocks_corr.' name '(:,1) = x2;'])
|
eval(['oo_.prior_density.shocks_corr.' name '(:,1) = x2;'])
|
||||||
eval(['oo_.prior_density.shocks_corr.' name '(:,2) = f2;'])
|
eval(['oo_.prior_density.shocks_corr.' name '(:,2) = f2;'])
|
||||||
if options_.posterior_mode_estimation
|
if options_.posterior_mode_estimation
|
||||||
eval(['pmod = oo_.posterior_mode.shocks_corr.' name ';'])
|
eval(['pmod = oo_.posterior_mode.shocks_corr.' name ';'])
|
||||||
end
|
end
|
||||||
elseif i <= nvx+nvn+ncx+ncn
|
elseif i <= nvx+nvn+ncx+ncn
|
||||||
j = i - (nvx+nvn+ncx);
|
j = i - (nvx+nvn+ncx);
|
||||||
k1 = estim_params_.corrn(j,1);
|
k1 = estim_params_.corrn(j,1);
|
||||||
k2 = estim_params_.corrn(j,2);
|
k2 = estim_params_.corrn(j,2);
|
||||||
name = [deblank(M_.endo_names(k1,:)) '_' deblank(M_.endo_names(k2,:))];
|
name = [deblank(M_.endo_names(k1,:)) '_' deblank(M_.endo_names(k2,:))];
|
||||||
eval(['x1 = oo_.posterior_density.measurement_errors_corr.' name '(:,1);'])
|
eval(['x1 = oo_.posterior_density.measurement_errors_corr.' name '(:,1);'])
|
||||||
eval(['f1 = oo_.posterior_density.measurement_errors_corr.' name '(:,2);'])
|
eval(['f1 = oo_.posterior_density.measurement_errors_corr.' name '(:,2);'])
|
||||||
eval(['oo_.prior_density.mearsurement_errors_corr.' name '(:,1) = x2;'])
|
eval(['oo_.prior_density.mearsurement_errors_corr.' name '(:,1) = x2;'])
|
||||||
eval(['oo_.prior_density.measurement_errors_corr.' name '(:,2) = f2;'])
|
eval(['oo_.prior_density.measurement_errors_corr.' name '(:,2) = f2;'])
|
||||||
if options_.posterior_mode_estimation
|
if options_.posterior_mode_estimation
|
||||||
eval(['pmod = oo_.posterior_mode.measurement_errors_corr.' name ';'])
|
eval(['pmod = oo_.posterior_mode.measurement_errors_corr.' name ';'])
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
j = i - (nvx+nvn+ncx+ncn);
|
j = i - (nvx+nvn+ncx+ncn);
|
||||||
name = deblank(M_.param_names(estim_params_.param_vals(j,1),:));
|
name = deblank(M_.param_names(estim_params_.param_vals(j,1),:));
|
||||||
eval(['x1 = oo_.posterior_density.parameters.' name '(:,1);'])
|
eval(['x1 = oo_.posterior_density.parameters.' name '(:,1);'])
|
||||||
eval(['f1 = oo_.posterior_density.parameters.' name '(:,2);'])
|
eval(['f1 = oo_.posterior_density.parameters.' name '(:,2);'])
|
||||||
eval(['oo_.prior_density.parameters.' name '(:,1) = x2;'])
|
eval(['oo_.prior_density.parameters.' name '(:,1) = x2;'])
|
||||||
eval(['oo_.prior_density.parameters.' name '(:,2) = f2;'])
|
eval(['oo_.prior_density.parameters.' name '(:,2) = f2;'])
|
||||||
if options_.posterior_mode_estimation
|
if options_.posterior_mode_estimation
|
||||||
eval(['pmod = oo_.posterior_mode.parameters.' name ';'])
|
eval(['pmod = oo_.posterior_mode.parameters.' name ';'])
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
top1 = max(f1);
|
top1 = max(f1);
|
||||||
top0 = max([top1;top2]);
|
top0 = max([top1;top2]);
|
||||||
binf1 = x1(1);
|
binf1 = x1(1);
|
||||||
bsup1 = x1(end);
|
bsup1 = x1(end);
|
||||||
borneinf = min(binf1,binf2);
|
borneinf = min(binf1,binf2);
|
||||||
bornesup = max(bsup1,bsup2);
|
bornesup = max(bsup1,bsup2);
|
||||||
subplot(nn,nn,subplotnum)
|
subplot(nn,nn,subplotnum)
|
||||||
hh = plot(x2,f2,'-k','linewidth',2);
|
hh = plot(x2,f2,'-k','linewidth',2);
|
||||||
set(hh,'color',[0.7 0.7 0.7]);
|
set(hh,'color',[0.7 0.7 0.7]);
|
||||||
hold on;
|
hold on;
|
||||||
plot(x1,f1,'-k','linewidth',2);
|
plot(x1,f1,'-k','linewidth',2);
|
||||||
if options_.posterior_mode_estimation
|
if options_.posterior_mode_estimation
|
||||||
plot( [pmod pmod], [0.0 1.1*top0], '--g', 'linewidth', 2);
|
plot( [pmod pmod], [0.0 1.1*top0], '--g', 'linewidth', 2);
|
||||||
end
|
end
|
||||||
box on;
|
box on;
|
||||||
axis([borneinf bornesup 0 1.1*top0]);
|
axis([borneinf bornesup 0 1.1*top0]);
|
||||||
title(nam,'Interpreter','none');
|
title(nam,'Interpreter','none');
|
||||||
hold off;
|
hold off;
|
||||||
drawnow
|
drawnow
|
||||||
if subplotnum == MaxNumberOfPlotPerFigure | i == npar;
|
if subplotnum == MaxNumberOfPlotPerFigure | i == npar;
|
||||||
eval(['print -depsc2 ' OutputDirectoryName '/' M_.fname '_PriorsAndPosteriors' int2str(figunumber) '.eps']);
|
eval(['print -depsc2 ' OutputDirectoryName '/' M_.fname '_PriorsAndPosteriors' int2str(figunumber) '.eps']);
|
||||||
if ~exist('OCTAVE_VERSION')
|
if ~exist('OCTAVE_VERSION')
|
||||||
eval(['print -dpdf ' OutputDirectoryName '/' M_.fname '_PriorsAndPosteriors' int2str(figunumber)]);
|
eval(['print -dpdf ' OutputDirectoryName '/' M_.fname '_PriorsAndPosteriors' int2str(figunumber)]);
|
||||||
end
|
end
|
||||||
if options_.nograph,
|
if options_.nograph,
|
||||||
set(hfig,'Visible','on');
|
set(hfig,'Visible','on');
|
||||||
end
|
end
|
||||||
if ~exist('OCTAVE_VERSION')
|
if ~exist('OCTAVE_VERSION')
|
||||||
saveas(hfig,[OutputDirectoryName '/' M_.fname '_PriorsAndPosteriors' int2str(figunumber) '.fig']);
|
saveas(hfig,[OutputDirectoryName '/' M_.fname '_PriorsAndPosteriors' int2str(figunumber) '.fig']);
|
||||||
end
|
end
|
||||||
if TeX
|
if TeX
|
||||||
fprintf(fidTeX,'\\begin{figure}[H]\n');
|
fprintf(fidTeX,'\\begin{figure}[H]\n');
|
||||||
for j = 1:size(NAMES,1)
|
for j = 1:size(NAMES,1)
|
||||||
fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(j,:)),deblank(TeXNAMES(j,:)));
|
fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(j,:)),deblank(TeXNAMES(j,:)));
|
||||||
end
|
end
|
||||||
fprintf(fidTeX,'\\centering\n');
|
fprintf(fidTeX,'\\centering\n');
|
||||||
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_PriorsAndPosteriors%s}\n',M_.fname,int2str(figunumber));
|
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_PriorsAndPosteriors%s}\n',M_.fname,int2str(figunumber));
|
||||||
fprintf(fidTeX,'\\caption{Priors and posteriors.}');
|
fprintf(fidTeX,'\\caption{Priors and posteriors.}');
|
||||||
fprintf(fidTeX,'\\label{Fig:PriorsAndPosteriors:%s}\n',int2str(figunumber));
|
fprintf(fidTeX,'\\label{Fig:PriorsAndPosteriors:%s}\n',int2str(figunumber));
|
||||||
fprintf(fidTeX,'\\end{figure}\n');
|
fprintf(fidTeX,'\\end{figure}\n');
|
||||||
fprintf(fidTeX,' \n');
|
fprintf(fidTeX,' \n');
|
||||||
if i == npar
|
if i == npar
|
||||||
fprintf(fidTeX,'%% End of TeX file.\n');
|
fprintf(fidTeX,'%% End of TeX file.\n');
|
||||||
fclose(fidTeX);
|
fclose(fidTeX);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if options_.nograph,
|
if options_.nograph,
|
||||||
close(hfig),
|
close(hfig),
|
||||||
end
|
end
|
||||||
subplotnum = 0;
|
subplotnum = 0;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
@ -1,273 +1,273 @@
|
||||||
function PosteriorFilterSmootherAndForecast(Y,gend, type,data_index)
|
function PosteriorFilterSmootherAndForecast(Y,gend, type,data_index)
|
||||||
|
|
||||||
% function PosteriorFilterSmootherAndForecast(Y,gend, type)
|
% function PosteriorFilterSmootherAndForecast(Y,gend, type)
|
||||||
% Computes posterior filter smoother and forecasts
|
% Computes posterior filter smoother and forecasts
|
||||||
%
|
%
|
||||||
% INPUTS
|
% INPUTS
|
||||||
% Y: data
|
% Y: data
|
||||||
% gend: number of observations
|
% gend: number of observations
|
||||||
% type: posterior
|
% type: posterior
|
||||||
% prior
|
% prior
|
||||||
% gsa
|
% gsa
|
||||||
%
|
%
|
||||||
% OUTPUTS
|
% OUTPUTS
|
||||||
% none
|
% none
|
||||||
%
|
%
|
||||||
% SPECIAL REQUIREMENTS
|
% SPECIAL REQUIREMENTS
|
||||||
% none
|
% none
|
||||||
|
|
||||||
% Copyright (C) 2005-2008 Dynare Team
|
% Copyright (C) 2005-2008 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
% Dynare is free software: you can redistribute it and/or modify
|
||||||
% it under the terms of the GNU General Public License as published by
|
% it under the terms of the GNU General Public License as published by
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
% the Free Software Foundation, either version 3 of the License, or
|
||||||
% (at your option) any later version.
|
% (at your option) any later version.
|
||||||
%
|
%
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
% Dynare is distributed in the hope that it will be useful,
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
global options_ estim_params_ oo_ M_ bayestopt_
|
global options_ estim_params_ oo_ M_ bayestopt_
|
||||||
|
|
||||||
nvx = estim_params_.nvx;
|
nvx = estim_params_.nvx;
|
||||||
nvn = estim_params_.nvn;
|
nvn = estim_params_.nvn;
|
||||||
ncx = estim_params_.ncx;
|
ncx = estim_params_.ncx;
|
||||||
ncn = estim_params_.ncn;
|
ncn = estim_params_.ncn;
|
||||||
np = estim_params_.np ;
|
np = estim_params_.np ;
|
||||||
npar = nvx+nvn+ncx+ncn+np;
|
npar = nvx+nvn+ncx+ncn+np;
|
||||||
offset = npar-np;
|
offset = npar-np;
|
||||||
naK = length(options_.filter_step_ahead);
|
naK = length(options_.filter_step_ahead);
|
||||||
%%
|
%%
|
||||||
MaxNumberOfPlotPerFigure = 4;% The square root must be an integer!
|
MaxNumberOfPlotPerFigure = 4;% The square root must be an integer!
|
||||||
MaxNumberOfBytes=options_.MaxNumberOfBytes;
|
MaxNumberOfBytes=options_.MaxNumberOfBytes;
|
||||||
endo_nbr=M_.endo_nbr;
|
endo_nbr=M_.endo_nbr;
|
||||||
exo_nbr=M_.exo_nbr;
|
exo_nbr=M_.exo_nbr;
|
||||||
nvobs = size(options_.varobs,1);
|
nvobs = size(options_.varobs,1);
|
||||||
nn = sqrt(MaxNumberOfPlotPerFigure);
|
nn = sqrt(MaxNumberOfPlotPerFigure);
|
||||||
iendo = 1:endo_nbr;
|
iendo = 1:endo_nbr;
|
||||||
i_last_obs = gend+(1-M_.maximum_endo_lag:0);
|
i_last_obs = gend+(1-M_.maximum_endo_lag:0);
|
||||||
horizon = options_.forecast;
|
horizon = options_.forecast;
|
||||||
maxlag = M_.maximum_endo_lag;
|
maxlag = M_.maximum_endo_lag;
|
||||||
%%
|
%%
|
||||||
CheckPath('Plots/');
|
CheckPath('Plots/');
|
||||||
DirectoryName = CheckPath('metropolis');
|
DirectoryName = CheckPath('metropolis');
|
||||||
load([ DirectoryName '/' M_.fname '_mh_history.mat'])
|
load([ DirectoryName '/' M_.fname '_mh_history.mat'])
|
||||||
FirstMhFile = record.KeepedDraws.FirstMhFile;
|
FirstMhFile = record.KeepedDraws.FirstMhFile;
|
||||||
FirstLine = record.KeepedDraws.FirstLine;
|
FirstLine = record.KeepedDraws.FirstLine;
|
||||||
TotalNumberOfMhFiles = sum(record.MhDraws(:,2)); LastMhFile = TotalNumberOfMhFiles;
|
TotalNumberOfMhFiles = sum(record.MhDraws(:,2)); LastMhFile = TotalNumberOfMhFiles;
|
||||||
TotalNumberOfMhDraws = sum(record.MhDraws(:,1));
|
TotalNumberOfMhDraws = sum(record.MhDraws(:,1));
|
||||||
NumberOfDraws = TotalNumberOfMhDraws-floor(options_.mh_drop*TotalNumberOfMhDraws);
|
NumberOfDraws = TotalNumberOfMhDraws-floor(options_.mh_drop*TotalNumberOfMhDraws);
|
||||||
clear record;
|
clear record;
|
||||||
B = min(1200, round(0.25*NumberOfDraws));
|
B = min(1200, round(0.25*NumberOfDraws));
|
||||||
B = 200;
|
B = 200;
|
||||||
%%
|
%%
|
||||||
MAX_nruns = min(B,ceil(options_.MaxNumberOfBytes/(npar+2)/8));
|
MAX_nruns = min(B,ceil(options_.MaxNumberOfBytes/(npar+2)/8));
|
||||||
MAX_nsmoo = min(B,ceil(MaxNumberOfBytes/((endo_nbr)*gend)/8));
|
MAX_nsmoo = min(B,ceil(MaxNumberOfBytes/((endo_nbr)*gend)/8));
|
||||||
MAX_ninno = min(B,ceil(MaxNumberOfBytes/(exo_nbr*gend)/8));
|
MAX_ninno = min(B,ceil(MaxNumberOfBytes/(exo_nbr*gend)/8));
|
||||||
MAX_nerro = min(B,ceil(MaxNumberOfBytes/(size(options_.varobs,1)*gend)/8));
|
MAX_nerro = min(B,ceil(MaxNumberOfBytes/(size(options_.varobs,1)*gend)/8));
|
||||||
if naK
|
if naK
|
||||||
MAX_naK = min(B,ceil(MaxNumberOfBytes/(size(options_.varobs,1)* ...
|
MAX_naK = min(B,ceil(MaxNumberOfBytes/(size(options_.varobs,1)* ...
|
||||||
length(options_.filter_step_ahead)*gend)/8));
|
length(options_.filter_step_ahead)*gend)/8));
|
||||||
end
|
end
|
||||||
if horizon
|
if horizon
|
||||||
MAX_nforc1 = min(B,ceil(MaxNumberOfBytes/((endo_nbr)*(horizon+maxlag))/8));
|
MAX_nforc1 = min(B,ceil(MaxNumberOfBytes/((endo_nbr)*(horizon+maxlag))/8));
|
||||||
MAX_nforc2 = min(B,ceil(MaxNumberOfBytes/((endo_nbr)*(horizon+maxlag))/ ...
|
MAX_nforc2 = min(B,ceil(MaxNumberOfBytes/((endo_nbr)*(horizon+maxlag))/ ...
|
||||||
8));
|
8));
|
||||||
IdObs = bayestopt_.mfys;
|
IdObs = bayestopt_.mfys;
|
||||||
|
|
||||||
end
|
end
|
||||||
|
|
||||||
%%
|
%%
|
||||||
varlist = options_.varlist;
|
varlist = options_.varlist;
|
||||||
if isempty(varlist)
|
if isempty(varlist)
|
||||||
varlist = M_.endo_names;
|
varlist = M_.endo_names;
|
||||||
SelecVariables = transpose(1:M_.endo_nbr);
|
SelecVariables = transpose(1:M_.endo_nbr);
|
||||||
nvar = M_.endo_nbr;
|
nvar = M_.endo_nbr;
|
||||||
else
|
else
|
||||||
nvar = size(varlist,1);
|
nvar = size(varlist,1);
|
||||||
SelecVariables = [];
|
SelecVariables = [];
|
||||||
for i=1:nvar
|
for i=1:nvar
|
||||||
if ~isempty(strmatch(varlist(i,:),M_.endo_names,'exact'))
|
if ~isempty(strmatch(varlist(i,:),M_.endo_names,'exact'))
|
||||||
SelecVariables = [SelecVariables;strmatch(varlist(i,:),M_.endo_names,'exact')];
|
SelecVariables = [SelecVariables;strmatch(varlist(i,:),M_.endo_names,'exact')];
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
||||||
irun1 = 1;
|
irun1 = 1;
|
||||||
irun2 = 1;
|
irun2 = 1;
|
||||||
irun3 = 1;
|
irun3 = 1;
|
||||||
irun4 = 1;
|
irun4 = 1;
|
||||||
irun5 = 1;
|
irun5 = 1;
|
||||||
irun6 = 1;
|
irun6 = 1;
|
||||||
irun7 = 1;
|
irun7 = 1;
|
||||||
ifil1 = 0;
|
ifil1 = 0;
|
||||||
ifil2 = 0;
|
ifil2 = 0;
|
||||||
ifil3 = 0;
|
ifil3 = 0;
|
||||||
ifil4 = 0;
|
ifil4 = 0;
|
||||||
ifil5 = 0;
|
ifil5 = 0;
|
||||||
ifil6 = 0;
|
ifil6 = 0;
|
||||||
ifil7 = 0;
|
ifil7 = 0;
|
||||||
|
|
||||||
h = waitbar(0,'Bayesian smoother...');
|
h = waitbar(0,'Bayesian smoother...');
|
||||||
|
|
||||||
stock_param = zeros(MAX_nruns, npar);
|
stock_param = zeros(MAX_nruns, npar);
|
||||||
stock_logpo = zeros(MAX_nruns,1);
|
stock_logpo = zeros(MAX_nruns,1);
|
||||||
stock_ys = zeros(MAX_nruns,endo_nbr);
|
stock_ys = zeros(MAX_nruns,endo_nbr);
|
||||||
if options_.smoother
|
if options_.smoother
|
||||||
stock_smooth = zeros(endo_nbr,gend,MAX_nsmoo);
|
stock_smooth = zeros(endo_nbr,gend,MAX_nsmoo);
|
||||||
stock_innov = zeros(exo_nbr,gend,B);
|
stock_innov = zeros(exo_nbr,gend,B);
|
||||||
stock_error = zeros(nvobs,gend,MAX_nerro);
|
stock_error = zeros(nvobs,gend,MAX_nerro);
|
||||||
end
|
end
|
||||||
if options_.filter_step_ahead
|
if options_.filter_step_ahead
|
||||||
stock_filter = zeros(naK,endo_nbr,gend+options_.filter_step_ahead(end),MAX_naK);
|
stock_filter = zeros(naK,endo_nbr,gend+options_.filter_step_ahead(end),MAX_naK);
|
||||||
end
|
end
|
||||||
if options_.forecast
|
if options_.forecast
|
||||||
stock_forcst_mean = zeros(endo_nbr,horizon+maxlag,MAX_nforc1);
|
stock_forcst_mean = zeros(endo_nbr,horizon+maxlag,MAX_nforc1);
|
||||||
stock_forcst_total = zeros(endo_nbr,horizon+maxlag,MAX_nforc2);
|
stock_forcst_total = zeros(endo_nbr,horizon+maxlag,MAX_nforc2);
|
||||||
end
|
end
|
||||||
|
|
||||||
for b=1:B
|
for b=1:B
|
||||||
%deep = GetOneDraw(NumberOfDraws,FirstMhFile,LastMhFile,FirstLine,MAX_nruns,DirectoryName);
|
%deep = GetOneDraw(NumberOfDraws,FirstMhFile,LastMhFile,FirstLine,MAX_nruns,DirectoryName);
|
||||||
[deep, logpo] = GetOneDraw(type);
|
[deep, logpo] = GetOneDraw(type);
|
||||||
set_all_parameters(deep);
|
set_all_parameters(deep);
|
||||||
dr = resol(oo_.steady_state,0);
|
dr = resol(oo_.steady_state,0);
|
||||||
[alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = ...
|
[alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = ...
|
||||||
DsgeSmoother(deep,gend,Y,data_index);
|
DsgeSmoother(deep,gend,Y,data_index);
|
||||||
|
|
||||||
if options_.loglinear
|
if options_.loglinear
|
||||||
stock_smooth(dr.order_var,:,irun1) = alphahat(1:endo_nbr,:)+ ...
|
stock_smooth(dr.order_var,:,irun1) = alphahat(1:endo_nbr,:)+ ...
|
||||||
repmat(log(dr.ys(dr.order_var)),1,gend);
|
repmat(log(dr.ys(dr.order_var)),1,gend);
|
||||||
else
|
else
|
||||||
stock_smooth(dr.order_var,:,irun1) = alphahat(1:endo_nbr,:)+ ...
|
stock_smooth(dr.order_var,:,irun1) = alphahat(1:endo_nbr,:)+ ...
|
||||||
repmat(dr.ys(dr.order_var),1,gend);
|
repmat(dr.ys(dr.order_var),1,gend);
|
||||||
end
|
end
|
||||||
if nvx
|
if nvx
|
||||||
stock_innov(:,:,irun2) = etahat;
|
stock_innov(:,:,irun2) = etahat;
|
||||||
end
|
end
|
||||||
if nvn
|
if nvn
|
||||||
stock_error(:,:,irun3) = epsilonhat;
|
stock_error(:,:,irun3) = epsilonhat;
|
||||||
end
|
end
|
||||||
if naK
|
if naK
|
||||||
stock_filter(:,dr.order_var,:,irun4) = aK(options_.filter_step_ahead,1:endo_nbr,:);
|
stock_filter(:,dr.order_var,:,irun4) = aK(options_.filter_step_ahead,1:endo_nbr,:);
|
||||||
end
|
end
|
||||||
stock_param(irun5,:) = deep;
|
stock_param(irun5,:) = deep;
|
||||||
stock_logpo(irun5,1) = logpo;
|
stock_logpo(irun5,1) = logpo;
|
||||||
stock_ys(irun5,:) = SteadyState';
|
stock_ys(irun5,:) = SteadyState';
|
||||||
|
|
||||||
if horizon
|
if horizon
|
||||||
yyyy = alphahat(iendo,i_last_obs);
|
yyyy = alphahat(iendo,i_last_obs);
|
||||||
yf = forcst2a(yyyy,dr,zeros(horizon,exo_nbr));
|
yf = forcst2a(yyyy,dr,zeros(horizon,exo_nbr));
|
||||||
if options_.prefilter == 1
|
if options_.prefilter == 1
|
||||||
yf(:,IdObs) = yf(:,IdObs)+repmat(bayestopt_.mean_varobs', ...
|
yf(:,IdObs) = yf(:,IdObs)+repmat(bayestopt_.mean_varobs', ...
|
||||||
horizon+maxlag,1);
|
horizon+maxlag,1);
|
||||||
end
|
end
|
||||||
yf(:,IdObs) = yf(:,IdObs)+(gend+[1-maxlag:horizon]')*trend_coeff';
|
yf(:,IdObs) = yf(:,IdObs)+(gend+[1-maxlag:horizon]')*trend_coeff';
|
||||||
if options_.loglinear == 1
|
if options_.loglinear == 1
|
||||||
yf = yf+repmat(log(SteadyState'),horizon+maxlag,1);
|
yf = yf+repmat(log(SteadyState'),horizon+maxlag,1);
|
||||||
% yf = exp(yf);
|
% yf = exp(yf);
|
||||||
else
|
else
|
||||||
yf = yf+repmat(SteadyState',horizon+maxlag,1);
|
yf = yf+repmat(SteadyState',horizon+maxlag,1);
|
||||||
end
|
end
|
||||||
yf1 = forcst2(yyyy,horizon,dr,1);
|
yf1 = forcst2(yyyy,horizon,dr,1);
|
||||||
if options_.prefilter == 1
|
if options_.prefilter == 1
|
||||||
yf1(:,IdObs,:) = yf1(:,IdObs,:)+ ...
|
yf1(:,IdObs,:) = yf1(:,IdObs,:)+ ...
|
||||||
repmat(bayestopt_.mean_varobs',[horizon+maxlag,1,1]);
|
repmat(bayestopt_.mean_varobs',[horizon+maxlag,1,1]);
|
||||||
end
|
end
|
||||||
yf1(:,IdObs,:) = yf1(:,IdObs,:)+repmat((gend+[1-maxlag:horizon]')* ...
|
yf1(:,IdObs,:) = yf1(:,IdObs,:)+repmat((gend+[1-maxlag:horizon]')* ...
|
||||||
trend_coeff',[1,1,1]);
|
trend_coeff',[1,1,1]);
|
||||||
if options_.loglinear == 1
|
if options_.loglinear == 1
|
||||||
yf1 = yf1 + repmat(log(SteadyState'),[horizon+maxlag,1,1]);
|
yf1 = yf1 + repmat(log(SteadyState'),[horizon+maxlag,1,1]);
|
||||||
% yf1 = exp(yf1);
|
% yf1 = exp(yf1);
|
||||||
else
|
else
|
||||||
yf1 = yf1 + repmat(SteadyState',[horizon+maxlag,1,1]);
|
yf1 = yf1 + repmat(SteadyState',[horizon+maxlag,1,1]);
|
||||||
end
|
end
|
||||||
|
|
||||||
stock_forcst_mean(:,:,irun6) = yf';
|
stock_forcst_mean(:,:,irun6) = yf';
|
||||||
stock_forcst_total(:,:,irun7) = yf1';
|
stock_forcst_total(:,:,irun7) = yf1';
|
||||||
end
|
end
|
||||||
|
|
||||||
irun1 = irun1 + 1;
|
irun1 = irun1 + 1;
|
||||||
irun2 = irun2 + 1;
|
irun2 = irun2 + 1;
|
||||||
irun3 = irun3 + 1;
|
irun3 = irun3 + 1;
|
||||||
irun4 = irun4 + 1;
|
irun4 = irun4 + 1;
|
||||||
irun5 = irun5 + 1;
|
irun5 = irun5 + 1;
|
||||||
irun6 = irun6 + 1;
|
irun6 = irun6 + 1;
|
||||||
irun7 = irun7 + 1;
|
irun7 = irun7 + 1;
|
||||||
|
|
||||||
if irun1 > MAX_nsmoo | b == B
|
if irun1 > MAX_nsmoo | b == B
|
||||||
stock = stock_smooth(:,:,1:irun1-1);
|
stock = stock_smooth(:,:,1:irun1-1);
|
||||||
ifil1 = ifil1 + 1;
|
ifil1 = ifil1 + 1;
|
||||||
save([DirectoryName '/' M_.fname '_smooth' int2str(ifil1) '.mat'],'stock');
|
save([DirectoryName '/' M_.fname '_smooth' int2str(ifil1) '.mat'],'stock');
|
||||||
irun1 = 1;
|
irun1 = 1;
|
||||||
end
|
end
|
||||||
|
|
||||||
if nvx & (irun2 > MAX_ninno | b == B)
|
if nvx & (irun2 > MAX_ninno | b == B)
|
||||||
stock = stock_innov(:,:,1:irun2-1);
|
stock = stock_innov(:,:,1:irun2-1);
|
||||||
ifil2 = ifil2 + 1;
|
ifil2 = ifil2 + 1;
|
||||||
save([DirectoryName '/' M_.fname '_inno' int2str(ifil2) '.mat'],'stock');
|
save([DirectoryName '/' M_.fname '_inno' int2str(ifil2) '.mat'],'stock');
|
||||||
irun2 = 1;
|
irun2 = 1;
|
||||||
end
|
end
|
||||||
|
|
||||||
if nvn & (irun3 > MAX_error | b == B)
|
if nvn & (irun3 > MAX_error | b == B)
|
||||||
stock = stock_error(:,:,1:irun3-1);
|
stock = stock_error(:,:,1:irun3-1);
|
||||||
ifil3 = ifil3 + 1;
|
ifil3 = ifil3 + 1;
|
||||||
save([DirectoryName '/' M_.fname '_error' int2str(ifil3) '.mat'],'stock');
|
save([DirectoryName '/' M_.fname '_error' int2str(ifil3) '.mat'],'stock');
|
||||||
irun3 = 1;
|
irun3 = 1;
|
||||||
end
|
end
|
||||||
|
|
||||||
if naK & (irun4 > MAX_naK | b == B)
|
if naK & (irun4 > MAX_naK | b == B)
|
||||||
stock = stock_filter(:,:,:,1:irun4-1);
|
stock = stock_filter(:,:,:,1:irun4-1);
|
||||||
ifil4 = ifil4 + 1;
|
ifil4 = ifil4 + 1;
|
||||||
save([DirectoryName '/' M_.fname '_filter' int2str(ifil4) '.mat'],'stock');
|
save([DirectoryName '/' M_.fname '_filter' int2str(ifil4) '.mat'],'stock');
|
||||||
irun4 = 1;
|
irun4 = 1;
|
||||||
end
|
end
|
||||||
|
|
||||||
if irun5 > MAX_nruns | b == B
|
if irun5 > MAX_nruns | b == B
|
||||||
stock = stock_param(1:irun5-1,:);
|
stock = stock_param(1:irun5-1,:);
|
||||||
ifil5 = ifil5 + 1;
|
ifil5 = ifil5 + 1;
|
||||||
save([DirectoryName '/' M_.fname '_param' int2str(ifil5) '.mat'],'stock','stock_logpo','stock_ys');
|
save([DirectoryName '/' M_.fname '_param' int2str(ifil5) '.mat'],'stock','stock_logpo','stock_ys');
|
||||||
irun5 = 1;
|
irun5 = 1;
|
||||||
end
|
end
|
||||||
|
|
||||||
if horizon & (irun6 > MAX_nforc1 | b == B)
|
if horizon & (irun6 > MAX_nforc1 | b == B)
|
||||||
stock = stock_forcst_mean(:,:,1:irun6-1);
|
stock = stock_forcst_mean(:,:,1:irun6-1);
|
||||||
ifil6 = ifil6 + 1;
|
ifil6 = ifil6 + 1;
|
||||||
save([DirectoryName '/' M_.fname '_forc_mean' int2str(ifil6) '.mat'],'stock');
|
save([DirectoryName '/' M_.fname '_forc_mean' int2str(ifil6) '.mat'],'stock');
|
||||||
irun6 = 1;
|
irun6 = 1;
|
||||||
end
|
end
|
||||||
|
|
||||||
if horizon & (irun7 > MAX_nforc2 | b == B)
|
if horizon & (irun7 > MAX_nforc2 | b == B)
|
||||||
stock = stock_forcst_total(:,:,1:irun7-1);
|
stock = stock_forcst_total(:,:,1:irun7-1);
|
||||||
ifil7 = ifil7 + 1;
|
ifil7 = ifil7 + 1;
|
||||||
save([DirectoryName '/' M_.fname '_forc_total' int2str(ifil7) '.mat'],'stock');
|
save([DirectoryName '/' M_.fname '_forc_total' int2str(ifil7) '.mat'],'stock');
|
||||||
irun7 = 1;
|
irun7 = 1;
|
||||||
end
|
end
|
||||||
|
|
||||||
waitbar(b/B,h);
|
waitbar(b/B,h);
|
||||||
end
|
end
|
||||||
close(h)
|
close(h)
|
||||||
|
|
||||||
stock_gend=gend;
|
stock_gend=gend;
|
||||||
stock_data=Y;
|
stock_data=Y;
|
||||||
save([DirectoryName '/' M_.fname '_data.mat'],'stock_gend','stock_data');
|
save([DirectoryName '/' M_.fname '_data.mat'],'stock_gend','stock_data');
|
||||||
|
|
||||||
if options_.smoother
|
if options_.smoother
|
||||||
pm3(endo_nbr,gend,ifil1,B,'Smoothed variables',...
|
pm3(endo_nbr,gend,ifil1,B,'Smoothed variables',...
|
||||||
M_.endo_names(SelecVariables),M_.endo_names,'tit_tex',M_.endo_names,...
|
M_.endo_names(SelecVariables),M_.endo_names,'tit_tex',M_.endo_names,...
|
||||||
'names2','smooth',[M_.fname '/metropolis'],'_smooth')
|
'names2','smooth',[M_.fname '/metropolis'],'_smooth')
|
||||||
end
|
end
|
||||||
|
|
||||||
if options_.forecast
|
if options_.forecast
|
||||||
pm3(endo_nbr,horizon+maxlag,ifil6,B,'Forecasted variables (mean)',...
|
pm3(endo_nbr,horizon+maxlag,ifil6,B,'Forecasted variables (mean)',...
|
||||||
M_.endo_names(SelecVariables),M_.endo_names,'tit_tex',M_.endo_names,...
|
M_.endo_names(SelecVariables),M_.endo_names,'tit_tex',M_.endo_names,...
|
||||||
'names2','smooth',[M_.fname '/metropolis'],'_forc_mean')
|
'names2','smooth',[M_.fname '/metropolis'],'_forc_mean')
|
||||||
pm3(endo_nbr,horizon+maxlag,ifil6,B,'Forecasted variables (total)',...
|
pm3(endo_nbr,horizon+maxlag,ifil6,B,'Forecasted variables (total)',...
|
||||||
M_.endo_names(SelecVariables),M_.endo_names,'tit_tex',M_.endo_names,...
|
M_.endo_names(SelecVariables),M_.endo_names,'tit_tex',M_.endo_names,...
|
||||||
'names2','smooth',[M_.fname '/metropolis'],'_forc_total')
|
'names2','smooth',[M_.fname '/metropolis'],'_forc_total')
|
||||||
end
|
end
|
||||||
|
|
|
||||||
|
|
@ -71,27 +71,27 @@ else
|
||||||
end
|
end
|
||||||
DirectoryName = CheckPath('Output');
|
DirectoryName = CheckPath('Output');
|
||||||
if strcmpi(type,'posterior')
|
if strcmpi(type,'posterior')
|
||||||
MhDirectoryName = CheckPath('metropolis');
|
MhDirectoryName = CheckPath('metropolis');
|
||||||
elseif strcmpi(type,'gsa')
|
elseif strcmpi(type,'gsa')
|
||||||
MhDirectoryName = CheckPath('GSA');
|
MhDirectoryName = CheckPath('GSA');
|
||||||
else
|
else
|
||||||
MhDirectoryName = CheckPath('prior');
|
MhDirectoryName = CheckPath('prior');
|
||||||
end
|
end
|
||||||
if strcmpi(type,'posterior')
|
if strcmpi(type,'posterior')
|
||||||
load([ MhDirectoryName '/' M_.fname '_mh_history.mat'])
|
load([ MhDirectoryName '/' M_.fname '_mh_history.mat'])
|
||||||
TotalNumberOfMhDraws = sum(record.MhDraws(:,1));
|
TotalNumberOfMhDraws = sum(record.MhDraws(:,1));
|
||||||
NumberOfDraws = TotalNumberOfMhDraws-floor(options_.mh_drop*TotalNumberOfMhDraws);
|
NumberOfDraws = TotalNumberOfMhDraws-floor(options_.mh_drop*TotalNumberOfMhDraws);
|
||||||
elseif strcmpi(type,'gsa')
|
elseif strcmpi(type,'gsa')
|
||||||
load([ MhDirectoryName '/' M_.fname '_prior.mat'],'lpmat0','lpmat','istable')
|
load([ MhDirectoryName '/' M_.fname '_prior.mat'],'lpmat0','lpmat','istable')
|
||||||
x=[lpmat0(istable,:) lpmat(istable,:)];
|
x=[lpmat0(istable,:) lpmat(istable,:)];
|
||||||
clear lpmat istable
|
clear lpmat istable
|
||||||
NumberOfDraws=size(x,1);
|
NumberOfDraws=size(x,1);
|
||||||
B=NumberOfDraws; options_.B = B;
|
B=NumberOfDraws; options_.B = B;
|
||||||
else% type = 'prior'
|
else% type = 'prior'
|
||||||
NumberOfDraws = 500;
|
NumberOfDraws = 500;
|
||||||
end
|
end
|
||||||
if ~strcmpi(type,'gsa')
|
if ~strcmpi(type,'gsa')
|
||||||
B = min([round(.5*NumberOfDraws),500]); options_.B = B;
|
B = min([round(.5*NumberOfDraws),500]); options_.B = B;
|
||||||
end
|
end
|
||||||
try delete([MhDirectoryName '/' M_.fname '_irf_dsge*.mat'])
|
try delete([MhDirectoryName '/' M_.fname '_irf_dsge*.mat'])
|
||||||
catch disp('No _IRFs (dsge) files to be deleted!')
|
catch disp('No _IRFs (dsge) files to be deleted!')
|
||||||
|
|
@ -106,22 +106,22 @@ NumberOfIRFfiles_dsge = 1;
|
||||||
NumberOfIRFfiles_dsgevar = 1;
|
NumberOfIRFfiles_dsgevar = 1;
|
||||||
ifil2 = 1;
|
ifil2 = 1;
|
||||||
if strcmpi(type,'posterior')
|
if strcmpi(type,'posterior')
|
||||||
h = waitbar(0,'Bayesian (posterior) IRFs...');
|
h = waitbar(0,'Bayesian (posterior) IRFs...');
|
||||||
elseif strcmpi(type,'gsa')
|
elseif strcmpi(type,'gsa')
|
||||||
h = waitbar(0,'GSA (prior) IRFs...');
|
h = waitbar(0,'GSA (prior) IRFs...');
|
||||||
else
|
else
|
||||||
h = waitbar(0,'Bayesian (prior) IRFs...');
|
h = waitbar(0,'Bayesian (prior) IRFs...');
|
||||||
end
|
end
|
||||||
% Create arrays
|
% Create arrays
|
||||||
if B <= MAX_nruns
|
if B <= MAX_nruns
|
||||||
stock_param = zeros(B, npar);
|
stock_param = zeros(B, npar);
|
||||||
else
|
else
|
||||||
stock_param = zeros(MAX_nruns, npar);
|
stock_param = zeros(MAX_nruns, npar);
|
||||||
end
|
end
|
||||||
if B >= MAX_nirfs_dsge
|
if B >= MAX_nirfs_dsge
|
||||||
stock_irf_dsge = zeros(options_.irf,nvar,M_.exo_nbr,MAX_nirfs_dsge);
|
stock_irf_dsge = zeros(options_.irf,nvar,M_.exo_nbr,MAX_nirfs_dsge);
|
||||||
else
|
else
|
||||||
stock_irf_dsge = zeros(options_.irf,nvar,M_.exo_nbr,B);
|
stock_irf_dsge = zeros(options_.irf,nvar,M_.exo_nbr,B);
|
||||||
end
|
end
|
||||||
if MAX_nirfs_dsgevar
|
if MAX_nirfs_dsgevar
|
||||||
if B >= MAX_nirfs_dsgevar
|
if B >= MAX_nirfs_dsgevar
|
||||||
|
|
@ -204,14 +204,14 @@ while b<=B
|
||||||
SIGMA_inv_upper_chol);
|
SIGMA_inv_upper_chol);
|
||||||
% draw from the conditional posterior of PHI
|
% draw from the conditional posterior of PHI
|
||||||
PHI_draw = rand_matrix_normal(NumberOfParametersPerEquation,nvobs, PHI, ...
|
PHI_draw = rand_matrix_normal(NumberOfParametersPerEquation,nvobs, PHI, ...
|
||||||
chol(SIGMAu_draw)', chol(iXX)');
|
chol(SIGMAu_draw)', chol(iXX)');
|
||||||
Companion_matrix(1:nvobs,:) = transpose(PHI_draw(1:NumberOfLagsTimesNvobs,:));
|
Companion_matrix(1:nvobs,:) = transpose(PHI_draw(1:NumberOfLagsTimesNvobs,:));
|
||||||
% Check for stationarity
|
% Check for stationarity
|
||||||
explosive_var = any(abs(eig(Companion_matrix))>1.000000001);
|
explosive_var = any(abs(eig(Companion_matrix))>1.000000001);
|
||||||
end
|
end
|
||||||
% Get the mean
|
% Get the mean
|
||||||
% $$$ if options_.noconstant
|
% $$$ if options_.noconstant
|
||||||
mu = zeros(1,nvobs);
|
mu = zeros(1,nvobs);
|
||||||
% $$$ else
|
% $$$ else
|
||||||
% $$$ AA = eye(nvobs);
|
% $$$ AA = eye(nvobs);
|
||||||
% $$$ for lag=1:NumberOfLags
|
% $$$ for lag=1:NumberOfLags
|
||||||
|
|
@ -219,7 +219,7 @@ while b<=B
|
||||||
% $$$ end
|
% $$$ end
|
||||||
% $$$ mu = transpose(AA\transpose(PHI_draw(end,:)));
|
% $$$ mu = transpose(AA\transpose(PHI_draw(end,:)));
|
||||||
% $$$ end
|
% $$$ end
|
||||||
% Get rotation
|
% Get rotation
|
||||||
if dsge_prior_weight > 0
|
if dsge_prior_weight > 0
|
||||||
Atheta(oo_.dr.order_var,M_.exo_names_orig_ord) = oo_.dr.ghu*sqrt(M_.Sigma_e);
|
Atheta(oo_.dr.order_var,M_.exo_names_orig_ord) = oo_.dr.ghu*sqrt(M_.Sigma_e);
|
||||||
A0 = Atheta(bayestopt_.mfys,:);
|
A0 = Atheta(bayestopt_.mfys,:);
|
||||||
|
|
@ -282,7 +282,7 @@ while b<=B
|
||||||
waitbar(b/B,h);
|
waitbar(b/B,h);
|
||||||
end
|
end
|
||||||
if nosaddle
|
if nosaddle
|
||||||
disp(['PosteriorIRF :: Percentage of discarded posterior draws = ' num2str(nosaddle/(B+nosaddle))])
|
disp(['PosteriorIRF :: Percentage of discarded posterior draws = ' num2str(nosaddle/(B+nosaddle))])
|
||||||
end
|
end
|
||||||
close(h);
|
close(h);
|
||||||
|
|
||||||
|
|
@ -292,7 +292,7 @@ if MAX_nirfs_dsgevar
|
||||||
end
|
end
|
||||||
|
|
||||||
if strcmpi(type,'gsa')
|
if strcmpi(type,'gsa')
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
|
|
||||||
IRF_DSGEs = dir([MhDirectoryName '/' M_.fname '_IRF_DSGEs*.mat']);
|
IRF_DSGEs = dir([MhDirectoryName '/' M_.fname '_IRF_DSGEs*.mat']);
|
||||||
|
|
@ -310,10 +310,10 @@ DistribIRF = zeros(options_.irf,9,nvar,M_.exo_nbr);
|
||||||
HPDIRF = zeros(options_.irf,2,nvar,M_.exo_nbr);
|
HPDIRF = zeros(options_.irf,2,nvar,M_.exo_nbr);
|
||||||
|
|
||||||
if options_.TeX
|
if options_.TeX
|
||||||
varlist_TeX = [];
|
varlist_TeX = [];
|
||||||
for i=1:nvar
|
for i=1:nvar
|
||||||
varlist_TeX = strvcat(varlist_TeX,M_.endo_names_tex(IndxVariables(i),:));
|
varlist_TeX = strvcat(varlist_TeX,M_.endo_names_tex(IndxVariables(i),:));
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
||||||
fprintf('MH: Posterior (dsge) IRFs...\n');
|
fprintf('MH: Posterior (dsge) IRFs...\n');
|
||||||
|
|
@ -321,31 +321,31 @@ tit(M_.exo_names_orig_ord,:) = M_.exo_names;
|
||||||
kdx = 0;
|
kdx = 0;
|
||||||
|
|
||||||
for file = 1:NumberOfIRFfiles_dsge
|
for file = 1:NumberOfIRFfiles_dsge
|
||||||
load([MhDirectoryName '/' M_.fname '_IRF_DSGEs' int2str(file) '.mat']);
|
load([MhDirectoryName '/' M_.fname '_IRF_DSGEs' int2str(file) '.mat']);
|
||||||
for i = 1:M_.exo_nbr
|
for i = 1:M_.exo_nbr
|
||||||
for j = 1:nvar
|
for j = 1:nvar
|
||||||
for k = 1:size(STOCK_IRF_DSGE,1)
|
for k = 1:size(STOCK_IRF_DSGE,1)
|
||||||
kk = k+kdx;
|
kk = k+kdx;
|
||||||
[MeanIRF(kk,j,i),MedianIRF(kk,j,i),VarIRF(kk,j,i),HPDIRF(kk,:,j,i),...
|
[MeanIRF(kk,j,i),MedianIRF(kk,j,i),VarIRF(kk,j,i),HPDIRF(kk,:,j,i),...
|
||||||
DistribIRF(kk,:,j,i)] = posterior_moments(squeeze(STOCK_IRF_DSGE(k,j,i,:)),0,options_.mh_conf_sig);
|
DistribIRF(kk,:,j,i)] = posterior_moments(squeeze(STOCK_IRF_DSGE(k,j,i,:)),0,options_.mh_conf_sig);
|
||||||
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
kdx = kdx + size(STOCK_IRF_DSGE,1);
|
||||||
kdx = kdx + size(STOCK_IRF_DSGE,1);
|
|
||||||
end
|
end
|
||||||
|
|
||||||
clear STOCK_IRF_DSGE;
|
clear STOCK_IRF_DSGE;
|
||||||
|
|
||||||
for i = 1:M_.exo_nbr
|
for i = 1:M_.exo_nbr
|
||||||
for j = 1:nvar
|
for j = 1:nvar
|
||||||
name = [deblank(M_.endo_names(IndxVariables(j),:)) '_' deblank(tit(i,:))];
|
name = [deblank(M_.endo_names(IndxVariables(j),:)) '_' deblank(tit(i,:))];
|
||||||
eval(['oo_.PosteriorIRF.dsge.Mean.' name ' = MeanIRF(:,j,i);']);
|
eval(['oo_.PosteriorIRF.dsge.Mean.' name ' = MeanIRF(:,j,i);']);
|
||||||
eval(['oo_.PosteriorIRF.dsge.Median.' name ' = MedianIRF(:,j,i);']);
|
eval(['oo_.PosteriorIRF.dsge.Median.' name ' = MedianIRF(:,j,i);']);
|
||||||
eval(['oo_.PosteriorIRF.dsge.Var.' name ' = VarIRF(:,j,i);']);
|
eval(['oo_.PosteriorIRF.dsge.Var.' name ' = VarIRF(:,j,i);']);
|
||||||
eval(['oo_.PosteriorIRF.dsge.Distribution.' name ' = DistribIRF(:,:,j,i);']);
|
eval(['oo_.PosteriorIRF.dsge.Distribution.' name ' = DistribIRF(:,:,j,i);']);
|
||||||
eval(['oo_.PosteriorIRF.dsge.HPDinf.' name ' = HPDIRF(:,1,j,i);']);
|
eval(['oo_.PosteriorIRF.dsge.HPDinf.' name ' = HPDIRF(:,1,j,i);']);
|
||||||
eval(['oo_.PosteriorIRF.dsge.HPDsup.' name ' = HPDIRF(:,2,j,i);']);
|
eval(['oo_.PosteriorIRF.dsge.HPDsup.' name ' = HPDIRF(:,2,j,i);']);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
||||||
|
|
||||||
|
|
@ -389,111 +389,111 @@ end
|
||||||
%% Finally I build the plots.
|
%% Finally I build the plots.
|
||||||
%%
|
%%
|
||||||
if options_.TeX
|
if options_.TeX
|
||||||
fidTeX = fopen([DirectoryName '/' M_.fname '_BayesianIRF.TeX'],'w');
|
fidTeX = fopen([DirectoryName '/' M_.fname '_BayesianIRF.TeX'],'w');
|
||||||
fprintf(fidTeX,'%% TeX eps-loader file generated by PosteriorIRF.m (Dynare).\n');
|
fprintf(fidTeX,'%% TeX eps-loader file generated by PosteriorIRF.m (Dynare).\n');
|
||||||
fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
|
fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
|
||||||
fprintf(fidTeX,' \n');
|
fprintf(fidTeX,' \n');
|
||||||
titTeX(M_.exo_names_orig_ord,:) = M_.exo_names_tex;
|
titTeX(M_.exo_names_orig_ord,:) = M_.exo_names_tex;
|
||||||
end
|
end
|
||||||
%%
|
%%
|
||||||
subplotnum = 0;
|
subplotnum = 0;
|
||||||
for i=1:M_.exo_nbr
|
for i=1:M_.exo_nbr
|
||||||
NAMES = [];
|
NAMES = [];
|
||||||
if options_.TeX
|
if options_.TeX
|
||||||
TEXNAMES = [];
|
TEXNAMES = [];
|
||||||
end
|
|
||||||
figunumber = 0;
|
|
||||||
for j=1:nvar
|
|
||||||
if max(abs(MeanIRF(:,j,i))) > 10^(-6)
|
|
||||||
subplotnum = subplotnum+1;
|
|
||||||
if options_.nograph
|
|
||||||
if subplotnum == 1 & options_.relative_irf
|
|
||||||
hh = figure('Name',['Relative response to orthogonalized shock to ' tit(i,:)],'Visible','off');
|
|
||||||
elseif subplotnum == 1 & ~options_.relative_irf
|
|
||||||
hh = figure('Name',['Orthogonalized shock to ' tit(i,:)],'Visible','off');
|
|
||||||
end
|
|
||||||
else
|
|
||||||
if subplotnum == 1 & options_.relative_irf
|
|
||||||
hh = figure('Name',['Relative response to orthogonalized shock to ' tit(i,:)]);
|
|
||||||
elseif subplotnum == 1 & ~options_.relative_irf
|
|
||||||
hh = figure('Name',['Orthogonalized shock to ' tit(i,:)]);
|
|
||||||
end
|
|
||||||
end
|
|
||||||
set(0,'CurrentFigure',hh)
|
|
||||||
subplot(nn,nn,subplotnum);
|
|
||||||
if ~MAX_nirfs_dsgevar
|
|
||||||
h1 = area(1:options_.irf,HPDIRF(:,2,j,i));
|
|
||||||
set(h1,'FaceColor',[.9 .9 .9]);
|
|
||||||
set(h1,'BaseValue',min(HPDIRF(:,1,j,i)));
|
|
||||||
hold on
|
|
||||||
h2 = area(1:options_.irf,HPDIRF(:,1,j,i),'FaceColor',[1 1 1],'BaseValue',min(HPDIRF(:,1,j,i)));
|
|
||||||
set(h2,'FaceColor',[1 1 1]);
|
|
||||||
set(h2,'BaseValue',min(HPDIRF(:,1,j,i)));
|
|
||||||
plot(1:options_.irf,MeanIRF(:,j,i),'-k','linewidth',3)
|
|
||||||
% plot([1 options_.irf],[0 0],'-r','linewidth',0.5);
|
|
||||||
box on
|
|
||||||
axis tight
|
|
||||||
xlim([1 options_.irf]);
|
|
||||||
hold off
|
|
||||||
else
|
|
||||||
h1 = area(1:options_.irf,HPDIRF(:,2,j,i));
|
|
||||||
set(h1,'FaceColor',[.9 .9 .9]);
|
|
||||||
set(h1,'BaseValue',min([min(HPDIRF(:,1,j,i)),min(HPDIRFdsgevar(:,1,j,i))]));
|
|
||||||
hold on;
|
|
||||||
h2 = area(1:options_.irf,HPDIRF(:,1,j,i));
|
|
||||||
set(h2,'FaceColor',[1 1 1]);
|
|
||||||
set(h2,'BaseValue',min([min(HPDIRF(:,1,j,i)),min(HPDIRFdsgevar(:,1,j,i))]));
|
|
||||||
plot(1:options_.irf,MeanIRF(:,j,i),'-k','linewidth',3)
|
|
||||||
% plot([1 options_.irf],[0 0],'-r','linewidth',0.5);
|
|
||||||
plot(1:options_.irf,MeanIRFdsgevar(:,j,i),'--k','linewidth',2)
|
|
||||||
plot(1:options_.irf,HPDIRFdsgevar(:,1,j,i),'--k','linewidth',1)
|
|
||||||
plot(1:options_.irf,HPDIRFdsgevar(:,2,j,i),'--k','linewidth',1)
|
|
||||||
box on
|
|
||||||
axis tight
|
|
||||||
xlim([1 options_.irf]);
|
|
||||||
hold off
|
|
||||||
end
|
|
||||||
name = deblank(varlist(j,:));
|
|
||||||
NAMES = strvcat(NAMES,name);
|
|
||||||
if options_.TeX
|
|
||||||
texname = deblank(varlist_TeX(j,:));
|
|
||||||
TEXNAMES = strvcat(TEXNAMES,['$' texname '$']);
|
|
||||||
end
|
|
||||||
title(name,'Interpreter','none')
|
|
||||||
end
|
end
|
||||||
if subplotnum == MaxNumberOfPlotPerFigure | (j == nvar & subplotnum> 0)
|
figunumber = 0;
|
||||||
figunumber = figunumber+1;
|
for j=1:nvar
|
||||||
set(hh,'visible','on')
|
if max(abs(MeanIRF(:,j,i))) > 10^(-6)
|
||||||
eval(['print -depsc2 ' DirectoryName '/' M_.fname '_Bayesian_IRF_' deblank(tit(i,:)) '_' int2str(figunumber) '.eps']);
|
subplotnum = subplotnum+1;
|
||||||
if ~exist('OCTAVE_VERSION')
|
if options_.nograph
|
||||||
eval(['print -dpdf ' DirectoryName '/' M_.fname '_Bayesian_IRF_' deblank(tit(i,:)) '_' int2str(figunumber)]);
|
if subplotnum == 1 & options_.relative_irf
|
||||||
saveas(hh,[DirectoryName '/' M_.fname '_Bayesian_IRF_' deblank(tit(i,:)) '_' int2str(figunumber) '.fig']);
|
hh = figure('Name',['Relative response to orthogonalized shock to ' tit(i,:)],'Visible','off');
|
||||||
end
|
elseif subplotnum == 1 & ~options_.relative_irf
|
||||||
set(hh,'visible','off')
|
hh = figure('Name',['Orthogonalized shock to ' tit(i,:)],'Visible','off');
|
||||||
if options_.nograph, close(hh), end
|
end
|
||||||
if options_.TeX
|
else
|
||||||
fprintf(fidTeX,'\\begin{figure}[H]\n');
|
if subplotnum == 1 & options_.relative_irf
|
||||||
for jj = 1:size(TEXNAMES,1)
|
hh = figure('Name',['Relative response to orthogonalized shock to ' tit(i,:)]);
|
||||||
fprintf(fidTeX,['\\psfrag{%s}[1][][0.5][0]{%s}\n'],deblank(NAMES(jj,:)),deblank(TEXNAMES(jj,:)));
|
elseif subplotnum == 1 & ~options_.relative_irf
|
||||||
end
|
hh = figure('Name',['Orthogonalized shock to ' tit(i,:)]);
|
||||||
fprintf(fidTeX,'\\centering \n');
|
end
|
||||||
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_Bayesian_IRF_%s}\n',M_.fname,deblank(tit(i,:)));
|
end
|
||||||
if options_.relative_irf
|
set(0,'CurrentFigure',hh)
|
||||||
fprintf(fidTeX,['\\caption{Bayesian relative IRF.}']);
|
subplot(nn,nn,subplotnum);
|
||||||
else
|
if ~MAX_nirfs_dsgevar
|
||||||
fprintf(fidTeX,'\\caption{Bayesian IRF.}');
|
h1 = area(1:options_.irf,HPDIRF(:,2,j,i));
|
||||||
|
set(h1,'FaceColor',[.9 .9 .9]);
|
||||||
|
set(h1,'BaseValue',min(HPDIRF(:,1,j,i)));
|
||||||
|
hold on
|
||||||
|
h2 = area(1:options_.irf,HPDIRF(:,1,j,i),'FaceColor',[1 1 1],'BaseValue',min(HPDIRF(:,1,j,i)));
|
||||||
|
set(h2,'FaceColor',[1 1 1]);
|
||||||
|
set(h2,'BaseValue',min(HPDIRF(:,1,j,i)));
|
||||||
|
plot(1:options_.irf,MeanIRF(:,j,i),'-k','linewidth',3)
|
||||||
|
% plot([1 options_.irf],[0 0],'-r','linewidth',0.5);
|
||||||
|
box on
|
||||||
|
axis tight
|
||||||
|
xlim([1 options_.irf]);
|
||||||
|
hold off
|
||||||
|
else
|
||||||
|
h1 = area(1:options_.irf,HPDIRF(:,2,j,i));
|
||||||
|
set(h1,'FaceColor',[.9 .9 .9]);
|
||||||
|
set(h1,'BaseValue',min([min(HPDIRF(:,1,j,i)),min(HPDIRFdsgevar(:,1,j,i))]));
|
||||||
|
hold on;
|
||||||
|
h2 = area(1:options_.irf,HPDIRF(:,1,j,i));
|
||||||
|
set(h2,'FaceColor',[1 1 1]);
|
||||||
|
set(h2,'BaseValue',min([min(HPDIRF(:,1,j,i)),min(HPDIRFdsgevar(:,1,j,i))]));
|
||||||
|
plot(1:options_.irf,MeanIRF(:,j,i),'-k','linewidth',3)
|
||||||
|
% plot([1 options_.irf],[0 0],'-r','linewidth',0.5);
|
||||||
|
plot(1:options_.irf,MeanIRFdsgevar(:,j,i),'--k','linewidth',2)
|
||||||
|
plot(1:options_.irf,HPDIRFdsgevar(:,1,j,i),'--k','linewidth',1)
|
||||||
|
plot(1:options_.irf,HPDIRFdsgevar(:,2,j,i),'--k','linewidth',1)
|
||||||
|
box on
|
||||||
|
axis tight
|
||||||
|
xlim([1 options_.irf]);
|
||||||
|
hold off
|
||||||
|
end
|
||||||
|
name = deblank(varlist(j,:));
|
||||||
|
NAMES = strvcat(NAMES,name);
|
||||||
|
if options_.TeX
|
||||||
|
texname = deblank(varlist_TeX(j,:));
|
||||||
|
TEXNAMES = strvcat(TEXNAMES,['$' texname '$']);
|
||||||
|
end
|
||||||
|
title(name,'Interpreter','none')
|
||||||
end
|
end
|
||||||
fprintf(fidTeX,'\\label{Fig:BayesianIRF:%s}\n',deblank(tit(i,:)));
|
if subplotnum == MaxNumberOfPlotPerFigure | (j == nvar & subplotnum> 0)
|
||||||
fprintf(fidTeX,'\\end{figure}\n');
|
figunumber = figunumber+1;
|
||||||
fprintf(fidTeX,' \n');
|
set(hh,'visible','on')
|
||||||
end
|
eval(['print -depsc2 ' DirectoryName '/' M_.fname '_Bayesian_IRF_' deblank(tit(i,:)) '_' int2str(figunumber) '.eps']);
|
||||||
subplotnum = 0;
|
if ~exist('OCTAVE_VERSION')
|
||||||
end
|
eval(['print -dpdf ' DirectoryName '/' M_.fname '_Bayesian_IRF_' deblank(tit(i,:)) '_' int2str(figunumber)]);
|
||||||
end% loop over selected endo_var
|
saveas(hh,[DirectoryName '/' M_.fname '_Bayesian_IRF_' deblank(tit(i,:)) '_' int2str(figunumber) '.fig']);
|
||||||
|
end
|
||||||
|
set(hh,'visible','off')
|
||||||
|
if options_.nograph, close(hh), end
|
||||||
|
if options_.TeX
|
||||||
|
fprintf(fidTeX,'\\begin{figure}[H]\n');
|
||||||
|
for jj = 1:size(TEXNAMES,1)
|
||||||
|
fprintf(fidTeX,['\\psfrag{%s}[1][][0.5][0]{%s}\n'],deblank(NAMES(jj,:)),deblank(TEXNAMES(jj,:)));
|
||||||
|
end
|
||||||
|
fprintf(fidTeX,'\\centering \n');
|
||||||
|
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_Bayesian_IRF_%s}\n',M_.fname,deblank(tit(i,:)));
|
||||||
|
if options_.relative_irf
|
||||||
|
fprintf(fidTeX,['\\caption{Bayesian relative IRF.}']);
|
||||||
|
else
|
||||||
|
fprintf(fidTeX,'\\caption{Bayesian IRF.}');
|
||||||
|
end
|
||||||
|
fprintf(fidTeX,'\\label{Fig:BayesianIRF:%s}\n',deblank(tit(i,:)));
|
||||||
|
fprintf(fidTeX,'\\end{figure}\n');
|
||||||
|
fprintf(fidTeX,' \n');
|
||||||
|
end
|
||||||
|
subplotnum = 0;
|
||||||
|
end
|
||||||
|
end% loop over selected endo_var
|
||||||
end% loop over exo_var
|
end% loop over exo_var
|
||||||
%%
|
%%
|
||||||
if options_.TeX
|
if options_.TeX
|
||||||
fprintf(fidTeX,'%% End of TeX file.\n');
|
fprintf(fidTeX,'%% End of TeX file.\n');
|
||||||
fclose(fidTeX);
|
fclose(fidTeX);
|
||||||
end
|
end
|
||||||
fprintf('MH: Posterior IRFs, done!\n');
|
fprintf('MH: Posterior IRFs, done!\n');
|
||||||
|
|
@ -1,183 +1,183 @@
|
||||||
function ReshapeMatFiles(type, type2)
|
function ReshapeMatFiles(type, type2)
|
||||||
% function ReshapeMatFiles(type, type2)
|
% function ReshapeMatFiles(type, type2)
|
||||||
% Reshapes and sorts (along the mcmc simulations) the mat files generated by DYNARE.
|
% Reshapes and sorts (along the mcmc simulations) the mat files generated by DYNARE.
|
||||||
% 4D-arrays are splitted along the first dimension.
|
% 4D-arrays are splitted along the first dimension.
|
||||||
% 3D-arrays are splitted along the second dimension.
|
% 3D-arrays are splitted along the second dimension.
|
||||||
%
|
%
|
||||||
% INPUTS:
|
% INPUTS:
|
||||||
% type: statistics type in the repertory:
|
% type: statistics type in the repertory:
|
||||||
% dgse
|
% dgse
|
||||||
% irf_bvardsge
|
% irf_bvardsge
|
||||||
% smooth
|
% smooth
|
||||||
% filter
|
% filter
|
||||||
% error
|
% error
|
||||||
% innov
|
% innov
|
||||||
% forcst
|
% forcst
|
||||||
% forcst1
|
% forcst1
|
||||||
% type2: analysis type:
|
% type2: analysis type:
|
||||||
% posterior
|
% posterior
|
||||||
% gsa
|
% gsa
|
||||||
% prior
|
% prior
|
||||||
%
|
%
|
||||||
% OUTPUTS:
|
% OUTPUTS:
|
||||||
% none
|
% none
|
||||||
%
|
%
|
||||||
% SPECIAL REQUIREMENTS
|
% SPECIAL REQUIREMENTS
|
||||||
% none
|
% none
|
||||||
|
|
||||||
% Copyright (C) 2003-2007 Dynare Team
|
% Copyright (C) 2003-2007 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
% Dynare is free software: you can redistribute it and/or modify
|
||||||
% it under the terms of the GNU General Public License as published by
|
% it under the terms of the GNU General Public License as published by
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
% the Free Software Foundation, either version 3 of the License, or
|
||||||
% (at your option) any later version.
|
% (at your option) any later version.
|
||||||
%
|
%
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
% Dynare is distributed in the hope that it will be useful,
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
global M_ options_
|
global M_ options_
|
||||||
|
|
||||||
if nargin==1,
|
if nargin==1,
|
||||||
MhDirectoryName = [ CheckPath('metropolis') '/' ];
|
MhDirectoryName = [ CheckPath('metropolis') '/' ];
|
||||||
else
|
else
|
||||||
if strcmpi(type2,'posterior')
|
if strcmpi(type2,'posterior')
|
||||||
MhDirectoryName = [CheckPath('metropolis') '/' ];
|
MhDirectoryName = [CheckPath('metropolis') '/' ];
|
||||||
elseif strcmpi(type2,'gsa')
|
elseif strcmpi(type2,'gsa')
|
||||||
if options_.opt_gsa.morris==1,
|
if options_.opt_gsa.morris==1,
|
||||||
MhDirectoryName = [CheckPath('GSA\SCREEN') '/' ];
|
MhDirectoryName = [CheckPath('GSA\SCREEN') '/' ];
|
||||||
elseif options_.opt_gsa.morris==2,
|
elseif options_.opt_gsa.morris==2,
|
||||||
MhDirectoryName = [CheckPath('GSA\IDENTIF') '/' ];
|
MhDirectoryName = [CheckPath('GSA\IDENTIF') '/' ];
|
||||||
else
|
else
|
||||||
MhDirectoryName = [CheckPath('GSA') '/' ];
|
MhDirectoryName = [CheckPath('GSA') '/' ];
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
MhDirectoryName = [CheckPath('prior') '/' ];
|
MhDirectoryName = [CheckPath('prior') '/' ];
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
switch type
|
switch type
|
||||||
case 'irf_dsge'
|
case 'irf_dsge'
|
||||||
CAPtype = 'IRF_DSGE';
|
CAPtype = 'IRF_DSGE';
|
||||||
TYPEsize = [ options_.irf , size(options_.varlist,1) , M_.exo_nbr ];
|
TYPEsize = [ options_.irf , size(options_.varlist,1) , M_.exo_nbr ];
|
||||||
TYPEarray = 4;
|
TYPEarray = 4;
|
||||||
case 'irf_bvardsge'
|
case 'irf_bvardsge'
|
||||||
CAPtype = 'IRF_BVARDSGE';
|
CAPtype = 'IRF_BVARDSGE';
|
||||||
TYPEsize = [ options_.irf , size(options_.varobs,1) , M_.exo_nbr ];
|
TYPEsize = [ options_.irf , size(options_.varobs,1) , M_.exo_nbr ];
|
||||||
TYPEarray = 4;
|
TYPEarray = 4;
|
||||||
case 'smooth'
|
case 'smooth'
|
||||||
CAPtype = 'SMOOTH';
|
CAPtype = 'SMOOTH';
|
||||||
TYPEsize = [ M_.endo_nbr , options_.nobs ];
|
TYPEsize = [ M_.endo_nbr , options_.nobs ];
|
||||||
TYPEarray = 3;
|
TYPEarray = 3;
|
||||||
case 'filter'
|
case 'filter'
|
||||||
CAPtype = 'FILTER';
|
CAPtype = 'FILTER';
|
||||||
TYPEsize = [ M_.endo_nbr , options_.nobs + 1 ];% TO BE CHECKED!
|
TYPEsize = [ M_.endo_nbr , options_.nobs + 1 ];% TO BE CHECKED!
|
||||||
TYPEarray = 3;
|
TYPEarray = 3;
|
||||||
case 'error'
|
case 'error'
|
||||||
CAPtype = 'ERROR';
|
CAPtype = 'ERROR';
|
||||||
TYPEsize = [ size(options_.varobs,1) , options_.nobs ];
|
TYPEsize = [ size(options_.varobs,1) , options_.nobs ];
|
||||||
TYPEarray = 3;
|
TYPEarray = 3;
|
||||||
case 'innov'
|
case 'innov'
|
||||||
CAPtype = 'INNOV';
|
CAPtype = 'INNOV';
|
||||||
TYPEsize = [ M_.exo_nbr , options_.nobs ];
|
TYPEsize = [ M_.exo_nbr , options_.nobs ];
|
||||||
TYPEarray = 3;
|
TYPEarray = 3;
|
||||||
case 'forcst'
|
case 'forcst'
|
||||||
CAPtype = 'FORCST';
|
CAPtype = 'FORCST';
|
||||||
TYPEsize = [ M_.endo_nbr , options_.forecast ];
|
TYPEsize = [ M_.endo_nbr , options_.forecast ];
|
||||||
TYPEarray = 3;
|
TYPEarray = 3;
|
||||||
case 'forcst1'
|
case 'forcst1'
|
||||||
CAPtype = 'FORCST1';
|
CAPtype = 'FORCST1';
|
||||||
TYPEsize = [ M_.endo_nbr , options_.forecast ];
|
TYPEsize = [ M_.endo_nbr , options_.forecast ];
|
||||||
TYPEarray = 3;
|
TYPEarray = 3;
|
||||||
otherwise
|
otherwise
|
||||||
disp('ReshapeMatFiles :: Unknown argument!')
|
disp('ReshapeMatFiles :: Unknown argument!')
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
|
|
||||||
TYPEfiles = dir([MhDirectoryName M_.fname '_' type '*.mat']);
|
TYPEfiles = dir([MhDirectoryName M_.fname '_' type '*.mat']);
|
||||||
NumberOfTYPEfiles = length(TYPEfiles);
|
NumberOfTYPEfiles = length(TYPEfiles);
|
||||||
B = options_.B;
|
B = options_.B;
|
||||||
|
|
||||||
switch TYPEarray
|
switch TYPEarray
|
||||||
case 4
|
case 4
|
||||||
if NumberOfTYPEfiles > 1
|
if NumberOfTYPEfiles > 1
|
||||||
NumberOfPeriodsPerTYPEfiles = ceil(TYPEsize(1)/NumberOfTYPEfiles);
|
NumberOfPeriodsPerTYPEfiles = ceil(TYPEsize(1)/NumberOfTYPEfiles);
|
||||||
foffset = NumberOfTYPEfiles-floor(TYPEsize(1)/NumberOfPeriodsPerTYPEfiles);
|
foffset = NumberOfTYPEfiles-floor(TYPEsize(1)/NumberOfPeriodsPerTYPEfiles);
|
||||||
reste = TYPEsize(1)-NumberOfPeriodsPerTYPEfiles*(NumberOfTYPEfiles-foffset);
|
reste = TYPEsize(1)-NumberOfPeriodsPerTYPEfiles*(NumberOfTYPEfiles-foffset);
|
||||||
idx = 0;
|
idx = 0;
|
||||||
jdx = 0;
|
jdx = 0;
|
||||||
for f1=1:NumberOfTYPEfiles-foffset
|
for f1=1:NumberOfTYPEfiles-foffset
|
||||||
eval(['STOCK_' CAPtype ' = zeros(NumberOfPeriodsPerTYPEfiles,TYPEsize(2),TYPEsize(3),B);'])
|
eval(['STOCK_' CAPtype ' = zeros(NumberOfPeriodsPerTYPEfiles,TYPEsize(2),TYPEsize(3),B);'])
|
||||||
for f2 = 1:NumberOfTYPEfiles
|
for f2 = 1:NumberOfTYPEfiles
|
||||||
load([MhDirectoryName M_.fname '_' type int2str(f2) '.mat']);
|
load([MhDirectoryName M_.fname '_' type int2str(f2) '.mat']);
|
||||||
eval(['STOCK_' CAPtype '(:,:,:,idx+1:idx+size(stock_' type ',4))=stock_' ...
|
eval(['STOCK_' CAPtype '(:,:,:,idx+1:idx+size(stock_' type ',4))=stock_' ...
|
||||||
type '(jdx+1:jdx+NumberOfPeriodsPerTYPEfiles,:,:,:);'])
|
type '(jdx+1:jdx+NumberOfPeriodsPerTYPEfiles,:,:,:);'])
|
||||||
eval(['idx = idx + size(stock_' type ',4);'])
|
eval(['idx = idx + size(stock_' type ',4);'])
|
||||||
end
|
end
|
||||||
%eval(['STOCK_' CAPtype ' = sort(STOCK_' CAPtype ',4);'])
|
%eval(['STOCK_' CAPtype ' = sort(STOCK_' CAPtype ',4);'])
|
||||||
save([MhDirectoryName M_.fname '_' CAPtype 's' int2str(f1) '.mat'],['STOCK_' CAPtype]);
|
save([MhDirectoryName M_.fname '_' CAPtype 's' int2str(f1) '.mat'],['STOCK_' CAPtype]);
|
||||||
jdx = jdx + NumberOfPeriodsPerTYPEfiles;
|
jdx = jdx + NumberOfPeriodsPerTYPEfiles;
|
||||||
idx = 0;
|
idx = 0;
|
||||||
end
|
end
|
||||||
if reste
|
if reste
|
||||||
eval(['STOCK_' CAPtype ' = zeros(reste,TYPEsize(2),TYPEsize(3),B);'])
|
eval(['STOCK_' CAPtype ' = zeros(reste,TYPEsize(2),TYPEsize(3),B);'])
|
||||||
for f2 = 1:NumberOfTYPEfiles
|
for f2 = 1:NumberOfTYPEfiles
|
||||||
load([MhDirectoryName M_.fname '_' type int2str(f2) '.mat']);
|
load([MhDirectoryName M_.fname '_' type int2str(f2) '.mat']);
|
||||||
eval(['STOCK_' CAPtype '(:,:,:,idx+1:idx+size(stock_' type ',4))=stock_' type '(jdx+1:jdx+reste,:,:,:);'])
|
eval(['STOCK_' CAPtype '(:,:,:,idx+1:idx+size(stock_' type ',4))=stock_' type '(jdx+1:jdx+reste,:,:,:);'])
|
||||||
eval(['idx = idx + size(stock_' type ',4);'])
|
eval(['idx = idx + size(stock_' type ',4);'])
|
||||||
end
|
end
|
||||||
%eval(['STOCK_' CAPtype ' = sort(STOCK_' CAPtype ',4);'])
|
%eval(['STOCK_' CAPtype ' = sort(STOCK_' CAPtype ',4);'])
|
||||||
save([MhDirectoryName M_.fname '_' CAPtype 's' int2str(NumberOfTYPEfiles-foffset+1) '.mat'],['STOCK_' CAPtype]);
|
save([MhDirectoryName M_.fname '_' CAPtype 's' int2str(NumberOfTYPEfiles-foffset+1) '.mat'],['STOCK_' CAPtype]);
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
load([MhDirectoryName M_.fname '_' type '1.mat']);
|
load([MhDirectoryName M_.fname '_' type '1.mat']);
|
||||||
%eval(['STOCK_' CAPtype ' = sort(stock_' type ',4);'])
|
%eval(['STOCK_' CAPtype ' = sort(stock_' type ',4);'])
|
||||||
eval(['STOCK_' CAPtype ' = stock_' type ';'])
|
eval(['STOCK_' CAPtype ' = stock_' type ';'])
|
||||||
save([MhDirectoryName M_.fname '_' CAPtype 's' int2str(1) '.mat'],['STOCK_' CAPtype ]);
|
save([MhDirectoryName M_.fname '_' CAPtype 's' int2str(1) '.mat'],['STOCK_' CAPtype ]);
|
||||||
end
|
end
|
||||||
% Original file format may be useful in some cases...
|
% Original file format may be useful in some cases...
|
||||||
% for file = 1:NumberOfTYPEfiles
|
% for file = 1:NumberOfTYPEfiles
|
||||||
% delete([MhDirectoryName M_.fname '_' type int2str(file) '.mat'])
|
% delete([MhDirectoryName M_.fname '_' type int2str(file) '.mat'])
|
||||||
% end
|
% end
|
||||||
case 3
|
case 3
|
||||||
if NumberOfTYPEfiles>1
|
if NumberOfTYPEfiles>1
|
||||||
NumberOfPeriodsPerTYPEfiles = ceil( TYPEsize(2)/NumberOfTYPEfiles );
|
NumberOfPeriodsPerTYPEfiles = ceil( TYPEsize(2)/NumberOfTYPEfiles );
|
||||||
reste = TYPEsize(2)-NumberOfPeriodsPerTYPEfiles*(NumberOfTYPEfiles-1);
|
reste = TYPEsize(2)-NumberOfPeriodsPerTYPEfiles*(NumberOfTYPEfiles-1);
|
||||||
idx = 0;
|
idx = 0;
|
||||||
jdx = 0;
|
jdx = 0;
|
||||||
for f1=1:NumberOfTYPEfiles-1
|
for f1=1:NumberOfTYPEfiles-1
|
||||||
eval(['STOCK_' CAPtype ' = zeros(TYPEsize(1),NumberOfPeriodsPerTYPEfiles,B);'])
|
eval(['STOCK_' CAPtype ' = zeros(TYPEsize(1),NumberOfPeriodsPerTYPEfiles,B);'])
|
||||||
for f2 = 1:NumberOfTYPEfiles
|
for f2 = 1:NumberOfTYPEfiles
|
||||||
load([MhDirectoryName M_.fname '_' type int2str(f2) '.mat']);
|
load([MhDirectoryName M_.fname '_' type int2str(f2) '.mat']);
|
||||||
eval(['STOCK_' CAPtype '(:,:,idx+1:idx+size(stock_ ' type ',3))=stock_' type '(:,jdx+1:jdx+NumberOfPeriodsPerTYPEfiles,:);'])
|
eval(['STOCK_' CAPtype '(:,:,idx+1:idx+size(stock_ ' type ',3))=stock_' type '(:,jdx+1:jdx+NumberOfPeriodsPerTYPEfiles,:);'])
|
||||||
eval(['idx = idx + size(stock_' type ',3);'])
|
eval(['idx = idx + size(stock_' type ',3);'])
|
||||||
end
|
end
|
||||||
%eval(['STOCK_' CAPtype ' = sort(STOCK_' CAPtype ',3);'])
|
%eval(['STOCK_' CAPtype ' = sort(STOCK_' CAPtype ',3);'])
|
||||||
save([MhDirectoryName M_.fname '_' CAPtype 's' int2str(f1) '.mat'],['STOCK_' CAPtype]);
|
save([MhDirectoryName M_.fname '_' CAPtype 's' int2str(f1) '.mat'],['STOCK_' CAPtype]);
|
||||||
jdx = jdx + NumberOfPeriodsPerTYPEfiles;
|
jdx = jdx + NumberOfPeriodsPerTYPEfiles;
|
||||||
idx = 0;
|
idx = 0;
|
||||||
end
|
end
|
||||||
eval(['STOCK_' CAPtype ' = zeros(TYPEsize(1),reste,B);'])
|
eval(['STOCK_' CAPtype ' = zeros(TYPEsize(1),reste,B);'])
|
||||||
for f2 = 1:NumberOfTYPEfiles
|
for f2 = 1:NumberOfTYPEfiles
|
||||||
load([MhDirectoryName M_.fname '_' type int2str(f2) '.mat']);
|
load([MhDirectoryName M_.fname '_' type int2str(f2) '.mat']);
|
||||||
eval(['STOCK_' CAPtype '(:,:,idx+1:idx+size(stock_' type ',3))=stock_' type '(:,jdx+1:jdx+reste,:);'])
|
eval(['STOCK_' CAPtype '(:,:,idx+1:idx+size(stock_' type ',3))=stock_' type '(:,jdx+1:jdx+reste,:);'])
|
||||||
eval(['idx = idx + size(stock_' type ',3);'])
|
eval(['idx = idx + size(stock_' type ',3);'])
|
||||||
end
|
end
|
||||||
%eval(['STOCK_' CAPtype ' = sort(STOCK_' CAPtype ',3);'])
|
%eval(['STOCK_' CAPtype ' = sort(STOCK_' CAPtype ',3);'])
|
||||||
save([MhDirectoryName M_.fname '_' CAPtype 's' int2str(NumberOfTYPEfiles) '.mat'],['STOCK_' CAPtype]);
|
save([MhDirectoryName M_.fname '_' CAPtype 's' int2str(NumberOfTYPEfiles) '.mat'],['STOCK_' CAPtype]);
|
||||||
else
|
else
|
||||||
load([MhDirectoryName M_.fname '_' type '1.mat']);
|
load([MhDirectoryName M_.fname '_' type '1.mat']);
|
||||||
%eval(['STOCK_' CAPtype ' = sort(stock_' type ',3);'])
|
%eval(['STOCK_' CAPtype ' = sort(stock_' type ',3);'])
|
||||||
eval(['STOCK_' CAPtype ' = stock_' type ';'])
|
eval(['STOCK_' CAPtype ' = stock_' type ';'])
|
||||||
save([MhDirectoryName M_.fname '_' CAPtype 's' int2str(1) '.mat'],['STOCK_' CAPtype ]);
|
save([MhDirectoryName M_.fname '_' CAPtype 's' int2str(1) '.mat'],['STOCK_' CAPtype ]);
|
||||||
end
|
end
|
||||||
% Original file format may be useful in some cases...
|
% Original file format may be useful in some cases...
|
||||||
% for file = 1:NumberOfTYPEfiles
|
% for file = 1:NumberOfTYPEfiles
|
||||||
% delete([MhDirectoryName M_.fname '_' type int2str(file) '.mat'])
|
% delete([MhDirectoryName M_.fname '_' type int2str(file) '.mat'])
|
||||||
% end
|
% end
|
||||||
end
|
end
|
||||||
|
|
@ -1,179 +1,179 @@
|
||||||
function [omega,f] = UnivariateSpectralDensity(dr,var_list)
|
function [omega,f] = UnivariateSpectralDensity(dr,var_list)
|
||||||
% This function computes the theoretical spectral density of each
|
% This function computes the theoretical spectral density of each
|
||||||
% endogenous variable declared in var_list. Results are stored in
|
% endogenous variable declared in var_list. Results are stored in
|
||||||
% oo_ and may be plotted.
|
% oo_ and may be plotted.
|
||||||
%
|
%
|
||||||
% Adapted from th_autocovariances.m.
|
% Adapted from th_autocovariances.m.
|
||||||
|
|
||||||
% Copyright (C) 2006-2009 Dynare Team
|
% Copyright (C) 2006-2009 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
% Dynare is free software: you can redistribute it and/or modify
|
||||||
% it under the terms of the GNU General Public License as published by
|
% it under the terms of the GNU General Public License as published by
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
% the Free Software Foundation, either version 3 of the License, or
|
||||||
% (at your option) any later version.
|
% (at your option) any later version.
|
||||||
%
|
%
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
% Dynare is distributed in the hope that it will be useful,
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
global options_ oo_ M_
|
global options_ oo_ M_
|
||||||
|
|
||||||
|
|
||||||
omega = []; f = [];
|
omega = []; f = [];
|
||||||
|
|
||||||
if options_.order > 1
|
if options_.order > 1
|
||||||
disp('UnivariateSpectralDensity :: I Cannot compute the theoretical spectral density')
|
disp('UnivariateSpectralDensity :: I Cannot compute the theoretical spectral density')
|
||||||
disp('with a second order approximation of the DSGE model!')
|
disp('with a second order approximation of the DSGE model!')
|
||||||
disp('Set order = 1.')
|
disp('Set order = 1.')
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
|
|
||||||
pltinfo = 1;%options_.SpectralDensity.Th.plot;
|
pltinfo = 1;%options_.SpectralDensity.Th.plot;
|
||||||
cutoff = 150;%options_.SpectralDensity.Th.cutoff;
|
cutoff = 150;%options_.SpectralDensity.Th.cutoff;
|
||||||
sdl = 0.01;%options_.SepctralDensity.Th.sdl;
|
sdl = 0.01;%options_.SepctralDensity.Th.sdl;
|
||||||
omega = (0:sdl:pi)';
|
omega = (0:sdl:pi)';
|
||||||
GridSize = length(omega);
|
GridSize = length(omega);
|
||||||
exo_names_orig_ord = M_.exo_names_orig_ord;
|
exo_names_orig_ord = M_.exo_names_orig_ord;
|
||||||
if exist('OCTAVE_VERSION')
|
if exist('OCTAVE_VERSION')
|
||||||
warning('off', 'Octave:divide-by-zero')
|
warning('off', 'Octave:divide-by-zero')
|
||||||
else
|
else
|
||||||
warning off MATLAB:dividebyzero
|
warning off MATLAB:dividebyzero
|
||||||
end
|
end
|
||||||
if nargin<2
|
if nargin<2
|
||||||
var_list = [];
|
var_list = [];
|
||||||
end
|
end
|
||||||
if size(var_list,1) == 0
|
if size(var_list,1) == 0
|
||||||
var_list = M_.endo_names(1:M_.orig_endo_nbr, :);
|
var_list = M_.endo_names(1:M_.orig_endo_nbr, :);
|
||||||
end
|
end
|
||||||
nvar = size(var_list,1);
|
nvar = size(var_list,1);
|
||||||
ivar=zeros(nvar,1);
|
ivar=zeros(nvar,1);
|
||||||
for i=1:nvar
|
for i=1:nvar
|
||||||
i_tmp = strmatch(var_list(i,:),M_.endo_names,'exact');
|
i_tmp = strmatch(var_list(i,:),M_.endo_names,'exact');
|
||||||
if isempty(i_tmp)
|
if isempty(i_tmp)
|
||||||
error (['One of the variable specified does not exist']) ;
|
error (['One of the variable specified does not exist']) ;
|
||||||
else
|
else
|
||||||
ivar(i) = i_tmp;
|
ivar(i) = i_tmp;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
f = zeros(nvar,GridSize);
|
f = zeros(nvar,GridSize);
|
||||||
ghx = dr.ghx;
|
ghx = dr.ghx;
|
||||||
ghu = dr.ghu;
|
ghu = dr.ghu;
|
||||||
npred = dr.npred;
|
npred = dr.npred;
|
||||||
nstatic = dr.nstatic;
|
nstatic = dr.nstatic;
|
||||||
kstate = dr.kstate;
|
kstate = dr.kstate;
|
||||||
order = dr.order_var;
|
order = dr.order_var;
|
||||||
iv(order) = [1:length(order)];
|
iv(order) = [1:length(order)];
|
||||||
nx = size(ghx,2);
|
nx = size(ghx,2);
|
||||||
ikx = [nstatic+1:nstatic+npred];
|
ikx = [nstatic+1:nstatic+npred];
|
||||||
A = zeros(nx,nx);
|
A = zeros(nx,nx);
|
||||||
k0 = kstate(find(kstate(:,2) <= M_.maximum_lag+1),:);
|
k0 = kstate(find(kstate(:,2) <= M_.maximum_lag+1),:);
|
||||||
i0 = find(k0(:,2) == M_.maximum_lag+1);
|
i0 = find(k0(:,2) == M_.maximum_lag+1);
|
||||||
i00 = i0;
|
i00 = i0;
|
||||||
n0 = length(i0);
|
n0 = length(i0);
|
||||||
A(i0,:) = ghx(ikx,:);
|
A(i0,:) = ghx(ikx,:);
|
||||||
AS = ghx(:,i0);
|
AS = ghx(:,i0);
|
||||||
ghu1 = zeros(nx,M_.exo_nbr);
|
ghu1 = zeros(nx,M_.exo_nbr);
|
||||||
ghu1(i0,:) = ghu(ikx,:);
|
ghu1(i0,:) = ghu(ikx,:);
|
||||||
for i=M_.maximum_lag:-1:2
|
for i=M_.maximum_lag:-1:2
|
||||||
i1 = find(k0(:,2) == i);
|
i1 = find(k0(:,2) == i);
|
||||||
n1 = size(i1,1);
|
n1 = size(i1,1);
|
||||||
j1 = zeros(n1,1);
|
j1 = zeros(n1,1);
|
||||||
j2 = j1;
|
j2 = j1;
|
||||||
for k1 = 1:n1
|
for k1 = 1:n1
|
||||||
j1(k1) = find(k0(i00,1)==k0(i1(k1),1));
|
j1(k1) = find(k0(i00,1)==k0(i1(k1),1));
|
||||||
j2(k1) = find(k0(i0,1)==k0(i1(k1),1));
|
j2(k1) = find(k0(i0,1)==k0(i1(k1),1));
|
||||||
end
|
end
|
||||||
AS(:,j1) = AS(:,j1)+ghx(:,i1);
|
AS(:,j1) = AS(:,j1)+ghx(:,i1);
|
||||||
i0 = i1;
|
i0 = i1;
|
||||||
end
|
end
|
||||||
Gamma = zeros(nvar,cutoff+1);
|
Gamma = zeros(nvar,cutoff+1);
|
||||||
[A,B] = kalman_transition_matrix(dr,ikx',1:nx,dr.transition_auxiliary_variables,M_.exo_nbr);
|
[A,B] = kalman_transition_matrix(dr,ikx',1:nx,dr.transition_auxiliary_variables,M_.exo_nbr);
|
||||||
[vx, u] = lyapunov_symm(A,B*M_.Sigma_e*B',options_.qz_criterium,options_.lyapunov_complex_threshold);
|
[vx, u] = lyapunov_symm(A,B*M_.Sigma_e*B',options_.qz_criterium,options_.lyapunov_complex_threshold);
|
||||||
iky = iv(ivar);
|
iky = iv(ivar);
|
||||||
if ~isempty(u)
|
if ~isempty(u)
|
||||||
iky = iky(find(any(abs(ghx(iky,:)*u) < options_.Schur_vec_tol,2)));
|
iky = iky(find(any(abs(ghx(iky,:)*u) < options_.Schur_vec_tol,2)));
|
||||||
ivar = dr.order_var(iky);
|
ivar = dr.order_var(iky);
|
||||||
end
|
end
|
||||||
aa = ghx(iky,:);
|
aa = ghx(iky,:);
|
||||||
bb = ghu(iky,:);
|
bb = ghu(iky,:);
|
||||||
|
|
||||||
if options_.hp_filter == 0
|
if options_.hp_filter == 0
|
||||||
tmp = aa*vx*aa'+ bb*M_.Sigma_e*bb';
|
tmp = aa*vx*aa'+ bb*M_.Sigma_e*bb';
|
||||||
k = find(abs(tmp) < 1e-12);
|
k = find(abs(tmp) < 1e-12);
|
||||||
tmp(k) = 0;
|
tmp(k) = 0;
|
||||||
Gamma(:,1) = diag(tmp);
|
Gamma(:,1) = diag(tmp);
|
||||||
vxy = (A*vx*aa'+ghu1*M_.Sigma_e*bb');
|
vxy = (A*vx*aa'+ghu1*M_.Sigma_e*bb');
|
||||||
tmp = aa*vxy;
|
tmp = aa*vxy;
|
||||||
k = find(abs(tmp) < 1e-12);
|
k = find(abs(tmp) < 1e-12);
|
||||||
tmp(k) = 0;
|
tmp(k) = 0;
|
||||||
Gamma(:,2) = diag(tmp);
|
Gamma(:,2) = diag(tmp);
|
||||||
for i=2:cutoff
|
for i=2:cutoff
|
||||||
vxy = A*vxy;
|
vxy = A*vxy;
|
||||||
tmp = aa*vxy;
|
tmp = aa*vxy;
|
||||||
k = find(abs(tmp) < 1e-12);
|
k = find(abs(tmp) < 1e-12);
|
||||||
tmp(k) = 0;
|
tmp(k) = 0;
|
||||||
Gamma(:,i+1) = diag(tmp);
|
Gamma(:,i+1) = diag(tmp);
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
iky = iv(ivar);
|
iky = iv(ivar);
|
||||||
aa = ghx(iky,:);
|
aa = ghx(iky,:);
|
||||||
bb = ghu(iky,:);
|
bb = ghu(iky,:);
|
||||||
lambda = options_.hp_filter;
|
lambda = options_.hp_filter;
|
||||||
ngrid = options_.hp_ngrid;
|
ngrid = options_.hp_ngrid;
|
||||||
freqs = 0 : ((2*pi)/ngrid) : (2*pi*(1 - .5/ngrid));
|
freqs = 0 : ((2*pi)/ngrid) : (2*pi*(1 - .5/ngrid));
|
||||||
tpos = exp( sqrt(-1)*freqs);
|
tpos = exp( sqrt(-1)*freqs);
|
||||||
tneg = exp(-sqrt(-1)*freqs);
|
tneg = exp(-sqrt(-1)*freqs);
|
||||||
hp1 = 4*lambda*(1 - cos(freqs)).^2 ./ (1 + 4*lambda*(1 - cos(freqs)).^2);
|
hp1 = 4*lambda*(1 - cos(freqs)).^2 ./ (1 + 4*lambda*(1 - cos(freqs)).^2);
|
||||||
mathp_col = [];
|
mathp_col = [];
|
||||||
IA = eye(size(A,1));
|
IA = eye(size(A,1));
|
||||||
IE = eye(M_.exo_nbr);
|
IE = eye(M_.exo_nbr);
|
||||||
for ig = 1:ngrid
|
for ig = 1:ngrid
|
||||||
f_omega =(1/(2*pi))*( [inv(IA-A*tneg(ig))*ghu1;IE]...
|
f_omega =(1/(2*pi))*( [inv(IA-A*tneg(ig))*ghu1;IE]...
|
||||||
*M_.Sigma_e*[ghu1'*inv(IA-A'*tpos(ig)) IE]); % state variables
|
*M_.Sigma_e*[ghu1'*inv(IA-A'*tpos(ig)) IE]); % state variables
|
||||||
g_omega = [aa*tneg(ig) bb]*f_omega*[aa'*tpos(ig); bb']; % selected variables
|
g_omega = [aa*tneg(ig) bb]*f_omega*[aa'*tpos(ig); bb']; % selected variables
|
||||||
f_hp = hp1(ig)^2*g_omega; % spectral density of selected filtered series
|
f_hp = hp1(ig)^2*g_omega; % spectral density of selected filtered series
|
||||||
mathp_col = [mathp_col ; (f_hp(:))']; % store as matrix row
|
mathp_col = [mathp_col ; (f_hp(:))']; % store as matrix row
|
||||||
% for ifft
|
% for ifft
|
||||||
end;
|
end;
|
||||||
imathp_col = real(ifft(mathp_col))*(2*pi);
|
imathp_col = real(ifft(mathp_col))*(2*pi);
|
||||||
tmp = reshape(imathp_col(1,:),nvar,nvar);
|
tmp = reshape(imathp_col(1,:),nvar,nvar);
|
||||||
k = find(abs(tmp)<1e-12);
|
k = find(abs(tmp)<1e-12);
|
||||||
tmp(k) = 0;
|
tmp(k) = 0;
|
||||||
Gamma(:,1) = diag(tmp);
|
Gamma(:,1) = diag(tmp);
|
||||||
sy = sqrt(Gamma(:,1));
|
sy = sqrt(Gamma(:,1));
|
||||||
sy = sy *sy';
|
sy = sy *sy';
|
||||||
for i=1:cutoff-1
|
for i=1:cutoff-1
|
||||||
tmp = reshape(imathp_col(i+1,:),nvar,nvar)./sy;
|
tmp = reshape(imathp_col(i+1,:),nvar,nvar)./sy;
|
||||||
k = find(abs(tmp) < 1e-12);
|
k = find(abs(tmp) < 1e-12);
|
||||||
tmp(k) = 0;
|
tmp(k) = 0;
|
||||||
Gamma(:,i+1) = diag(tmp);
|
Gamma(:,i+1) = diag(tmp);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
||||||
H = 1:cutoff;
|
H = 1:cutoff;
|
||||||
for i=1:nvar
|
for i=1:nvar
|
||||||
f(i,:) = Gamma(i,1)/(2*pi) + Gamma(i,H+1)*cos(H'*omega')/pi;
|
f(i,:) = Gamma(i,1)/(2*pi) + Gamma(i,H+1)*cos(H'*omega')/pi;
|
||||||
end
|
end
|
||||||
|
|
||||||
if exist('OCTAVE_VERSION')
|
if exist('OCTAVE_VERSION')
|
||||||
warning('on', 'Octave:divide-by-zero')
|
warning('on', 'Octave:divide-by-zero')
|
||||||
else
|
else
|
||||||
warning on MATLAB:dividebyzero
|
warning on MATLAB:dividebyzero
|
||||||
end
|
end
|
||||||
|
|
||||||
if pltinfo
|
if pltinfo
|
||||||
for i= 1:nvar
|
for i= 1:nvar
|
||||||
figure('Name',['Spectral Density of ' deblank(M_.endo_names(ivar(i),:)) '.'])
|
figure('Name',['Spectral Density of ' deblank(M_.endo_names(ivar(i),:)) '.'])
|
||||||
plot(omega,f(i,:),'-k','linewidth',2)
|
plot(omega,f(i,:),'-k','linewidth',2)
|
||||||
xlabel('0 \leq \omega \leq \pi')
|
xlabel('0 \leq \omega \leq \pi')
|
||||||
ylabel('f(\omega)')
|
ylabel('f(\omega)')
|
||||||
box on
|
box on
|
||||||
axis tight
|
axis tight
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
@ -1,7 +1,7 @@
|
||||||
function ys1 = add_auxiliary_variables_to_steadystate(ys,aux_vars,fname, ...
|
function ys1 = add_auxiliary_variables_to_steadystate(ys,aux_vars,fname, ...
|
||||||
exo_steady_state, exo_det_steady_state,params)
|
exo_steady_state, exo_det_steady_state,params)
|
||||||
% Add auxiliary variables to the steady state vector
|
% Add auxiliary variables to the steady state vector
|
||||||
|
|
||||||
% Copyright (C) 2009 Dynare Team
|
% Copyright (C) 2009 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
|
|
@ -18,29 +18,28 @@ function ys1 = add_auxiliary_variables_to_steadystate(ys,aux_vars,fname, ...
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
n = length(aux_vars);
|
n = length(aux_vars);
|
||||||
ys1 = [ys;zeros(n,1)];
|
ys1 = [ys;zeros(n,1)];
|
||||||
k = size(ys,1)+1;
|
k = size(ys,1)+1;
|
||||||
aux_lead_nbr = 0;
|
aux_lead_nbr = 0;
|
||||||
for i=1:n
|
for i=1:n
|
||||||
if aux_vars(i).type == 1
|
if aux_vars(i).type == 1
|
||||||
ys1(k) = ys(aux_vars(i).orig_index);
|
ys1(k) = ys(aux_vars(i).orig_index);
|
||||||
elseif aux_vars(i).type == 0
|
elseif aux_vars(i).type == 0
|
||||||
aux_lead_nbr = aux_lead_nbr + 1;
|
aux_lead_nbr = aux_lead_nbr + 1;
|
||||||
end
|
|
||||||
k = k+1;
|
|
||||||
end
|
end
|
||||||
|
k = k+1;
|
||||||
for i=1:aux_lead_nbr+1;
|
end
|
||||||
res = feval([fname '_static'],ys1,...
|
|
||||||
[exo_steady_state; ...
|
for i=1:aux_lead_nbr+1;
|
||||||
exo_det_steady_state],params);
|
res = feval([fname '_static'],ys1,...
|
||||||
for j=1:n
|
[exo_steady_state; ...
|
||||||
if aux_vars(j).type == 0
|
exo_det_steady_state],params);
|
||||||
el = aux_vars(j).endo_index;
|
for j=1:n
|
||||||
ys1(el) = ys1(el)-res(el);
|
if aux_vars(j).type == 0
|
||||||
end
|
el = aux_vars(j).endo_index;
|
||||||
|
ys1(el) = ys1(el)-res(el);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
end
|
||||||
|
|
|
||||||
|
|
@ -59,28 +59,28 @@ function [InnovationVariance,AutoregressiveParameters] = autoregressive_process_
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
AutoregressiveParameters = NaN(p,1);
|
AutoregressiveParameters = NaN(p,1);
|
||||||
InnovationVariance = NaN;
|
InnovationVariance = NaN;
|
||||||
switch p
|
switch p
|
||||||
case 1
|
case 1
|
||||||
AutoregressiveParameters = Rho(1);
|
AutoregressiveParameters = Rho(1);
|
||||||
case 2
|
case 2
|
||||||
tmp = (Rho(2)-1)/(Rho(1)*Rho(1)-1);
|
tmp = (Rho(2)-1)/(Rho(1)*Rho(1)-1);
|
||||||
AutoregressiveParameters(1) = Rho(1)*tmp;
|
AutoregressiveParameters(1) = Rho(1)*tmp;
|
||||||
AutoregressiveParameters(2) = 1-tmp;
|
AutoregressiveParameters(2) = 1-tmp;
|
||||||
case 3
|
case 3
|
||||||
t1 = 1/(Rho(2)-2*Rho(1)*Rho(1)+1);
|
t1 = 1/(Rho(2)-2*Rho(1)*Rho(1)+1);
|
||||||
t2 = (1.5*Rho(1)-2*Rho(1)*Rho(1)*Rho(1)+.5*Rho(3))*t1;
|
t2 = (1.5*Rho(1)-2*Rho(1)*Rho(1)*Rho(1)+.5*Rho(3))*t1;
|
||||||
t3 = .5*(Rho(1)- Rho(3))/(Rho(2)-1);
|
t3 = .5*(Rho(1)- Rho(3))/(Rho(2)-1);
|
||||||
AutoregressiveParameters(1) = t2-t3-Rho(1);
|
AutoregressiveParameters(1) = t2-t3-Rho(1);
|
||||||
AutoregressiveParameters(2) = (Rho(2)*Rho(2)-Rho(3)*Rho(1)-Rho(1)*Rho(1)+Rho(2))*t1 ;
|
AutoregressiveParameters(2) = (Rho(2)*Rho(2)-Rho(3)*Rho(1)-Rho(1)*Rho(1)+Rho(2))*t1 ;
|
||||||
AutoregressiveParameters(3) = t3-Rho(1)+t2;
|
AutoregressiveParameters(3) = t3-Rho(1)+t2;
|
||||||
otherwise
|
otherwise
|
||||||
AutocorrelationMatrix = eye(p);
|
AutocorrelationMatrix = eye(p);
|
||||||
for i=1:p-1
|
for i=1:p-1
|
||||||
AutocorrelationMatrix = AutocorrelationMatrix + diag(Rho(i)*ones(p-i,1),i);
|
AutocorrelationMatrix = AutocorrelationMatrix + diag(Rho(i)*ones(p-i,1),i);
|
||||||
AutocorrelationMatrix = AutocorrelationMatrix + diag(Rho(i)*ones(p-i,1),-i);
|
AutocorrelationMatrix = AutocorrelationMatrix + diag(Rho(i)*ones(p-i,1),-i);
|
||||||
end
|
|
||||||
AutoregressiveParameters = AutocorrelationMatrix\Rho;
|
|
||||||
end
|
end
|
||||||
InnovationVariance = Variance * (1-AutoregressiveParameters'*Rho);
|
AutoregressiveParameters = AutocorrelationMatrix\Rho;
|
||||||
|
end
|
||||||
|
InnovationVariance = Variance * (1-AutoregressiveParameters'*Rho);
|
||||||
|
|
@ -25,20 +25,20 @@ function H = bfgsi(H0,dg,dx)
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
if size(dg,2)>1
|
if size(dg,2)>1
|
||||||
dg=dg';
|
dg=dg';
|
||||||
end
|
end
|
||||||
if size(dx,2)>1
|
if size(dx,2)>1
|
||||||
dx=dx';
|
dx=dx';
|
||||||
end
|
end
|
||||||
Hdg = H0*dg;
|
Hdg = H0*dg;
|
||||||
dgdx = dg'*dx;
|
dgdx = dg'*dx;
|
||||||
if (abs(dgdx) >1e-12)
|
if (abs(dgdx) >1e-12)
|
||||||
H = H0 + (1+(dg'*Hdg)/dgdx)*(dx*dx')/dgdx - (dx*Hdg'+Hdg*dx')/dgdx;
|
H = H0 + (1+(dg'*Hdg)/dgdx)*(dx*dx')/dgdx - (dx*Hdg'+Hdg*dx')/dgdx;
|
||||||
else
|
else
|
||||||
disp('bfgs update failed.')
|
disp('bfgs update failed.')
|
||||||
disp(['|dg| = ' num2str(sqrt(dg'*dg)) '|dx| = ' num2str(sqrt(dx'*dx))]);
|
disp(['|dg| = ' num2str(sqrt(dg'*dg)) '|dx| = ' num2str(sqrt(dx'*dx))]);
|
||||||
disp(['dg''*dx = ' num2str(dgdx)])
|
disp(['dg''*dx = ' num2str(dgdx)])
|
||||||
disp(['|H*dg| = ' num2str(Hdg'*Hdg)])
|
disp(['|H*dg| = ' num2str(Hdg'*Hdg)])
|
||||||
H=H0;
|
H=H0;
|
||||||
end
|
end
|
||||||
save H.dat H
|
save H.dat H
|
||||||
|
|
|
||||||
|
|
@ -17,20 +17,20 @@ function [x,status]=bicgstab_(func,b,x,tole,kmax,varargin)
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
status = 0;
|
status = 0;
|
||||||
r=b-feval(func,x,varargin{:});
|
r=b-feval(func,x,varargin{:});
|
||||||
rh_0 = r;
|
rh_0 = r;
|
||||||
rh = r;
|
rh = r;
|
||||||
rho_0 = 1;
|
rho_0 = 1;
|
||||||
alpha = 1;
|
alpha = 1;
|
||||||
w = 1;
|
w = 1;
|
||||||
v = 0;
|
v = 0;
|
||||||
p = 0;
|
p = 0;
|
||||||
k = 0;
|
k = 0;
|
||||||
rho_1 = rh_0'*r;
|
rho_1 = rh_0'*r;
|
||||||
tolr = tole*norm(b);
|
tolr = tole*norm(b);
|
||||||
|
|
||||||
while norm(r) > tolr & k < kmax
|
while norm(r) > tolr & k < kmax
|
||||||
k = k+1;
|
k = k+1;
|
||||||
beta = (rho_1/rho_0)*(alpha/w);
|
beta = (rho_1/rho_0)*(alpha/w);
|
||||||
p = r+beta*(p-w*v);
|
p = r+beta*(p-w*v);
|
||||||
|
|
@ -43,8 +43,8 @@ function [x,status]=bicgstab_(func,b,x,tole,kmax,varargin)
|
||||||
rho_1 = -w*(rh_0'*t);
|
rho_1 = -w*(rh_0'*t);
|
||||||
x = x+alpha*p+w*r;
|
x = x+alpha*p+w*r;
|
||||||
r = r-w*t;
|
r = r-w*t;
|
||||||
end
|
end
|
||||||
if k == kmax
|
if k == kmax
|
||||||
status = 1;
|
status = 1;
|
||||||
warning(sprintf('BICSTABN didn''t converge after %d iterations: norm(r) = %g',kmax,norm(r)));
|
warning(sprintf('BICSTABN didn''t converge after %d iterations: norm(r) = %g',kmax,norm(r)));
|
||||||
end
|
end
|
||||||
|
|
@ -36,9 +36,9 @@ irf = iyf+(options_.periods-1)*ny ;
|
||||||
icf = [1:size(iyf,2)] ;
|
icf = [1:size(iyf,2)] ;
|
||||||
|
|
||||||
for i = 2:options_.periods
|
for i = 2:options_.periods
|
||||||
c(ir,jcf) = c(ir,jcf)-c(ir,icf)*c(irf,jcf) ;
|
c(ir,jcf) = c(ir,jcf)-c(ir,icf)*c(irf,jcf) ;
|
||||||
ir = ir-ny ;
|
ir = ir-ny ;
|
||||||
irf = irf-ny ;
|
irf = irf-ny ;
|
||||||
end
|
end
|
||||||
|
|
||||||
d = c(:,jcf) ;
|
d = c(:,jcf) ;
|
||||||
|
|
|
||||||
|
|
@ -49,28 +49,28 @@ ir = ir-ny ;
|
||||||
i = 2 ;
|
i = 2 ;
|
||||||
|
|
||||||
while i <= M_.maximum_lead | i <= options_.periods
|
while i <= M_.maximum_lead | i <= options_.periods
|
||||||
irf1 = selif(irf,irf<=options_.periods*ny) ;
|
irf1 = selif(irf,irf<=options_.periods*ny) ;
|
||||||
|
|
||||||
ofs = (((options_.periods-i)*ny+1)-1)*jcf*8 ;
|
ofs = (((options_.periods-i)*ny+1)-1)*jcf*8 ;
|
||||||
junk = fseek(fid,ofs,-1) ;
|
junk = fseek(fid,ofs,-1) ;
|
||||||
c = fread(fid,[jcf,ny],'float64')' ;
|
c = fread(fid,[jcf,ny],'float64')' ;
|
||||||
|
|
||||||
d1(ir) = c(:,jcf) - c(:,1:size(irf1,1))*d1(irf1) ;
|
d1(ir) = c(:,jcf) - c(:,1:size(irf1,1))*d1(irf1) ;
|
||||||
ir = ir - ny ;
|
ir = ir - ny ;
|
||||||
irf = irf - ny ;
|
irf = irf - ny ;
|
||||||
i = i + 1 ;
|
i = i + 1 ;
|
||||||
end
|
end
|
||||||
|
|
||||||
while i <= options_.periods
|
while i <= options_.periods
|
||||||
|
|
||||||
ofs = (((options_.periods-i)*ny+1)-1)*jcf*8 ;
|
ofs = (((options_.periods-i)*ny+1)-1)*jcf*8 ;
|
||||||
junk = fseek(fid,ofs,-1) ;
|
junk = fseek(fid,ofs,-1) ;
|
||||||
c = fread(fid,[jcf,ny],'float64')' ;
|
c = fread(fid,[jcf,ny],'float64')' ;
|
||||||
|
|
||||||
d1(ir) = c(:,jcf)-c(:,icf)*d1(irf) ;
|
d1(ir) = c(:,jcf)-c(:,icf)*d1(irf) ;
|
||||||
ir = ir-ny ;
|
ir = ir-ny ;
|
||||||
irf = irf-ny ;
|
irf = irf-ny ;
|
||||||
i = i+1;
|
i = i+1;
|
||||||
end
|
end
|
||||||
|
|
||||||
return ;
|
return ;
|
||||||
|
|
|
||||||
|
|
@ -19,12 +19,12 @@ function [r, g1] = block_mfs_steadystate(y, b)
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
global M_ oo_
|
global M_ oo_
|
||||||
|
|
||||||
ss = oo_.steady_state;
|
|
||||||
indx = M_.blocksMFS{b};
|
|
||||||
|
|
||||||
ss(indx) = y;
|
|
||||||
x = [oo_.exo_steady_state; oo_.exo_det_steady_state];
|
|
||||||
|
|
||||||
eval(['[r,g1] = ' M_.fname '_static(b, ss, x, M_.params);']);
|
ss = oo_.steady_state;
|
||||||
|
indx = M_.blocksMFS{b};
|
||||||
|
|
||||||
|
ss(indx) = y;
|
||||||
|
x = [oo_.exo_steady_state; oo_.exo_det_steady_state];
|
||||||
|
|
||||||
|
eval(['[r,g1] = ' M_.fname '_static(b, ss, x, M_.params);']);
|
||||||
|
|
|
||||||
|
|
@ -23,26 +23,26 @@ s1=upper(deblank(s1));
|
||||||
s2=upper(deblank(s2));
|
s2=upper(deblank(s2));
|
||||||
|
|
||||||
for im = 1:m
|
for im = 1:m
|
||||||
key = s1(im,:) ;
|
key = s1(im,:) ;
|
||||||
h = size(s2,1) ;
|
h = size(s2,1) ;
|
||||||
l = 1 ;
|
l = 1 ;
|
||||||
while l <= h
|
while l <= h
|
||||||
mid = round((h+l)/2) ;
|
mid = round((h+l)/2) ;
|
||||||
temp = s2(mid,:) ;
|
temp = s2(mid,:) ;
|
||||||
if ~ strcmp(key,temp)
|
if ~ strcmp(key,temp)
|
||||||
for i = 1:min(length(key),length(temp))
|
for i = 1:min(length(key),length(temp))
|
||||||
if temp(i) > key(i)
|
if temp(i) > key(i)
|
||||||
h = mid - 1 ;
|
h = mid - 1 ;
|
||||||
break
|
break
|
||||||
else
|
else
|
||||||
l = mid + 1 ;
|
l = mid + 1 ;
|
||||||
break
|
break
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
x(im) = mid ;
|
x(im) = mid ;
|
||||||
break
|
break
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -29,21 +29,21 @@ function bvar_density(maxnlags)
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
for nlags = 1:maxnlags
|
for nlags = 1:maxnlags
|
||||||
[ny, nx, posterior, prior] = bvar_toolbox(nlags);
|
[ny, nx, posterior, prior] = bvar_toolbox(nlags);
|
||||||
|
|
||||||
posterior_int = matrictint(posterior.S, posterior.df, posterior.XXi);
|
posterior_int = matrictint(posterior.S, posterior.df, posterior.XXi);
|
||||||
prior_int = matrictint(prior.S, prior.df, prior.XXi);
|
prior_int = matrictint(prior.S, prior.df, prior.XXi);
|
||||||
|
|
||||||
lik_nobs = posterior.df - prior.df;
|
lik_nobs = posterior.df - prior.df;
|
||||||
|
|
||||||
log_dnsty = posterior_int - prior_int - 0.5*ny*lik_nobs*log(2*pi);
|
log_dnsty = posterior_int - prior_int - 0.5*ny*lik_nobs*log(2*pi);
|
||||||
|
|
||||||
disp(' ')
|
disp(' ')
|
||||||
fprintf('The marginal log density of the BVAR(%g) model is equal to %10.4f\n', ...
|
fprintf('The marginal log density of the BVAR(%g) model is equal to %10.4f\n', ...
|
||||||
nlags, log_dnsty);
|
nlags, log_dnsty);
|
||||||
disp(' ')
|
disp(' ')
|
||||||
end
|
end
|
||||||
|
|
||||||
|
|
||||||
function w = matrictint(S, df, XXi)
|
function w = matrictint(S, df, XXi)
|
||||||
|
|
@ -64,31 +64,31 @@ function w = matrictint(S, df, XXi)
|
||||||
|
|
||||||
% Original file downloaded from:
|
% Original file downloaded from:
|
||||||
% http://sims.princeton.edu/yftp/VARtools/matlab/matrictint.m
|
% http://sims.princeton.edu/yftp/VARtools/matlab/matrictint.m
|
||||||
|
|
||||||
k=size(XXi,1);
|
|
||||||
ny=size(S,1);
|
|
||||||
[cx,p]=chol(XXi);
|
|
||||||
[cs,q]=chol(S);
|
|
||||||
|
|
||||||
if any(diag(cx)<100*eps)
|
k=size(XXi,1);
|
||||||
error('singular XXi')
|
ny=size(S,1);
|
||||||
end
|
[cx,p]=chol(XXi);
|
||||||
if any(diag(cs<100*eps))
|
[cs,q]=chol(S);
|
||||||
error('singular S')
|
|
||||||
end
|
|
||||||
|
|
||||||
% Matrix-normal component
|
if any(diag(cx)<100*eps)
|
||||||
w1 = 0.5*k*ny*log(2*pi)+ny*sum(log(diag(cx)));
|
error('singular XXi')
|
||||||
|
end
|
||||||
% Inverse-Wishart component
|
if any(diag(cs<100*eps))
|
||||||
w2 = -df*sum(log(diag(cs))) + 0.5*df*ny*log(2) + ny*(ny-1)*0.25*log(pi) + ggammaln(ny, df);
|
error('singular S')
|
||||||
|
end
|
||||||
w = w1 + w2;
|
|
||||||
|
% Matrix-normal component
|
||||||
|
w1 = 0.5*k*ny*log(2*pi)+ny*sum(log(diag(cx)));
|
||||||
|
|
||||||
|
% Inverse-Wishart component
|
||||||
|
w2 = -df*sum(log(diag(cs))) + 0.5*df*ny*log(2) + ny*(ny-1)*0.25*log(pi) + ggammaln(ny, df);
|
||||||
|
|
||||||
|
w = w1 + w2;
|
||||||
|
|
||||||
function lgg = ggammaln(m, df)
|
function lgg = ggammaln(m, df)
|
||||||
if df <= (m-1)
|
if df <= (m-1)
|
||||||
error('too few df in ggammaln')
|
error('too few df in ggammaln')
|
||||||
else
|
else
|
||||||
garg = 0.5*(df+(0:-1:1-m));
|
garg = 0.5*(df+(0:-1:1-m));
|
||||||
lgg = sum(gammaln(garg));
|
lgg = sum(gammaln(garg));
|
||||||
end
|
end
|
||||||
|
|
|
||||||
|
|
@ -28,158 +28,158 @@ function bvar_forecast(nlags)
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
global options_ oo_ M_
|
global options_ oo_ M_
|
||||||
|
|
||||||
options_ = set_default_option(options_, 'bvar_replic', 2000);
|
|
||||||
if options_.forecast == 0
|
|
||||||
error('bvar_forecast: you must specify "forecast" option')
|
|
||||||
end
|
|
||||||
[ny, nx, posterior, prior, forecast_data] = bvar_toolbox(nlags);
|
|
||||||
|
|
||||||
sims_no_shock = NaN(options_.forecast, ny, options_.bvar_replic);
|
options_ = set_default_option(options_, 'bvar_replic', 2000);
|
||||||
sims_with_shocks = NaN(options_.forecast, ny, options_.bvar_replic);
|
if options_.forecast == 0
|
||||||
|
error('bvar_forecast: you must specify "forecast" option')
|
||||||
|
end
|
||||||
|
[ny, nx, posterior, prior, forecast_data] = bvar_toolbox(nlags);
|
||||||
|
|
||||||
|
sims_no_shock = NaN(options_.forecast, ny, options_.bvar_replic);
|
||||||
|
sims_with_shocks = NaN(options_.forecast, ny, options_.bvar_replic);
|
||||||
|
|
||||||
|
S_inv_upper_chol = chol(inv(posterior.S));
|
||||||
|
|
||||||
|
% Option 'lower' of chol() not available in old versions of
|
||||||
|
% Matlab, so using transpose
|
||||||
|
XXi_lower_chol = chol(posterior.XXi)';
|
||||||
|
|
||||||
|
k = ny*nlags+nx;
|
||||||
|
|
||||||
|
% Declaration of the companion matrix
|
||||||
|
Companion_matrix = diag(ones(ny*(nlags-1),1),-ny);
|
||||||
|
|
||||||
|
% Number of explosive VAR models sampled
|
||||||
|
p = 0;
|
||||||
|
% Loop counter initialization
|
||||||
|
d = 0;
|
||||||
|
while d <= options_.bvar_replic
|
||||||
|
|
||||||
S_inv_upper_chol = chol(inv(posterior.S));
|
Sigma = rand_inverse_wishart(ny, posterior.df, S_inv_upper_chol);
|
||||||
|
|
||||||
% Option 'lower' of chol() not available in old versions of
|
% Option 'lower' of chol() not available in old versions of
|
||||||
% Matlab, so using transpose
|
% Matlab, so using transpose
|
||||||
XXi_lower_chol = chol(posterior.XXi)';
|
Sigma_lower_chol = chol(Sigma)';
|
||||||
|
|
||||||
k = ny*nlags+nx;
|
|
||||||
|
|
||||||
% Declaration of the companion matrix
|
|
||||||
Companion_matrix = diag(ones(ny*(nlags-1),1),-ny);
|
|
||||||
|
|
||||||
% Number of explosive VAR models sampled
|
|
||||||
p = 0;
|
|
||||||
% Loop counter initialization
|
|
||||||
d = 0;
|
|
||||||
while d <= options_.bvar_replic
|
|
||||||
|
|
||||||
Sigma = rand_inverse_wishart(ny, posterior.df, S_inv_upper_chol);
|
|
||||||
|
|
||||||
% Option 'lower' of chol() not available in old versions of
|
Phi = rand_matrix_normal(k, ny, posterior.PhiHat, Sigma_lower_chol, XXi_lower_chol);
|
||||||
% Matlab, so using transpose
|
|
||||||
Sigma_lower_chol = chol(Sigma)';
|
|
||||||
|
|
||||||
Phi = rand_matrix_normal(k, ny, posterior.PhiHat, Sigma_lower_chol, XXi_lower_chol);
|
|
||||||
|
|
||||||
% All the eigenvalues of the companion matrix have to be on or inside the unit circle
|
|
||||||
Companion_matrix(1:ny,:) = Phi(1:ny*nlags,:)';
|
|
||||||
test = (abs(eig(Companion_matrix)));
|
|
||||||
if any(test>1.0000000000001)
|
|
||||||
p = p+1;
|
|
||||||
end
|
|
||||||
d = d+1;
|
|
||||||
|
|
||||||
% Without shocks
|
|
||||||
lags_data = forecast_data.initval;
|
|
||||||
for t = 1:options_.forecast
|
|
||||||
X = [ reshape(flipdim(lags_data, 1)', 1, ny*nlags) forecast_data.xdata(t, :) ];
|
|
||||||
y = X * Phi;
|
|
||||||
lags_data(1:end-1,:) = lags_data(2:end, :);
|
|
||||||
lags_data(end,:) = y;
|
|
||||||
sims_no_shock(t, :, d) = y;
|
|
||||||
end
|
|
||||||
|
|
||||||
% With shocks
|
% All the eigenvalues of the companion matrix have to be on or inside the unit circle
|
||||||
lags_data = forecast_data.initval;
|
Companion_matrix(1:ny,:) = Phi(1:ny*nlags,:)';
|
||||||
for t = 1:options_.forecast
|
test = (abs(eig(Companion_matrix)));
|
||||||
X = [ reshape(flipdim(lags_data, 1)', 1, ny*nlags) forecast_data.xdata(t, :) ];
|
if any(test>1.0000000000001)
|
||||||
shock = (Sigma_lower_chol * randn(ny, 1))';
|
p = p+1;
|
||||||
y = X * Phi + shock;
|
end
|
||||||
lags_data(1:end-1,:) = lags_data(2:end, :);
|
d = d+1;
|
||||||
lags_data(end,:) = y;
|
|
||||||
sims_with_shocks(t, :, d) = y;
|
% Without shocks
|
||||||
end
|
lags_data = forecast_data.initval;
|
||||||
|
for t = 1:options_.forecast
|
||||||
|
X = [ reshape(flipdim(lags_data, 1)', 1, ny*nlags) forecast_data.xdata(t, :) ];
|
||||||
|
y = X * Phi;
|
||||||
|
lags_data(1:end-1,:) = lags_data(2:end, :);
|
||||||
|
lags_data(end,:) = y;
|
||||||
|
sims_no_shock(t, :, d) = y;
|
||||||
end
|
end
|
||||||
|
|
||||||
if p > 0
|
% With shocks
|
||||||
disp(' ')
|
lags_data = forecast_data.initval;
|
||||||
disp(['Some of the VAR models sampled from the posterior distribution'])
|
for t = 1:options_.forecast
|
||||||
disp(['were found to be explosive (' num2str(p/options_.bvar_replic) ' percent).'])
|
X = [ reshape(flipdim(lags_data, 1)', 1, ny*nlags) forecast_data.xdata(t, :) ];
|
||||||
disp(' ')
|
shock = (Sigma_lower_chol * randn(ny, 1))';
|
||||||
|
y = X * Phi + shock;
|
||||||
|
lags_data(1:end-1,:) = lags_data(2:end, :);
|
||||||
|
lags_data(end,:) = y;
|
||||||
|
sims_with_shocks(t, :, d) = y;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
if p > 0
|
||||||
|
disp(' ')
|
||||||
|
disp(['Some of the VAR models sampled from the posterior distribution'])
|
||||||
|
disp(['were found to be explosive (' num2str(p/options_.bvar_replic) ' percent).'])
|
||||||
|
disp(' ')
|
||||||
|
end
|
||||||
|
|
||||||
|
% Plot graphs
|
||||||
|
sims_no_shock_mean = mean(sims_no_shock, 3);
|
||||||
|
|
||||||
|
sort_idx = round((0.5 + [-options_.conf_sig, options_.conf_sig, 0]/2) * options_.bvar_replic);
|
||||||
|
|
||||||
|
sims_no_shock_sort = sort(sims_no_shock, 3);
|
||||||
|
sims_no_shock_down_conf = sims_no_shock_sort(:, :, sort_idx(1));
|
||||||
|
sims_no_shock_up_conf = sims_no_shock_sort(:, :, sort_idx(2));
|
||||||
|
sims_no_shock_median = sims_no_shock_sort(:, :, sort_idx(3));
|
||||||
|
|
||||||
|
sims_with_shocks_sort = sort(sims_with_shocks, 3);
|
||||||
|
sims_with_shocks_down_conf = sims_with_shocks_sort(:, :, sort_idx(1));
|
||||||
|
sims_with_shocks_up_conf = sims_with_shocks_sort(:, :, sort_idx(2));
|
||||||
|
|
||||||
|
dynare_graph_init(sprintf('BVAR forecasts (nlags = %d)', nlags), ny, {'b-' 'g-' 'g-' 'r-' 'r-'});
|
||||||
|
|
||||||
|
for i = 1:ny
|
||||||
|
dynare_graph([ sims_no_shock_median(:, i) ...
|
||||||
|
sims_no_shock_up_conf(:, i) sims_no_shock_down_conf(:, i) ...
|
||||||
|
sims_with_shocks_up_conf(:, i) sims_with_shocks_down_conf(:, i) ], ...
|
||||||
|
options_.varobs(i, :));
|
||||||
|
end
|
||||||
|
|
||||||
|
dynare_graph_close;
|
||||||
|
|
||||||
|
|
||||||
|
% Compute RMSE
|
||||||
|
|
||||||
|
if ~isempty(forecast_data.realized_val)
|
||||||
|
|
||||||
|
sq_err_cumul = zeros(1, ny);
|
||||||
|
|
||||||
|
lags_data = forecast_data.initval;
|
||||||
|
for t = 1:size(forecast_data.realized_val, 1)
|
||||||
|
X = [ reshape(flipdim(lags_data, 1)', 1, ny*nlags) forecast_data.realized_xdata(t, :) ];
|
||||||
|
y = X * posterior.PhiHat;
|
||||||
|
lags_data(1:end-1,:) = lags_data(2:end, :);
|
||||||
|
lags_data(end,:) = y;
|
||||||
|
sq_err_cumul = sq_err_cumul + (y - forecast_data.realized_val(t, :)) .^ 2;
|
||||||
end
|
end
|
||||||
|
|
||||||
% Plot graphs
|
rmse = sqrt(sq_err_cumul / size(sq_err_cumul, 1));
|
||||||
sims_no_shock_mean = mean(sims_no_shock, 3);
|
|
||||||
|
|
||||||
sort_idx = round((0.5 + [-options_.conf_sig, options_.conf_sig, 0]/2) * options_.bvar_replic);
|
fprintf('RMSE of BVAR(%d):\n', nlags);
|
||||||
|
|
||||||
sims_no_shock_sort = sort(sims_no_shock, 3);
|
|
||||||
sims_no_shock_down_conf = sims_no_shock_sort(:, :, sort_idx(1));
|
|
||||||
sims_no_shock_up_conf = sims_no_shock_sort(:, :, sort_idx(2));
|
|
||||||
sims_no_shock_median = sims_no_shock_sort(:, :, sort_idx(3));
|
|
||||||
|
|
||||||
sims_with_shocks_sort = sort(sims_with_shocks, 3);
|
|
||||||
sims_with_shocks_down_conf = sims_with_shocks_sort(:, :, sort_idx(1));
|
|
||||||
sims_with_shocks_up_conf = sims_with_shocks_sort(:, :, sort_idx(2));
|
|
||||||
|
|
||||||
dynare_graph_init(sprintf('BVAR forecasts (nlags = %d)', nlags), ny, {'b-' 'g-' 'g-' 'r-' 'r-'});
|
|
||||||
|
|
||||||
for i = 1:ny
|
|
||||||
dynare_graph([ sims_no_shock_median(:, i) ...
|
|
||||||
sims_no_shock_up_conf(:, i) sims_no_shock_down_conf(:, i) ...
|
|
||||||
sims_with_shocks_up_conf(:, i) sims_with_shocks_down_conf(:, i) ], ...
|
|
||||||
options_.varobs(i, :));
|
|
||||||
end
|
|
||||||
|
|
||||||
dynare_graph_close;
|
|
||||||
|
|
||||||
|
|
||||||
% Compute RMSE
|
|
||||||
|
|
||||||
if ~isempty(forecast_data.realized_val)
|
|
||||||
|
|
||||||
sq_err_cumul = zeros(1, ny);
|
|
||||||
|
|
||||||
lags_data = forecast_data.initval;
|
|
||||||
for t = 1:size(forecast_data.realized_val, 1)
|
|
||||||
X = [ reshape(flipdim(lags_data, 1)', 1, ny*nlags) forecast_data.realized_xdata(t, :) ];
|
|
||||||
y = X * posterior.PhiHat;
|
|
||||||
lags_data(1:end-1,:) = lags_data(2:end, :);
|
|
||||||
lags_data(end,:) = y;
|
|
||||||
sq_err_cumul = sq_err_cumul + (y - forecast_data.realized_val(t, :)) .^ 2;
|
|
||||||
end
|
|
||||||
|
|
||||||
rmse = sqrt(sq_err_cumul / size(sq_err_cumul, 1));
|
|
||||||
|
|
||||||
fprintf('RMSE of BVAR(%d):\n', nlags);
|
|
||||||
|
|
||||||
for i = 1:size(options_.varobs, 1)
|
|
||||||
fprintf('%s: %10.4f\n', options_.varobs(i, :), rmse(i));
|
|
||||||
end
|
|
||||||
end
|
|
||||||
|
|
||||||
% Store results
|
|
||||||
|
|
||||||
DirectoryName = [ M_.fname '/bvar_forecast' ];
|
|
||||||
if ~isdir(DirectoryName)
|
|
||||||
if ~isdir(M_.fname)
|
|
||||||
mkdir(M_.fname);
|
|
||||||
end
|
|
||||||
mkdir(DirectoryName);
|
|
||||||
end
|
|
||||||
save([ DirectoryName '/simulations.mat'], 'sims_no_shock', 'sims_with_shocks');
|
|
||||||
|
|
||||||
for i = 1:size(options_.varobs, 1)
|
for i = 1:size(options_.varobs, 1)
|
||||||
name = options_.varobs(i, :);
|
fprintf('%s: %10.4f\n', options_.varobs(i, :), rmse(i));
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
sims = squeeze(sims_with_shocks(:,i,:));
|
% Store results
|
||||||
eval(['oo_.bvar.forecast.with_shocks.Mean.' name ' = mean(sims, 2);']);
|
|
||||||
eval(['oo_.bvar.forecast.with_shocks.Median.' name ' = median(sims, 2);']);
|
|
||||||
eval(['oo_.bvar.forecast.with_shocks.Var.' name ' = var(sims, 0, 2);']);
|
|
||||||
eval(['oo_.bvar.forecast.with_shocks.HPDsup.' name ' = sims_with_shocks_up_conf(:,i);']);
|
|
||||||
eval(['oo_.bvar.forecast.with_shocks.HPDinf.' name ' = sims_with_shocks_down_conf(:,i);']);
|
|
||||||
|
|
||||||
sims = squeeze(sims_no_shock(:,i,:));
|
DirectoryName = [ M_.fname '/bvar_forecast' ];
|
||||||
eval(['oo_.bvar.forecast.no_shock.Mean.' name ' = sims_no_shock_mean(:,i);']);
|
if ~isdir(DirectoryName)
|
||||||
eval(['oo_.bvar.forecast.no_shock.Median.' name ' = sims_no_shock_median(:,i);']);
|
if ~isdir(M_.fname)
|
||||||
eval(['oo_.bvar.forecast.no_shock.Var.' name ' = var(sims, 0, 2);']);
|
mkdir(M_.fname);
|
||||||
eval(['oo_.bvar.forecast.no_shock.HPDsup.' name ' = sims_no_shock_up_conf(:,i);']);
|
|
||||||
eval(['oo_.bvar.forecast.no_shock.HPDinf.' name ' = sims_no_shock_down_conf(:,i);']);
|
|
||||||
|
|
||||||
if exist('rmse')
|
|
||||||
eval(['oo_.bvar.forecast.rmse.' name ' = rmse(i);']);
|
|
||||||
end
|
|
||||||
end
|
end
|
||||||
|
mkdir(DirectoryName);
|
||||||
|
end
|
||||||
|
save([ DirectoryName '/simulations.mat'], 'sims_no_shock', 'sims_with_shocks');
|
||||||
|
|
||||||
|
for i = 1:size(options_.varobs, 1)
|
||||||
|
name = options_.varobs(i, :);
|
||||||
|
|
||||||
|
sims = squeeze(sims_with_shocks(:,i,:));
|
||||||
|
eval(['oo_.bvar.forecast.with_shocks.Mean.' name ' = mean(sims, 2);']);
|
||||||
|
eval(['oo_.bvar.forecast.with_shocks.Median.' name ' = median(sims, 2);']);
|
||||||
|
eval(['oo_.bvar.forecast.with_shocks.Var.' name ' = var(sims, 0, 2);']);
|
||||||
|
eval(['oo_.bvar.forecast.with_shocks.HPDsup.' name ' = sims_with_shocks_up_conf(:,i);']);
|
||||||
|
eval(['oo_.bvar.forecast.with_shocks.HPDinf.' name ' = sims_with_shocks_down_conf(:,i);']);
|
||||||
|
|
||||||
|
sims = squeeze(sims_no_shock(:,i,:));
|
||||||
|
eval(['oo_.bvar.forecast.no_shock.Mean.' name ' = sims_no_shock_mean(:,i);']);
|
||||||
|
eval(['oo_.bvar.forecast.no_shock.Median.' name ' = sims_no_shock_median(:,i);']);
|
||||||
|
eval(['oo_.bvar.forecast.no_shock.Var.' name ' = var(sims, 0, 2);']);
|
||||||
|
eval(['oo_.bvar.forecast.no_shock.HPDsup.' name ' = sims_no_shock_up_conf(:,i);']);
|
||||||
|
eval(['oo_.bvar.forecast.no_shock.HPDinf.' name ' = sims_no_shock_down_conf(:,i);']);
|
||||||
|
|
||||||
|
if exist('rmse')
|
||||||
|
eval(['oo_.bvar.forecast.rmse.' name ' = rmse(i);']);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
|
||||||
|
|
@ -28,125 +28,125 @@ function bvar_irf(nlags,identification)
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
global options_ oo_ M_
|
global options_ oo_ M_
|
||||||
|
|
||||||
if nargin==1
|
|
||||||
identification = 'Cholesky';
|
|
||||||
end
|
|
||||||
|
|
||||||
options_ = set_default_option(options_, 'bvar_replic', 2000);
|
|
||||||
|
|
||||||
[ny, nx, posterior, prior] = bvar_toolbox(nlags);
|
if nargin==1
|
||||||
|
identification = 'Cholesky';
|
||||||
S_inv_upper_chol = chol(inv(posterior.S));
|
end
|
||||||
|
|
||||||
% Option 'lower' of chol() not available in old versions of
|
options_ = set_default_option(options_, 'bvar_replic', 2000);
|
||||||
% Matlab, so using transpose
|
|
||||||
XXi_lower_chol = chol(posterior.XXi)';
|
|
||||||
|
|
||||||
k = ny*nlags+nx;
|
|
||||||
|
|
||||||
% Declaration of the companion matrix
|
|
||||||
Companion_matrix = diag(ones(ny*(nlags-1),1),-ny);
|
|
||||||
|
|
||||||
% Number of explosive VAR models sampled
|
|
||||||
p = 0;
|
|
||||||
|
|
||||||
% Initialize a four dimensional array.
|
[ny, nx, posterior, prior] = bvar_toolbox(nlags);
|
||||||
sampled_irfs = NaN(ny, ny, options_.irf, options_.bvar_replic);
|
|
||||||
|
|
||||||
for draw=1:options_.bvar_replic
|
|
||||||
|
|
||||||
% Get a covariance matrix from an inverted Wishart distribution.
|
|
||||||
Sigma = rand_inverse_wishart(ny, posterior.df, S_inv_upper_chol);
|
|
||||||
Sigma_upper_chol = chol(Sigma);
|
|
||||||
Sigma_lower_chol = transpose(Sigma_upper_chol);
|
|
||||||
|
|
||||||
% Get the Autoregressive matrices from a matrix variate distribution.
|
S_inv_upper_chol = chol(inv(posterior.S));
|
||||||
Phi = rand_matrix_normal(k, ny, posterior.PhiHat, Sigma_lower_chol, XXi_lower_chol);
|
|
||||||
|
|
||||||
% Form the companion matrix.
|
|
||||||
Companion_matrix(1:ny,:) = transpose(Phi(1:ny*nlags,:));
|
|
||||||
|
|
||||||
% All the eigenvalues of the companion matrix have to be on or
|
|
||||||
% inside the unit circle to rule out explosive time series.
|
|
||||||
test = (abs(eig(Companion_matrix)));
|
|
||||||
if any(test>1.0000000000001)
|
|
||||||
p = p+1;
|
|
||||||
end
|
|
||||||
|
|
||||||
if strcmpi(identification,'Cholesky')
|
% Option 'lower' of chol() not available in old versions of
|
||||||
StructuralMat = Sigma_lower_chol;
|
% Matlab, so using transpose
|
||||||
elseif strcmpi(identification,'SquareRoot')
|
XXi_lower_chol = chol(posterior.XXi)';
|
||||||
StructuralMat = sqrtm(Sigma);
|
|
||||||
end
|
k = ny*nlags+nx;
|
||||||
|
|
||||||
% Build the IRFs...
|
% Declaration of the companion matrix
|
||||||
lags_data = zeros(ny,ny*nlags) ;
|
Companion_matrix = diag(ones(ny*(nlags-1),1),-ny);
|
||||||
sampled_irfs(:,:,1,draw) = Sigma_lower_chol ;
|
|
||||||
lags_data(:,1:ny) = Sigma_lower_chol ;
|
% Number of explosive VAR models sampled
|
||||||
for t=2:options_.irf
|
p = 0;
|
||||||
sampled_irfs(:,:,t,draw) = lags_data(:,:)*Phi(1:ny*nlags,:) ;
|
|
||||||
lags_data(:,ny+1:end) = lags_data(:,1:end-ny) ;
|
% Initialize a four dimensional array.
|
||||||
lags_data(:,1:ny) = sampled_irfs(:,:,t,draw) ;
|
sampled_irfs = NaN(ny, ny, options_.irf, options_.bvar_replic);
|
||||||
end
|
|
||||||
|
for draw=1:options_.bvar_replic
|
||||||
end
|
|
||||||
|
|
||||||
if p > 0
|
% Get a covariance matrix from an inverted Wishart distribution.
|
||||||
disp(' ')
|
Sigma = rand_inverse_wishart(ny, posterior.df, S_inv_upper_chol);
|
||||||
disp(['Some of the VAR models sampled from the posterior distribution'])
|
Sigma_upper_chol = chol(Sigma);
|
||||||
disp(['were found to be explosive (' int2str(p) ' samples).'])
|
Sigma_lower_chol = transpose(Sigma_upper_chol);
|
||||||
disp(' ')
|
|
||||||
|
% Get the Autoregressive matrices from a matrix variate distribution.
|
||||||
|
Phi = rand_matrix_normal(k, ny, posterior.PhiHat, Sigma_lower_chol, XXi_lower_chol);
|
||||||
|
|
||||||
|
% Form the companion matrix.
|
||||||
|
Companion_matrix(1:ny,:) = transpose(Phi(1:ny*nlags,:));
|
||||||
|
|
||||||
|
% All the eigenvalues of the companion matrix have to be on or
|
||||||
|
% inside the unit circle to rule out explosive time series.
|
||||||
|
test = (abs(eig(Companion_matrix)));
|
||||||
|
if any(test>1.0000000000001)
|
||||||
|
p = p+1;
|
||||||
end
|
end
|
||||||
|
|
||||||
posterior_mean_irfs = mean(sampled_irfs,4);
|
if strcmpi(identification,'Cholesky')
|
||||||
posterior_variance_irfs = var(sampled_irfs, 1, 4);
|
StructuralMat = Sigma_lower_chol;
|
||||||
|
elseif strcmpi(identification,'SquareRoot')
|
||||||
sorted_irfs = sort(sampled_irfs,4);
|
StructuralMat = sqrtm(Sigma);
|
||||||
sort_idx = round((0.5 + [-options_.conf_sig, options_.conf_sig, .0]/2) * options_.bvar_replic);
|
|
||||||
|
|
||||||
posterior_down_conf_irfs = sorted_irfs(:,:,:,sort_idx(1));
|
|
||||||
posterior_up_conf_irfs = sorted_irfs(:,:,:,sort_idx(2));
|
|
||||||
posterior_median_irfs = sorted_irfs(:,:,:,sort_idx(3));
|
|
||||||
|
|
||||||
number_of_columns = fix(sqrt(ny));
|
|
||||||
number_of_rows = ceil(ny / number_of_columns) ;
|
|
||||||
|
|
||||||
% Plots of the IRFs
|
|
||||||
for shock=1:ny
|
|
||||||
figure('Name',['Posterior BVAR Impulse Responses (shock in equation ' int2str(shock) ').']);
|
|
||||||
for variable=1:ny
|
|
||||||
subplot(number_of_rows,number_of_columns,variable);
|
|
||||||
h1 = area(1:options_.irf,squeeze(posterior_up_conf_irfs(shock,variable,:)));
|
|
||||||
set(h1,'BaseValue',min([min(posterior_up_conf_irfs(shock,variable,:)),min(posterior_down_conf_irfs(shock,variable,:))]))
|
|
||||||
set(h1,'FaceColor',[.9 .9 .9])
|
|
||||||
hold on
|
|
||||||
h2 = area(1:options_.irf,squeeze(posterior_down_conf_irfs(shock,variable,:)));
|
|
||||||
set(h2,'BaseValue',min([min(posterior_up_conf_irfs(shock,variable,:)),min(posterior_down_conf_irfs(shock,variable,:))]))
|
|
||||||
set(h2,'FaceColor',[1 1 1])
|
|
||||||
plot(1:options_.irf,squeeze(posterior_median_irfs(shock,variable,:)),'-k','linewidth',2)
|
|
||||||
axis tight
|
|
||||||
hold off
|
|
||||||
end
|
|
||||||
end
|
end
|
||||||
|
|
||||||
% Save intermediate results
|
% Build the IRFs...
|
||||||
DirectoryName = [ M_.fname '/bvar_irf' ];
|
lags_data = zeros(ny,ny*nlags) ;
|
||||||
if ~isdir(DirectoryName)
|
sampled_irfs(:,:,1,draw) = Sigma_lower_chol ;
|
||||||
mkdir('.',DirectoryName);
|
lags_data(:,1:ny) = Sigma_lower_chol ;
|
||||||
|
for t=2:options_.irf
|
||||||
|
sampled_irfs(:,:,t,draw) = lags_data(:,:)*Phi(1:ny*nlags,:) ;
|
||||||
|
lags_data(:,ny+1:end) = lags_data(:,1:end-ny) ;
|
||||||
|
lags_data(:,1:ny) = sampled_irfs(:,:,t,draw) ;
|
||||||
end
|
end
|
||||||
save([ DirectoryName '/simulations.mat'], 'sampled_irfs');
|
|
||||||
|
end
|
||||||
|
|
||||||
% Save results in oo_
|
if p > 0
|
||||||
for i=1:ny
|
disp(' ')
|
||||||
shock_name = options_.varobs(i, :);
|
disp(['Some of the VAR models sampled from the posterior distribution'])
|
||||||
for j=1:ny
|
disp(['were found to be explosive (' int2str(p) ' samples).'])
|
||||||
variable_name = options_.varobs(j, :);
|
disp(' ')
|
||||||
eval(['oo_.bvar.irf.Mean.' variable_name '.' shock_name ' = posterior_mean_irfs(' int2str(j) ',' int2str(i) ',:);'])
|
end
|
||||||
eval(['oo_.bvar.irf.Median.' variable_name '.' shock_name ' = posterior_median_irfs(' int2str(j) ',' int2str(i) ',:);'])
|
|
||||||
eval(['oo_.bvar.irf.Var.' variable_name '.' shock_name ' = posterior_variance_irfs(' int2str(j) ',' int2str(i) ',:);'])
|
posterior_mean_irfs = mean(sampled_irfs,4);
|
||||||
eval(['oo_.bvar.irf.Upper_bound.' variable_name '.' shock_name ' = posterior_up_conf_irfs(' int2str(j) ',' int2str(i) ',:);'])
|
posterior_variance_irfs = var(sampled_irfs, 1, 4);
|
||||||
eval(['oo_.bvar.irf.Lower_bound.' variable_name '.' shock_name ' = posterior_down_conf_irfs(' int2str(j) ',' int2str(i) ',:);'])
|
|
||||||
end
|
sorted_irfs = sort(sampled_irfs,4);
|
||||||
end
|
sort_idx = round((0.5 + [-options_.conf_sig, options_.conf_sig, .0]/2) * options_.bvar_replic);
|
||||||
|
|
||||||
|
posterior_down_conf_irfs = sorted_irfs(:,:,:,sort_idx(1));
|
||||||
|
posterior_up_conf_irfs = sorted_irfs(:,:,:,sort_idx(2));
|
||||||
|
posterior_median_irfs = sorted_irfs(:,:,:,sort_idx(3));
|
||||||
|
|
||||||
|
number_of_columns = fix(sqrt(ny));
|
||||||
|
number_of_rows = ceil(ny / number_of_columns) ;
|
||||||
|
|
||||||
|
% Plots of the IRFs
|
||||||
|
for shock=1:ny
|
||||||
|
figure('Name',['Posterior BVAR Impulse Responses (shock in equation ' int2str(shock) ').']);
|
||||||
|
for variable=1:ny
|
||||||
|
subplot(number_of_rows,number_of_columns,variable);
|
||||||
|
h1 = area(1:options_.irf,squeeze(posterior_up_conf_irfs(shock,variable,:)));
|
||||||
|
set(h1,'BaseValue',min([min(posterior_up_conf_irfs(shock,variable,:)),min(posterior_down_conf_irfs(shock,variable,:))]))
|
||||||
|
set(h1,'FaceColor',[.9 .9 .9])
|
||||||
|
hold on
|
||||||
|
h2 = area(1:options_.irf,squeeze(posterior_down_conf_irfs(shock,variable,:)));
|
||||||
|
set(h2,'BaseValue',min([min(posterior_up_conf_irfs(shock,variable,:)),min(posterior_down_conf_irfs(shock,variable,:))]))
|
||||||
|
set(h2,'FaceColor',[1 1 1])
|
||||||
|
plot(1:options_.irf,squeeze(posterior_median_irfs(shock,variable,:)),'-k','linewidth',2)
|
||||||
|
axis tight
|
||||||
|
hold off
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
% Save intermediate results
|
||||||
|
DirectoryName = [ M_.fname '/bvar_irf' ];
|
||||||
|
if ~isdir(DirectoryName)
|
||||||
|
mkdir('.',DirectoryName);
|
||||||
|
end
|
||||||
|
save([ DirectoryName '/simulations.mat'], 'sampled_irfs');
|
||||||
|
|
||||||
|
% Save results in oo_
|
||||||
|
for i=1:ny
|
||||||
|
shock_name = options_.varobs(i, :);
|
||||||
|
for j=1:ny
|
||||||
|
variable_name = options_.varobs(j, :);
|
||||||
|
eval(['oo_.bvar.irf.Mean.' variable_name '.' shock_name ' = posterior_mean_irfs(' int2str(j) ',' int2str(i) ',:);'])
|
||||||
|
eval(['oo_.bvar.irf.Median.' variable_name '.' shock_name ' = posterior_median_irfs(' int2str(j) ',' int2str(i) ',:);'])
|
||||||
|
eval(['oo_.bvar.irf.Var.' variable_name '.' shock_name ' = posterior_variance_irfs(' int2str(j) ',' int2str(i) ',:);'])
|
||||||
|
eval(['oo_.bvar.irf.Upper_bound.' variable_name '.' shock_name ' = posterior_up_conf_irfs(' int2str(j) ',' int2str(i) ',:);'])
|
||||||
|
eval(['oo_.bvar.irf.Lower_bound.' variable_name '.' shock_name ' = posterior_down_conf_irfs(' int2str(j) ',' int2str(i) ',:);'])
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
@ -40,7 +40,7 @@ function [ny, nx, posterior, prior, forecast_data] = bvar_toolbox(nlags)
|
||||||
% This function uses the following Dynare options:
|
% This function uses the following Dynare options:
|
||||||
% - datafile, first_obs, varobs, xls_sheet, xls_range, nobs, presample
|
% - datafile, first_obs, varobs, xls_sheet, xls_range, nobs, presample
|
||||||
% - bvar_prior_{tau,decay,lambda,mu,omega,flat,train}
|
% - bvar_prior_{tau,decay,lambda,mu,omega,flat,train}
|
||||||
|
|
||||||
% Copyright (C) 2003-2007 Christopher Sims
|
% Copyright (C) 2003-2007 Christopher Sims
|
||||||
% Copyright (C) 2007-2008 Dynare Team
|
% Copyright (C) 2007-2008 Dynare Team
|
||||||
%
|
%
|
||||||
|
|
@ -59,105 +59,105 @@ function [ny, nx, posterior, prior, forecast_data] = bvar_toolbox(nlags)
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
global options_
|
global options_
|
||||||
|
|
||||||
% Load dataset
|
|
||||||
dataset = read_variables(options_.datafile, options_.varobs, [], options_.xls_sheet, options_.xls_range);
|
|
||||||
options_ = set_default_option(options_, 'nobs', size(dataset,1)-options_.first_obs+1);
|
|
||||||
|
|
||||||
% Parameters for prior
|
|
||||||
options_ = set_default_option(options_, 'bvar_prior_tau', 3);
|
|
||||||
options_ = set_default_option(options_, 'bvar_prior_decay', 0.5);
|
|
||||||
options_ = set_default_option(options_, 'bvar_prior_lambda', 5);
|
|
||||||
options_ = set_default_option(options_, 'bvar_prior_mu', 2);
|
|
||||||
options_ = set_default_option(options_, 'bvar_prior_omega', 1);
|
|
||||||
options_ = set_default_option(options_, 'bvar_prior_flat', 0);
|
|
||||||
options_ = set_default_option(options_, 'bvar_prior_train', 0);
|
|
||||||
|
|
||||||
if options_.first_obs + options_.presample <= nlags
|
|
||||||
error('first_obs+presample should be > nlags (for initializing the VAR)')
|
|
||||||
end
|
|
||||||
|
|
||||||
train = options_.bvar_prior_train;
|
% Load dataset
|
||||||
|
dataset = read_variables(options_.datafile, options_.varobs, [], options_.xls_sheet, options_.xls_range);
|
||||||
if options_.first_obs + options_.presample - train <= nlags
|
options_ = set_default_option(options_, 'nobs', size(dataset,1)-options_.first_obs+1);
|
||||||
error('first_obs+presample-train should be > nlags (for initializating the VAR)')
|
|
||||||
end
|
|
||||||
|
|
||||||
idx = options_.first_obs+options_.presample-train-nlags:options_.first_obs+options_.nobs-1;
|
% Parameters for prior
|
||||||
|
options_ = set_default_option(options_, 'bvar_prior_tau', 3);
|
||||||
% Prepare dataset
|
options_ = set_default_option(options_, 'bvar_prior_decay', 0.5);
|
||||||
if options_.loglinear & ~options_.logdata
|
options_ = set_default_option(options_, 'bvar_prior_lambda', 5);
|
||||||
dataset = log(dataset);
|
options_ = set_default_option(options_, 'bvar_prior_mu', 2);
|
||||||
end
|
options_ = set_default_option(options_, 'bvar_prior_omega', 1);
|
||||||
if options_.prefilter
|
options_ = set_default_option(options_, 'bvar_prior_flat', 0);
|
||||||
dataset(idx,:) = dataset(idx,:) - ones(length(idx),1)*mean(dataset(idx,:));
|
options_ = set_default_option(options_, 'bvar_prior_train', 0);
|
||||||
end
|
|
||||||
|
|
||||||
mnprior.tight = options_.bvar_prior_tau;
|
|
||||||
mnprior.decay = options_.bvar_prior_decay;
|
|
||||||
|
|
||||||
% Use only initializations lags for the variance prior
|
if options_.first_obs + options_.presample <= nlags
|
||||||
vprior.sig = std(dataset(options_.first_obs+options_.presample-nlags:options_.first_obs+options_.presample-1,:))';
|
error('first_obs+presample should be > nlags (for initializing the VAR)')
|
||||||
vprior.w = options_.bvar_prior_omega;
|
end
|
||||||
|
|
||||||
lambda = options_.bvar_prior_lambda;
|
train = options_.bvar_prior_train;
|
||||||
mu = options_.bvar_prior_mu;
|
|
||||||
flat = options_.bvar_prior_flat;
|
if options_.first_obs + options_.presample - train <= nlags
|
||||||
|
error('first_obs+presample-train should be > nlags (for initializating the VAR)')
|
||||||
ny = size(dataset, 2);
|
end
|
||||||
if options_.prefilter | options_.noconstant
|
|
||||||
nx = 0;
|
idx = options_.first_obs+options_.presample-train-nlags:options_.first_obs+options_.nobs-1;
|
||||||
|
|
||||||
|
% Prepare dataset
|
||||||
|
if options_.loglinear & ~options_.logdata
|
||||||
|
dataset = log(dataset);
|
||||||
|
end
|
||||||
|
if options_.prefilter
|
||||||
|
dataset(idx,:) = dataset(idx,:) - ones(length(idx),1)*mean(dataset(idx,:));
|
||||||
|
end
|
||||||
|
|
||||||
|
mnprior.tight = options_.bvar_prior_tau;
|
||||||
|
mnprior.decay = options_.bvar_prior_decay;
|
||||||
|
|
||||||
|
% Use only initializations lags for the variance prior
|
||||||
|
vprior.sig = std(dataset(options_.first_obs+options_.presample-nlags:options_.first_obs+options_.presample-1,:))';
|
||||||
|
vprior.w = options_.bvar_prior_omega;
|
||||||
|
|
||||||
|
lambda = options_.bvar_prior_lambda;
|
||||||
|
mu = options_.bvar_prior_mu;
|
||||||
|
flat = options_.bvar_prior_flat;
|
||||||
|
|
||||||
|
ny = size(dataset, 2);
|
||||||
|
if options_.prefilter | options_.noconstant
|
||||||
|
nx = 0;
|
||||||
|
else
|
||||||
|
nx = 1;
|
||||||
|
end
|
||||||
|
|
||||||
|
[ydum, xdum, pbreaks] = varprior(ny, nx, nlags, mnprior, vprior);
|
||||||
|
|
||||||
|
ydata = dataset(idx, :);
|
||||||
|
T = size(ydata, 1);
|
||||||
|
xdata = ones(T,nx);
|
||||||
|
|
||||||
|
% Posterior density
|
||||||
|
var = rfvar3([ydata; ydum], nlags, [xdata; xdum], [T; T+pbreaks], lambda, mu);
|
||||||
|
Tu = size(var.u, 1);
|
||||||
|
|
||||||
|
posterior.df = Tu - ny*nlags - nx - flat*(ny+1);
|
||||||
|
posterior.S = var.u' * var.u;
|
||||||
|
posterior.XXi = var.xxi;
|
||||||
|
posterior.PhiHat = var.B;
|
||||||
|
|
||||||
|
% Prior density
|
||||||
|
Tp = train + nlags;
|
||||||
|
if nx
|
||||||
|
xdata = xdata(1:Tp, :);
|
||||||
|
else
|
||||||
|
xdata = [];
|
||||||
|
end
|
||||||
|
varp = rfvar3([ydata(1:Tp, :); ydum], nlags, [xdata; xdum], [Tp; Tp + pbreaks], lambda, mu);
|
||||||
|
Tup = size(varp.u, 1);
|
||||||
|
|
||||||
|
prior.df = Tup - ny*nlags - nx - flat*(ny+1);
|
||||||
|
prior.S = varp.u' * varp.u;
|
||||||
|
prior.XXi = varp.xxi;
|
||||||
|
prior.PhiHat = varp.B;
|
||||||
|
|
||||||
|
if prior.df < ny
|
||||||
|
error('Too few degrees of freedom in the inverse-Wishart part of prior distribution. You should increase training sample size.')
|
||||||
|
end
|
||||||
|
|
||||||
|
% Add forecast informations
|
||||||
|
if nargout >= 5
|
||||||
|
forecast_data.xdata = ones(options_.forecast, nx);
|
||||||
|
forecast_data.initval = ydata(end-nlags+1:end, :);
|
||||||
|
if options_.first_obs + options_.nobs <= size(dataset, 1)
|
||||||
|
forecast_data.realized_val = dataset(options_.first_obs+options_.nobs:end, :);
|
||||||
|
forecast_data.realized_xdata = ones(size(forecast_data.realized_val, 1), nx);
|
||||||
else
|
else
|
||||||
nx = 1;
|
forecast_data.realized_val = [];
|
||||||
end
|
end
|
||||||
|
end
|
||||||
[ydum, xdum, pbreaks] = varprior(ny, nx, nlags, mnprior, vprior);
|
|
||||||
|
|
||||||
ydata = dataset(idx, :);
|
|
||||||
T = size(ydata, 1);
|
|
||||||
xdata = ones(T,nx);
|
|
||||||
|
|
||||||
% Posterior density
|
|
||||||
var = rfvar3([ydata; ydum], nlags, [xdata; xdum], [T; T+pbreaks], lambda, mu);
|
|
||||||
Tu = size(var.u, 1);
|
|
||||||
|
|
||||||
posterior.df = Tu - ny*nlags - nx - flat*(ny+1);
|
|
||||||
posterior.S = var.u' * var.u;
|
|
||||||
posterior.XXi = var.xxi;
|
|
||||||
posterior.PhiHat = var.B;
|
|
||||||
|
|
||||||
% Prior density
|
|
||||||
Tp = train + nlags;
|
|
||||||
if nx
|
|
||||||
xdata = xdata(1:Tp, :);
|
|
||||||
else
|
|
||||||
xdata = [];
|
|
||||||
end
|
|
||||||
varp = rfvar3([ydata(1:Tp, :); ydum], nlags, [xdata; xdum], [Tp; Tp + pbreaks], lambda, mu);
|
|
||||||
Tup = size(varp.u, 1);
|
|
||||||
|
|
||||||
prior.df = Tup - ny*nlags - nx - flat*(ny+1);
|
|
||||||
prior.S = varp.u' * varp.u;
|
|
||||||
prior.XXi = varp.xxi;
|
|
||||||
prior.PhiHat = varp.B;
|
|
||||||
|
|
||||||
if prior.df < ny
|
|
||||||
error('Too few degrees of freedom in the inverse-Wishart part of prior distribution. You should increase training sample size.')
|
|
||||||
end
|
|
||||||
|
|
||||||
% Add forecast informations
|
|
||||||
if nargout >= 5
|
|
||||||
forecast_data.xdata = ones(options_.forecast, nx);
|
|
||||||
forecast_data.initval = ydata(end-nlags+1:end, :);
|
|
||||||
if options_.first_obs + options_.nobs <= size(dataset, 1)
|
|
||||||
forecast_data.realized_val = dataset(options_.first_obs+options_.nobs:end, :);
|
|
||||||
forecast_data.realized_xdata = ones(size(forecast_data.realized_val, 1), nx);
|
|
||||||
else
|
|
||||||
forecast_data.realized_val = [];
|
|
||||||
end
|
|
||||||
end
|
|
||||||
|
|
||||||
|
|
||||||
function [ydum,xdum,breaks]=varprior(nv,nx,lags,mnprior,vprior)
|
function [ydum,xdum,breaks]=varprior(nv,nx,lags,mnprior,vprior)
|
||||||
%function [ydum,xdum,breaks]=varprior(nv,nx,lags,mnprior,vprior)
|
%function [ydum,xdum,breaks]=varprior(nv,nx,lags,mnprior,vprior)
|
||||||
|
|
@ -185,41 +185,41 @@ function [ydum,xdum,breaks]=varprior(nv,nx,lags,mnprior,vprior)
|
||||||
% Original file downloaded from:
|
% Original file downloaded from:
|
||||||
% http://sims.princeton.edu/yftp/VARtools/matlab/varprior.m
|
% http://sims.princeton.edu/yftp/VARtools/matlab/varprior.m
|
||||||
|
|
||||||
if ~isempty(mnprior)
|
if ~isempty(mnprior)
|
||||||
xdum = zeros(lags+1,nx,lags,nv);
|
xdum = zeros(lags+1,nx,lags,nv);
|
||||||
ydum = zeros(lags+1,nv,lags,nv);
|
ydum = zeros(lags+1,nv,lags,nv);
|
||||||
for il = 1:lags
|
for il = 1:lags
|
||||||
ydum(il+1,:,il,:) = il^mnprior.decay*diag(vprior.sig);
|
ydum(il+1,:,il,:) = il^mnprior.decay*diag(vprior.sig);
|
||||||
end
|
end
|
||||||
ydum(1,:,1,:) = diag(vprior.sig);
|
ydum(1,:,1,:) = diag(vprior.sig);
|
||||||
ydum = mnprior.tight*reshape(ydum,[lags+1,nv,lags*nv]);
|
ydum = mnprior.tight*reshape(ydum,[lags+1,nv,lags*nv]);
|
||||||
ydum = flipdim(ydum,1);
|
ydum = flipdim(ydum,1);
|
||||||
xdum = mnprior.tight*reshape(xdum,[lags+1,nx,lags*nv]);
|
xdum = mnprior.tight*reshape(xdum,[lags+1,nx,lags*nv]);
|
||||||
xdum = flipdim(xdum,1);
|
xdum = flipdim(xdum,1);
|
||||||
breaks = (lags+1)*[1:(nv*lags)]';
|
breaks = (lags+1)*[1:(nv*lags)]';
|
||||||
|
lbreak = breaks(end);
|
||||||
|
else
|
||||||
|
ydum = [];
|
||||||
|
xdum = [];
|
||||||
|
breaks = [];
|
||||||
|
lbreak = 0;
|
||||||
|
end
|
||||||
|
if ~isempty(vprior) & vprior.w>0
|
||||||
|
ydum2 = zeros(lags+1,nv,nv);
|
||||||
|
xdum2 = zeros(lags+1,nx,nv);
|
||||||
|
ydum2(end,:,:) = diag(vprior.sig);
|
||||||
|
for i = 1:vprior.w
|
||||||
|
ydum = cat(3,ydum,ydum2);
|
||||||
|
xdum = cat(3,xdum,xdum2);
|
||||||
|
breaks = [breaks;(lags+1)*[1:nv]'+lbreak];
|
||||||
lbreak = breaks(end);
|
lbreak = breaks(end);
|
||||||
else
|
|
||||||
ydum = [];
|
|
||||||
xdum = [];
|
|
||||||
breaks = [];
|
|
||||||
lbreak = 0;
|
|
||||||
end
|
end
|
||||||
if ~isempty(vprior) & vprior.w>0
|
end
|
||||||
ydum2 = zeros(lags+1,nv,nv);
|
dimy = size(ydum);
|
||||||
xdum2 = zeros(lags+1,nx,nv);
|
ydum = reshape(permute(ydum,[1 3 2]),dimy(1)*dimy(3),nv);
|
||||||
ydum2(end,:,:) = diag(vprior.sig);
|
xdum = reshape(permute(xdum,[1 3 2]),dimy(1)*dimy(3),nx);
|
||||||
for i = 1:vprior.w
|
breaks = breaks(1:(end-1));
|
||||||
ydum = cat(3,ydum,ydum2);
|
|
||||||
xdum = cat(3,xdum,xdum2);
|
|
||||||
breaks = [breaks;(lags+1)*[1:nv]'+lbreak];
|
|
||||||
lbreak = breaks(end);
|
|
||||||
end
|
|
||||||
end
|
|
||||||
dimy = size(ydum);
|
|
||||||
ydum = reshape(permute(ydum,[1 3 2]),dimy(1)*dimy(3),nv);
|
|
||||||
xdum = reshape(permute(xdum,[1 3 2]),dimy(1)*dimy(3),nx);
|
|
||||||
breaks = breaks(1:(end-1));
|
|
||||||
|
|
||||||
|
|
||||||
function var=rfvar3(ydata,lags,xdata,breaks,lambda,mu)
|
function var=rfvar3(ydata,lags,xdata,breaks,lambda,mu)
|
||||||
%function var=rfvar3(ydata,lags,xdata,breaks,lambda,mu)
|
%function var=rfvar3(ydata,lags,xdata,breaks,lambda,mu)
|
||||||
|
|
@ -253,74 +253,74 @@ function var=rfvar3(ydata,lags,xdata,breaks,lambda,mu)
|
||||||
% Original file downloaded from:
|
% Original file downloaded from:
|
||||||
% http://sims.princeton.edu/yftp/VARtools/matlab/rfvar3.m
|
% http://sims.princeton.edu/yftp/VARtools/matlab/rfvar3.m
|
||||||
|
|
||||||
[T,nvar] = size(ydata);
|
[T,nvar] = size(ydata);
|
||||||
nox = isempty(xdata);
|
nox = isempty(xdata);
|
||||||
|
if ~nox
|
||||||
|
[T2,nx] = size(xdata);
|
||||||
|
else
|
||||||
|
T2 = T;
|
||||||
|
nx = 0;
|
||||||
|
xdata = zeros(T2,0);
|
||||||
|
end
|
||||||
|
% note that x must be same length as y, even though first part of x will not be used.
|
||||||
|
% This is so that the lags parameter can be changed without reshaping the xdata matrix.
|
||||||
|
if T2 ~= T, error('Mismatch of x and y data lengths'),end
|
||||||
|
if nargin < 4
|
||||||
|
nbreaks = 0;
|
||||||
|
breaks = [];
|
||||||
|
else
|
||||||
|
nbreaks = length(breaks);
|
||||||
|
end
|
||||||
|
breaks = [0;breaks;T];
|
||||||
|
smpl = [];
|
||||||
|
for nb = 1:nbreaks+1
|
||||||
|
smpl = [smpl;[breaks(nb)+lags+1:breaks(nb+1)]'];
|
||||||
|
end
|
||||||
|
Tsmpl = size(smpl,1);
|
||||||
|
X = zeros(Tsmpl,nvar,lags);
|
||||||
|
for is = 1:length(smpl)
|
||||||
|
X(is,:,:) = ydata(smpl(is)-(1:lags),:)';
|
||||||
|
end
|
||||||
|
X = [X(:,:) xdata(smpl,:)];
|
||||||
|
y = ydata(smpl,:);
|
||||||
|
% Everything now set up with input data for y=Xb+e
|
||||||
|
|
||||||
|
% Add persistence dummies
|
||||||
|
if lambda ~= 0 | mu > 0
|
||||||
|
ybar = mean(ydata(1:lags,:),1);
|
||||||
if ~nox
|
if ~nox
|
||||||
[T2,nx] = size(xdata);
|
xbar = mean(xdata(1:lags,:),1);
|
||||||
else
|
else
|
||||||
T2 = T;
|
xbar = [];
|
||||||
nx = 0;
|
|
||||||
xdata = zeros(T2,0);
|
|
||||||
end
|
end
|
||||||
% note that x must be same length as y, even though first part of x will not be used.
|
if lambda ~= 0
|
||||||
% This is so that the lags parameter can be changed without reshaping the xdata matrix.
|
if lambda>0
|
||||||
if T2 ~= T, error('Mismatch of x and y data lengths'),end
|
xdum = lambda*[repmat(ybar,1,lags) xbar];
|
||||||
if nargin < 4
|
|
||||||
nbreaks = 0;
|
|
||||||
breaks = [];
|
|
||||||
else
|
|
||||||
nbreaks = length(breaks);
|
|
||||||
end
|
|
||||||
breaks = [0;breaks;T];
|
|
||||||
smpl = [];
|
|
||||||
for nb = 1:nbreaks+1
|
|
||||||
smpl = [smpl;[breaks(nb)+lags+1:breaks(nb+1)]'];
|
|
||||||
end
|
|
||||||
Tsmpl = size(smpl,1);
|
|
||||||
X = zeros(Tsmpl,nvar,lags);
|
|
||||||
for is = 1:length(smpl)
|
|
||||||
X(is,:,:) = ydata(smpl(is)-(1:lags),:)';
|
|
||||||
end
|
|
||||||
X = [X(:,:) xdata(smpl,:)];
|
|
||||||
y = ydata(smpl,:);
|
|
||||||
% Everything now set up with input data for y=Xb+e
|
|
||||||
|
|
||||||
% Add persistence dummies
|
|
||||||
if lambda ~= 0 | mu > 0
|
|
||||||
ybar = mean(ydata(1:lags,:),1);
|
|
||||||
if ~nox
|
|
||||||
xbar = mean(xdata(1:lags,:),1);
|
|
||||||
else
|
else
|
||||||
xbar = [];
|
lambda = -lambda;
|
||||||
end
|
xdum = lambda*[repmat(ybar,1,lags) zeros(size(xbar))];
|
||||||
if lambda ~= 0
|
|
||||||
if lambda>0
|
|
||||||
xdum = lambda*[repmat(ybar,1,lags) xbar];
|
|
||||||
else
|
|
||||||
lambda = -lambda;
|
|
||||||
xdum = lambda*[repmat(ybar,1,lags) zeros(size(xbar))];
|
|
||||||
end
|
|
||||||
ydum = zeros(1,nvar);
|
|
||||||
ydum(1,:) = lambda*ybar;
|
|
||||||
y = [y;ydum];
|
|
||||||
X = [X;xdum];
|
|
||||||
end
|
|
||||||
if mu>0
|
|
||||||
xdum = [repmat(diag(ybar),1,lags) zeros(nvar,nx)]*mu;
|
|
||||||
ydum = mu*diag(ybar);
|
|
||||||
X = [X;xdum];
|
|
||||||
y = [y;ydum];
|
|
||||||
end
|
end
|
||||||
|
ydum = zeros(1,nvar);
|
||||||
|
ydum(1,:) = lambda*ybar;
|
||||||
|
y = [y;ydum];
|
||||||
|
X = [X;xdum];
|
||||||
end
|
end
|
||||||
|
if mu>0
|
||||||
% Compute OLS regression and residuals
|
xdum = [repmat(diag(ybar),1,lags) zeros(nvar,nx)]*mu;
|
||||||
[vl,d,vr] = svd(X,0);
|
ydum = mu*diag(ybar);
|
||||||
di = 1./diag(d);
|
X = [X;xdum];
|
||||||
B = (vr.*repmat(di',nvar*lags+nx,1))*vl'*y;
|
y = [y;ydum];
|
||||||
u = y-X*B;
|
end
|
||||||
xxi = vr.*repmat(di',nvar*lags+nx,1);
|
end
|
||||||
xxi = xxi*xxi';
|
|
||||||
|
% Compute OLS regression and residuals
|
||||||
var.B = B;
|
[vl,d,vr] = svd(X,0);
|
||||||
var.u = u;
|
di = 1./diag(d);
|
||||||
var.xxi = xxi;
|
B = (vr.*repmat(di',nvar*lags+nx,1))*vl'*y;
|
||||||
|
u = y-X*B;
|
||||||
|
xxi = vr.*repmat(di',nvar*lags+nx,1);
|
||||||
|
xxi = xxi*xxi';
|
||||||
|
|
||||||
|
var.B = B;
|
||||||
|
var.u = u;
|
||||||
|
var.xxi = xxi;
|
||||||
|
|
|
||||||
266
matlab/calib.m
266
matlab/calib.m
|
|
@ -17,177 +17,177 @@ function M_.Sigma_e = calib(var_indices,targets,var_weights,nar,cova,M_.Sigma_e)
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
global oo_ M_ vx
|
global oo_ M_ vx
|
||||||
|
|
||||||
ncstr = 0;
|
|
||||||
ni = size(var_indices,1);
|
|
||||||
for i=1:nar+3
|
|
||||||
ncstr = ncstr + size(var_indices{i},1);
|
|
||||||
end
|
|
||||||
if cova
|
|
||||||
if ncstr < M_.exo_nbr*(M_.exo_nbr+1)/2
|
|
||||||
error(['number of preset variances is smaller than number of shock' ...
|
|
||||||
' variances and covariances to be estimated !'])
|
|
||||||
end
|
|
||||||
else
|
|
||||||
if ncstr < M_.exo_nbr
|
|
||||||
error(['number of preset variances is smaller than number of shock' ...
|
|
||||||
' variances to be estimated !'])
|
|
||||||
end
|
|
||||||
end
|
|
||||||
|
|
||||||
% computes approximate solution at order 1
|
|
||||||
dr = resol(oo_.steady_state,0,0,1);
|
|
||||||
|
|
||||||
ghx = dr.ghx;
|
|
||||||
ghu = dr.ghu;
|
|
||||||
npred = dr.npred;
|
|
||||||
nstatic = dr.nstatic;
|
|
||||||
kstate = dr.kstate;
|
|
||||||
order = dr.order_var;
|
|
||||||
iv(order) = [1:M_.endo_nbr];
|
|
||||||
iv = iv';
|
|
||||||
nx = size(ghx,2);
|
|
||||||
|
|
||||||
ikx = [nstatic+1:nstatic+npred];
|
ncstr = 0;
|
||||||
|
ni = size(var_indices,1);
|
||||||
A = zeros(nx,nx);
|
for i=1:nar+3
|
||||||
A(1:npred,:)=ghx(ikx,:);
|
ncstr = ncstr + size(var_indices{i},1);
|
||||||
offset_r = npred;
|
end
|
||||||
offset_c = 0;
|
if cova
|
||||||
i0 = find(kstate(:,2) == M_.maximum_lag+1);
|
if ncstr < M_.exo_nbr*(M_.exo_nbr+1)/2
|
||||||
n0 = size(i0,1);
|
error(['number of preset variances is smaller than number of shock' ...
|
||||||
for i=M_.maximum_lag:-1:2
|
' variances and covariances to be estimated !'])
|
||||||
|
end
|
||||||
|
else
|
||||||
|
if ncstr < M_.exo_nbr
|
||||||
|
error(['number of preset variances is smaller than number of shock' ...
|
||||||
|
' variances to be estimated !'])
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
% computes approximate solution at order 1
|
||||||
|
dr = resol(oo_.steady_state,0,0,1);
|
||||||
|
|
||||||
|
ghx = dr.ghx;
|
||||||
|
ghu = dr.ghu;
|
||||||
|
npred = dr.npred;
|
||||||
|
nstatic = dr.nstatic;
|
||||||
|
kstate = dr.kstate;
|
||||||
|
order = dr.order_var;
|
||||||
|
iv(order) = [1:M_.endo_nbr];
|
||||||
|
iv = iv';
|
||||||
|
nx = size(ghx,2);
|
||||||
|
|
||||||
|
ikx = [nstatic+1:nstatic+npred];
|
||||||
|
|
||||||
|
A = zeros(nx,nx);
|
||||||
|
A(1:npred,:)=ghx(ikx,:);
|
||||||
|
offset_r = npred;
|
||||||
|
offset_c = 0;
|
||||||
|
i0 = find(kstate(:,2) == M_.maximum_lag+1);
|
||||||
|
n0 = size(i0,1);
|
||||||
|
for i=M_.maximum_lag:-1:2
|
||||||
i1 = find(kstate(:,2) == i);
|
i1 = find(kstate(:,2) == i);
|
||||||
n1 = size(i1,1);
|
n1 = size(i1,1);
|
||||||
j = zeros(n1,1);
|
j = zeros(n1,1);
|
||||||
for j1 = 1:n1
|
for j1 = 1:n1
|
||||||
j(j1) = find(kstate(i0,1)==kstate(i1(j1),1));
|
j(j1) = find(kstate(i0,1)==kstate(i1(j1),1));
|
||||||
end
|
end
|
||||||
A(offset_r+1:offset_r+n1,offset_c+j)=eye(n1);
|
A(offset_r+1:offset_r+n1,offset_c+j)=eye(n1);
|
||||||
offset_r = offset_r + n1;
|
offset_r = offset_r + n1;
|
||||||
offset_c = offset_c + n0;
|
offset_c = offset_c + n0;
|
||||||
i0 = i1;
|
i0 = i1;
|
||||||
n0 = n1;
|
n0 = n1;
|
||||||
end
|
end
|
||||||
ghu1 = [ghu(ikx,:);zeros(nx-npred,M_.exo_nbr)];
|
ghu1 = [ghu(ikx,:);zeros(nx-npred,M_.exo_nbr)];
|
||||||
% IA = speye(nx*nx)-kron(A,A);
|
% IA = speye(nx*nx)-kron(A,A);
|
||||||
% kron_ghu = kron(ghu1,ghu1);
|
% kron_ghu = kron(ghu1,ghu1);
|
||||||
|
|
||||||
% vx1 such that vec(sigma_x) = vx1 * vec(M_.Sigma_e) (predetermined vars)
|
|
||||||
vx1 = [];
|
|
||||||
% vx1 = IA\kron_ghu;
|
|
||||||
IA = [];
|
|
||||||
kron_ghu = [];
|
|
||||||
|
|
||||||
% computes required variables and indices among required variables
|
|
||||||
iiy = [];
|
|
||||||
for i=1:nar+3
|
|
||||||
if i ~= 3 & ~isempty(var_indices{i})
|
|
||||||
iiy = union(iiy, iv(var_indices{i}(:,1)));
|
|
||||||
end
|
|
||||||
end
|
|
||||||
if ~isempty(var_indices{2})
|
|
||||||
iiy = union(iiy, iv(var_indices{2}(:,2)));
|
|
||||||
end
|
|
||||||
ny = size(iiy,1);
|
|
||||||
|
|
||||||
for i=1:nar+3
|
% vx1 such that vec(sigma_x) = vx1 * vec(M_.Sigma_e) (predetermined vars)
|
||||||
|
vx1 = [];
|
||||||
|
% vx1 = IA\kron_ghu;
|
||||||
|
IA = [];
|
||||||
|
kron_ghu = [];
|
||||||
|
|
||||||
|
% computes required variables and indices among required variables
|
||||||
|
iiy = [];
|
||||||
|
for i=1:nar+3
|
||||||
if i ~= 3 & ~isempty(var_indices{i})
|
if i ~= 3 & ~isempty(var_indices{i})
|
||||||
var_indices{i}(:,1) = indnv(iv(var_indices{i}(:,1)),iiy);
|
iiy = union(iiy, iv(var_indices{i}(:,1)));
|
||||||
|
end
|
||||||
|
end
|
||||||
|
if ~isempty(var_indices{2})
|
||||||
|
iiy = union(iiy, iv(var_indices{2}(:,2)));
|
||||||
|
end
|
||||||
|
ny = size(iiy,1);
|
||||||
|
|
||||||
|
for i=1:nar+3
|
||||||
|
if i ~= 3 & ~isempty(var_indices{i})
|
||||||
|
var_indices{i}(:,1) = indnv(iv(var_indices{i}(:,1)),iiy);
|
||||||
end
|
end
|
||||||
if i ~= 2 & i ~= 3 & ~isempty(var_indices{i})
|
if i ~= 2 & i ~= 3 & ~isempty(var_indices{i})
|
||||||
var_indices{i} = sub2ind([ny ny],var_indices{i},var_indices{i});
|
var_indices{i} = sub2ind([ny ny],var_indices{i},var_indices{i});
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if ~isempty(var_indices{2})
|
if ~isempty(var_indices{2})
|
||||||
var_indices{2}(:,2) = indnv(iv(var_indices{2}(:,2)),iiy);
|
var_indices{2}(:,2) = indnv(iv(var_indices{2}(:,2)),iiy);
|
||||||
var_indices{2} = sub2ind([ny ny],var_indices{2}(:,1),var_indices{2}(:,2));
|
var_indices{2} = sub2ind([ny ny],var_indices{2}(:,1),var_indices{2}(:,2));
|
||||||
end
|
end
|
||||||
if ~isempty(var_indices{3})
|
if ~isempty(var_indices{3})
|
||||||
var_indices{3} = sub2ind([M_.exo_nbr M_.exo_nbr],var_indices{3}(:,1),var_indices{3}(:,2));
|
var_indices{3} = sub2ind([M_.exo_nbr M_.exo_nbr],var_indices{3}(:,1),var_indices{3}(:,2));
|
||||||
end
|
end
|
||||||
if isempty(M_.Sigma_e)
|
if isempty(M_.Sigma_e)
|
||||||
M_.Sigma_e = 0.01*eye(M_.exo_nbr);
|
M_.Sigma_e = 0.01*eye(M_.exo_nbr);
|
||||||
b = 0.1*ghu1*ghu1';
|
b = 0.1*ghu1*ghu1';
|
||||||
else
|
else
|
||||||
b = ghu1*M_.Sigma_e*ghu1';
|
b = ghu1*M_.Sigma_e*ghu1';
|
||||||
M_.Sigma_e = chol(M_.Sigma_e+1e-14*eye(M_.exo_nbr));
|
M_.Sigma_e = chol(M_.Sigma_e+1e-14*eye(M_.exo_nbr));
|
||||||
end
|
end
|
||||||
options=optimset('LargeScale','on','MaxFunEvals',20000*ny,'TolX',1e-4, ...
|
options=optimset('LargeScale','on','MaxFunEvals',20000*ny,'TolX',1e-4, ...
|
||||||
'TolFun',1e-4,'Display','Iter','MaxIter',10000);
|
'TolFun',1e-4,'Display','Iter','MaxIter',10000);
|
||||||
% [M_.Sigma_e,f]=fminunc(@calib_obj,M_.Sigma_e,options,A,ghu1,ghx(iiy,:),ghu(iiy,:),targets,var_weights,var_indices,nar);
|
% [M_.Sigma_e,f]=fminunc(@calib_obj,M_.Sigma_e,options,A,ghu1,ghx(iiy,:),ghu(iiy,:),targets,var_weights,var_indices,nar);
|
||||||
[M_.Sigma_e,f]=fmincon(@calib_obj,diag(M_.Sigma_e).^2,-eye(M_.exo_nbr),zeros(M_.exo_nbr,1),[],[],[],[],[],options,A,ghu1,ghx(iiy,:),ghu(iiy,:),targets,var_weights,var_indices,nar);
|
[M_.Sigma_e,f]=fmincon(@calib_obj,diag(M_.Sigma_e).^2,-eye(M_.exo_nbr),zeros(M_.exo_nbr,1),[],[],[],[],[],options,A,ghu1,ghx(iiy,:),ghu(iiy,:),targets,var_weights,var_indices,nar);
|
||||||
M_.Sigma_e = diag(M_.Sigma_e);
|
M_.Sigma_e = diag(M_.Sigma_e);
|
||||||
|
|
||||||
objective = calib_obj2(diag(M_.Sigma_e),A,ghu1,ghx(iiy,:),ghu(iiy,:),targets,var_weights,var_indices,nar);
|
objective = calib_obj2(diag(M_.Sigma_e),A,ghu1,ghx(iiy,:),ghu(iiy,:),targets,var_weights,var_indices,nar);
|
||||||
disp('CALIBRATION')
|
disp('CALIBRATION')
|
||||||
disp('')
|
disp('')
|
||||||
if ~isempty(var_indices{1})
|
if ~isempty(var_indices{1})
|
||||||
disp(sprintf('%16s %14s %14s %14s %14s','Std. Dev','Target','Obtained','Diff'));
|
disp(sprintf('%16s %14s %14s %14s %14s','Std. Dev','Target','Obtained','Diff'));
|
||||||
str = ' ';
|
str = ' ';
|
||||||
for i=1:size(var_indices{1},1)
|
for i=1:size(var_indices{1},1)
|
||||||
[i1,i2] = ind2sub([ny ny],var_indices{1}(i));
|
[i1,i2] = ind2sub([ny ny],var_indices{1}(i));
|
||||||
str = sprintf('%16s: %14.2f %14.2f %14.2f',M_.endo_names(order(iiy(i1)),:),targets{1}(i),objective{1}(i),objective{1}(i)-targets{1}(i));
|
str = sprintf('%16s: %14.2f %14.2f %14.2f',M_.endo_names(order(iiy(i1)),:),targets{1}(i),objective{1}(i),objective{1}(i)-targets{1}(i));
|
||||||
disp(str);
|
disp(str);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if ~isempty(var_indices{2})
|
if ~isempty(var_indices{2})
|
||||||
disp(sprintf('%32s %14s %14s','Correlations','Target','Obtained','Diff'));
|
disp(sprintf('%32s %14s %14s','Correlations','Target','Obtained','Diff'));
|
||||||
str = ' ';
|
str = ' ';
|
||||||
for i=1:size(var_indices{2},1)
|
for i=1:size(var_indices{2},1)
|
||||||
[i1,i2]=ind2sub([ny ny],var_indices{2}(i));
|
[i1,i2]=ind2sub([ny ny],var_indices{2}(i));
|
||||||
str = sprintf('%16s,%16s: %14.2f %14.2f %14.2f',M_.endo_names(order(iiy(i1)),:), ...
|
str = sprintf('%16s,%16s: %14.2f %14.2f %14.2f',M_.endo_names(order(iiy(i1)),:), ...
|
||||||
M_.endo_names(order(iiy(i2)),:),targets{2}(i),objective{2}(i),objective{2}(i)-targets{2}(i));
|
M_.endo_names(order(iiy(i2)),:),targets{2}(i),objective{2}(i),objective{2}(i)-targets{2}(i));
|
||||||
disp(str);
|
disp(str);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if ~isempty(var_indices{3})
|
if ~isempty(var_indices{3})
|
||||||
disp(sprintf('%32s %16s %16s','Constrained shocks (co)variances','Target','Obtained'));
|
disp(sprintf('%32s %16s %16s','Constrained shocks (co)variances','Target','Obtained'));
|
||||||
str = ' ';
|
str = ' ';
|
||||||
for i=1:size(var_indices{3},1)
|
for i=1:size(var_indices{3},1)
|
||||||
[i1,i2]=ind2sub([M_.exo_nbr M_.exo_nbr],var_indices{3}(i));
|
[i1,i2]=ind2sub([M_.exo_nbr M_.exo_nbr],var_indices{3}(i));
|
||||||
if i1 == i2
|
if i1 == i2
|
||||||
str = sprintf('%32s: %16.4f %16.4f',M_.exo_name(order(i1),:), ...
|
str = sprintf('%32s: %16.4f %16.4f',M_.exo_name(order(i1),:), ...
|
||||||
targets{3}(i),objective{3}(i));
|
targets{3}(i),objective{3}(i));
|
||||||
else
|
else
|
||||||
str = sprintf('%16s,%16s: %16.4f %16.4f',M_.exo_name(order(i1),:), ...
|
str = sprintf('%16s,%16s: %16.4f %16.4f',M_.exo_name(order(i1),:), ...
|
||||||
M_.exo_name(order(i2), :),targets{3}(i),objective{3}(i));
|
M_.exo_name(order(i2), :),targets{3}(i),objective{3}(i));
|
||||||
end
|
end
|
||||||
disp(str);
|
disp(str);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
flag = 1;
|
flag = 1;
|
||||||
for j=4:nar+3
|
for j=4:nar+3
|
||||||
if ~isempty(var_indices{j})
|
if ~isempty(var_indices{j})
|
||||||
if flag
|
if flag
|
||||||
disp(sprintf('%16s %16s %16s','Autocorrelations','Target','Obtained'));
|
disp(sprintf('%16s %16s %16s','Autocorrelations','Target','Obtained'));
|
||||||
str = ' ';
|
str = ' ';
|
||||||
flag = 0;
|
flag = 0;
|
||||||
end
|
end
|
||||||
for i=1:size(var_indices{j},1)
|
for i=1:size(var_indices{j},1)
|
||||||
[i1,i2] = ind2sub([ny ny],var_indices{j}(i));
|
[i1,i2] = ind2sub([ny ny],var_indices{j}(i));
|
||||||
str = sprintf('%16s(%d): %16.4f %16.4f',M_.endo_names(order(iiy(i1)),:), ...
|
str = sprintf('%16s(%d): %16.4f %16.4f',M_.endo_names(order(iiy(i1)),:), ...
|
||||||
j-3,targets{j}(i),objective{j}(i));
|
j-3,targets{j}(i),objective{j}(i));
|
||||||
disp(str);
|
disp(str);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
||||||
disp('');
|
|
||||||
disp('Calibrated variances')
|
|
||||||
str = ' ';
|
|
||||||
for i=1:M_.exo_nbr
|
|
||||||
str = [str sprintf('%16s',M_.exo_name(i,:))];
|
|
||||||
end
|
|
||||||
disp(str);
|
|
||||||
disp('');
|
|
||||||
str = ' ';
|
|
||||||
for i=1:M_.exo_nbr
|
|
||||||
str = [str sprintf('%16f',M_.Sigma_e(i,i))];
|
|
||||||
end
|
|
||||||
disp(str);
|
|
||||||
|
|
||||||
|
|
||||||
|
disp('');
|
||||||
% 10/9/02 MJ
|
disp('Calibrated variances')
|
||||||
|
str = ' ';
|
||||||
|
for i=1:M_.exo_nbr
|
||||||
|
str = [str sprintf('%16s',M_.exo_name(i,:))];
|
||||||
|
end
|
||||||
|
disp(str);
|
||||||
|
disp('');
|
||||||
|
str = ' ';
|
||||||
|
for i=1:M_.exo_nbr
|
||||||
|
str = [str sprintf('%16f',M_.Sigma_e(i,i))];
|
||||||
|
end
|
||||||
|
disp(str);
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
% 10/9/02 MJ
|
||||||
|
|
@ -19,67 +19,66 @@ function f=calib_obj(M_.Sigma_e,A,ghu1,ghx,ghu,targets,var_weights,iy,nar)
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
global vx fold options_
|
global vx fold options_
|
||||||
|
|
||||||
oo_.gamma_y = cell(nar+1,1);
|
oo_.gamma_y = cell(nar+1,1);
|
||||||
% M_.Sigma_e = M_.Sigma_e'*M_.Sigma_e;
|
% M_.Sigma_e = M_.Sigma_e'*M_.Sigma_e;
|
||||||
M_.Sigma_e=diag(M_.Sigma_e);
|
M_.Sigma_e=diag(M_.Sigma_e);
|
||||||
nx = size(ghx,2);
|
nx = size(ghx,2);
|
||||||
b=ghu1*M_.Sigma_e*ghu1';
|
b=ghu1*M_.Sigma_e*ghu1';
|
||||||
vx = [];
|
vx = [];
|
||||||
if isempty(vx)
|
if isempty(vx)
|
||||||
vx = lyapunov_symm(A,b,options_.qz_criterium,options_.lyapunov_complex_threshold);
|
vx = lyapunov_symm(A,b,options_.qz_criterium,options_.lyapunov_complex_threshold);
|
||||||
else
|
else
|
||||||
[vx,status] = bicgstab_(@f_var,b(:),vx(:),1e-8,50,A,nx);
|
[vx,status] = bicgstab_(@f_var,b(:),vx(:),1e-8,50,A,nx);
|
||||||
if status
|
if status
|
||||||
vx = lyapunov_symm(A,b,options_.qz_criterium,options_.lyapunov_complex_threshold);
|
vx = lyapunov_symm(A,b,options_.qz_criterium,options_.lyapunov_complex_threshold);
|
||||||
else
|
else
|
||||||
vx=reshape(vx,nx,nx);
|
vx=reshape(vx,nx,nx);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
oo_.gamma_y{1} = ghx*vx*ghx'+ ghu*M_.Sigma_e*ghu';
|
oo_.gamma_y{1} = ghx*vx*ghx'+ ghu*M_.Sigma_e*ghu';
|
||||||
f = 0;
|
f = 0;
|
||||||
if ~isempty(targets{1})
|
if ~isempty(targets{1})
|
||||||
e = targets{1}-sqrt(oo_.gamma_y{1}(iy{1}));
|
e = targets{1}-sqrt(oo_.gamma_y{1}(iy{1}));
|
||||||
f = e'*(var_weights{1}.*e);
|
f = e'*(var_weights{1}.*e);
|
||||||
end
|
end
|
||||||
|
|
||||||
sy = sqrt(diag(oo_.gamma_y{1}));
|
sy = sqrt(diag(oo_.gamma_y{1}));
|
||||||
sy = sy *sy';
|
sy = sy *sy';
|
||||||
if ~isempty(targets{2})
|
if ~isempty(targets{2})
|
||||||
e = targets{2}-oo_.gamma_y{1}(iy{2})./(sy(iy{2})+1e-10);
|
e = targets{2}-oo_.gamma_y{1}(iy{2})./(sy(iy{2})+1e-10);
|
||||||
f = f+e'*(var_weights{2}.*e);
|
f = f+e'*(var_weights{2}.*e);
|
||||||
end
|
end
|
||||||
|
|
||||||
if ~isempty(targets{3})
|
if ~isempty(targets{3})
|
||||||
e = targets{3}-sqrt(M_.Sigma_e(iy{3}));
|
e = targets{3}-sqrt(M_.Sigma_e(iy{3}));
|
||||||
f = f+e'*(var_weights{3}.*e);
|
f = f+e'*(var_weights{3}.*e);
|
||||||
end
|
end
|
||||||
|
|
||||||
% autocorrelations
|
% autocorrelations
|
||||||
if nar > 0
|
if nar > 0
|
||||||
vxy = (A*vx*ghx'+ghu1*M_.Sigma_e*ghu');
|
vxy = (A*vx*ghx'+ghu1*M_.Sigma_e*ghu');
|
||||||
|
|
||||||
oo_.gamma_y{2} = ghx*vxy./(sy+1e-10);
|
oo_.gamma_y{2} = ghx*vxy./(sy+1e-10);
|
||||||
if ~isempty(targets{4})
|
if ~isempty(targets{4})
|
||||||
e = targets{4}-oo_.gamma_y{2}(iy{4});
|
e = targets{4}-oo_.gamma_y{2}(iy{4});
|
||||||
f = f+e'*(var_weights{4}.*e);
|
f = f+e'*(var_weights{4}.*e);
|
||||||
end
|
end
|
||||||
|
|
||||||
for i=2:nar
|
for i=2:nar
|
||||||
vxy = A*vxy;
|
vxy = A*vxy;
|
||||||
oo_.gamma_y{i+1} = ghx*vxy./(sy+1e-10);
|
oo_.gamma_y{i+1} = ghx*vxy./(sy+1e-10);
|
||||||
if ~isempty(targets{i+3})
|
if ~isempty(targets{i+3})
|
||||||
e = targets{i+3}-oo_.gamma_y{i+1}(iy{i+3});
|
e = targets{i+3}-oo_.gamma_y{i+1}(iy{i+3});
|
||||||
f = f+e'*(var_weights{i+3}.*e);
|
f = f+e'*(var_weights{i+3}.*e);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if isempty(fold) | f < 2*fold
|
if isempty(fold) | f < 2*fold
|
||||||
fold = f;
|
fold = f;
|
||||||
vxold = vx;
|
vxold = vx;
|
||||||
end
|
end
|
||||||
% 11/04/02 MJ generalized for correlations, autocorrelations and
|
% 11/04/02 MJ generalized for correlations, autocorrelations and
|
||||||
% constraints on M_.Sigma_e
|
% constraints on M_.Sigma_e
|
||||||
% 01/25/03 MJ targets std. dev. instead of variances
|
% 01/25/03 MJ targets std. dev. instead of variances
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -19,46 +19,46 @@ function objective=calib_obj2(M_.Sigma_e,A,ghu1,ghx,ghu,targets,var_weights,iy,n
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
global vx fold options_
|
global vx fold options_
|
||||||
|
|
||||||
objective = cell (nar+3);
|
|
||||||
oo_.gamma_y = cell(nar+1,1);
|
|
||||||
M_.Sigma_e=diag(M_.Sigma_e);
|
|
||||||
nx = size(ghx,2);
|
|
||||||
b=ghu1*M_.Sigma_e*ghu1';
|
|
||||||
vx = lyapunov_symm(A,b,options_.qz_criterium,options_.lyapunov_complex_threshold);
|
|
||||||
oo_.gamma_y{1} = ghx*vx*ghx'+ ghu*M_.Sigma_e*ghu';
|
|
||||||
if ~isempty(targets{1})
|
|
||||||
objective{1} = sqrt(oo_.gamma_y{1}(iy{1}));
|
|
||||||
end
|
|
||||||
|
|
||||||
sy = sqrt(diag(oo_.gamma_y{1}));
|
objective = cell (nar+3);
|
||||||
sy = sy *sy';
|
oo_.gamma_y = cell(nar+1,1);
|
||||||
if ~isempty(targets{2})
|
M_.Sigma_e=diag(M_.Sigma_e);
|
||||||
|
nx = size(ghx,2);
|
||||||
|
b=ghu1*M_.Sigma_e*ghu1';
|
||||||
|
vx = lyapunov_symm(A,b,options_.qz_criterium,options_.lyapunov_complex_threshold);
|
||||||
|
oo_.gamma_y{1} = ghx*vx*ghx'+ ghu*M_.Sigma_e*ghu';
|
||||||
|
if ~isempty(targets{1})
|
||||||
|
objective{1} = sqrt(oo_.gamma_y{1}(iy{1}));
|
||||||
|
end
|
||||||
|
|
||||||
|
sy = sqrt(diag(oo_.gamma_y{1}));
|
||||||
|
sy = sy *sy';
|
||||||
|
if ~isempty(targets{2})
|
||||||
objective{2} = oo_.gamma_y{1}(iy{2})./(sy(iy{2})+1e-10);
|
objective{2} = oo_.gamma_y{1}(iy{2})./(sy(iy{2})+1e-10);
|
||||||
end
|
end
|
||||||
|
|
||||||
if ~isempty(targets{3})
|
if ~isempty(targets{3})
|
||||||
objective{3} = M_.Sigma_e(iy{3});
|
objective{3} = M_.Sigma_e(iy{3});
|
||||||
end
|
end
|
||||||
|
|
||||||
% autocorrelations
|
% autocorrelations
|
||||||
if nar > 0
|
if nar > 0
|
||||||
vxy = (A*vx*ghx'+ghu1*M_.Sigma_e*ghu');
|
vxy = (A*vx*ghx'+ghu1*M_.Sigma_e*ghu');
|
||||||
|
|
||||||
oo_.gamma_y{2} = ghx*vxy./(sy+1e-10);
|
oo_.gamma_y{2} = ghx*vxy./(sy+1e-10);
|
||||||
if ~isempty(targets{4})
|
if ~isempty(targets{4})
|
||||||
objective{4} = oo_.gamma_y{2}(iy{4});
|
objective{4} = oo_.gamma_y{2}(iy{4});
|
||||||
end
|
end
|
||||||
|
|
||||||
for i=2:nar
|
for i=2:nar
|
||||||
vxy = A*vxy;
|
vxy = A*vxy;
|
||||||
oo_.gamma_y{i+1} = ghx*vxy./(sy+1e-10);
|
oo_.gamma_y{i+1} = ghx*vxy./(sy+1e-10);
|
||||||
if ~isempty(targets{i+3})
|
if ~isempty(targets{i+3})
|
||||||
objecitve{i+3} = oo_.gamma_y{i+1}(iy{i+3});
|
objecitve{i+3} = oo_.gamma_y{i+1}(iy{i+3});
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
||||||
% 11/04/02 MJ generalized for correlations, autocorrelations and
|
% 11/04/02 MJ generalized for correlations, autocorrelations and
|
||||||
% constraints on M_.Sigma_e
|
% constraints on M_.Sigma_e
|
||||||
110
matlab/check.m
110
matlab/check.m
|
|
@ -30,63 +30,63 @@ function result = check
|
||||||
|
|
||||||
global M_ options_ oo_
|
global M_ options_ oo_
|
||||||
|
|
||||||
if options_.block || options_.bytecode
|
if options_.block || options_.bytecode
|
||||||
error('CHECK: incompatibility with "block" or "bytecode" option')
|
error('CHECK: incompatibility with "block" or "bytecode" option')
|
||||||
|
end
|
||||||
|
|
||||||
|
temp_options = options_;
|
||||||
|
tempex = oo_.exo_simul;
|
||||||
|
if ~options_.initval_file & M_.exo_nbr > 1
|
||||||
|
oo_.exo_simul = ones(M_.maximum_lead+M_.maximum_lag+1,1)*oo_.exo_steady_state';
|
||||||
|
end
|
||||||
|
|
||||||
|
options_.order = 1;
|
||||||
|
|
||||||
|
[dr, info] = resol(oo_.steady_state,1);
|
||||||
|
|
||||||
|
oo_.dr = dr;
|
||||||
|
|
||||||
|
if info(1) ~= 0 & info(1) ~= 3 & info(1) ~= 4
|
||||||
|
print_info(info, options_.noprint);
|
||||||
|
end
|
||||||
|
|
||||||
|
oo_.exo_simul = tempex;
|
||||||
|
|
||||||
|
eigenvalues_ = dr.eigval;
|
||||||
|
nyf = nnz(dr.kstate(:,2)>M_.maximum_endo_lag+1);
|
||||||
|
[m_lambda,i]=sort(abs(eigenvalues_));
|
||||||
|
n_explod = nnz(abs(eigenvalues_) > options_.qz_criterium);
|
||||||
|
|
||||||
|
if options_.noprint == 0
|
||||||
|
disp(' ')
|
||||||
|
disp('EIGENVALUES:')
|
||||||
|
disp(sprintf('%16s %16s %16s\n','Modulus','Real','Imaginary'))
|
||||||
|
z=[m_lambda real(eigenvalues_(i)) imag(eigenvalues_(i))]';
|
||||||
|
disp(sprintf('%16.4g %16.4g %16.4g\n',z))
|
||||||
|
options_ = set_default_option(options_,'qz_criterium',1.000001);
|
||||||
|
disp(sprintf('\nThere are %d eigenvalue(s) larger than 1 in modulus ', ...
|
||||||
|
n_explod));
|
||||||
|
disp(sprintf('for %d forward-looking variable(s)',nyf));
|
||||||
|
disp(' ')
|
||||||
|
if dr.rank == nyf & nyf == n_explod
|
||||||
|
disp('The rank condition is verified.')
|
||||||
|
else
|
||||||
|
disp('The rank conditions ISN''T verified!')
|
||||||
end
|
end
|
||||||
|
disp(' ')
|
||||||
|
end
|
||||||
|
|
||||||
|
options_ = temp_options;
|
||||||
|
|
||||||
|
% 2/9/99 MJ: line 15, added test for absence of exogenous variable.
|
||||||
|
% 8/27/2000 MJ: change JACOB call. Added ...,1 to cumsum()
|
||||||
|
% 6/24/01 MJ: added count of abs(eigenvalues) > 1
|
||||||
|
% 2/21/02 MJ: count eigenvalues > 1[+1e-5]
|
||||||
|
% 01/22/03 MJ: warning(warning_state) needs parentheses for Matlab 6.5
|
||||||
|
% name conflicts with parameters
|
||||||
|
% 05/21/03 MJ: replace computation by dr1.m and add rank check
|
||||||
|
% 06/05/03 MJ: corrected bug when M_.maximum_lag > 0
|
||||||
|
|
||||||
temp_options = options_;
|
|
||||||
tempex = oo_.exo_simul;
|
|
||||||
if ~options_.initval_file & M_.exo_nbr > 1
|
|
||||||
oo_.exo_simul = ones(M_.maximum_lead+M_.maximum_lag+1,1)*oo_.exo_steady_state';
|
|
||||||
end
|
|
||||||
|
|
||||||
options_.order = 1;
|
|
||||||
|
|
||||||
[dr, info] = resol(oo_.steady_state,1);
|
|
||||||
|
|
||||||
oo_.dr = dr;
|
|
||||||
|
|
||||||
if info(1) ~= 0 & info(1) ~= 3 & info(1) ~= 4
|
|
||||||
print_info(info, options_.noprint);
|
|
||||||
end
|
|
||||||
|
|
||||||
oo_.exo_simul = tempex;
|
|
||||||
|
|
||||||
eigenvalues_ = dr.eigval;
|
|
||||||
nyf = nnz(dr.kstate(:,2)>M_.maximum_endo_lag+1);
|
|
||||||
[m_lambda,i]=sort(abs(eigenvalues_));
|
|
||||||
n_explod = nnz(abs(eigenvalues_) > options_.qz_criterium);
|
|
||||||
|
|
||||||
if options_.noprint == 0
|
|
||||||
disp(' ')
|
|
||||||
disp('EIGENVALUES:')
|
|
||||||
disp(sprintf('%16s %16s %16s\n','Modulus','Real','Imaginary'))
|
|
||||||
z=[m_lambda real(eigenvalues_(i)) imag(eigenvalues_(i))]';
|
|
||||||
disp(sprintf('%16.4g %16.4g %16.4g\n',z))
|
|
||||||
options_ = set_default_option(options_,'qz_criterium',1.000001);
|
|
||||||
disp(sprintf('\nThere are %d eigenvalue(s) larger than 1 in modulus ', ...
|
|
||||||
n_explod));
|
|
||||||
disp(sprintf('for %d forward-looking variable(s)',nyf));
|
|
||||||
disp(' ')
|
|
||||||
if dr.rank == nyf & nyf == n_explod
|
|
||||||
disp('The rank condition is verified.')
|
|
||||||
else
|
|
||||||
disp('The rank conditions ISN''T verified!')
|
|
||||||
end
|
|
||||||
disp(' ')
|
|
||||||
end
|
|
||||||
|
|
||||||
options_ = temp_options;
|
|
||||||
|
|
||||||
% 2/9/99 MJ: line 15, added test for absence of exogenous variable.
|
|
||||||
% 8/27/2000 MJ: change JACOB call. Added ...,1 to cumsum()
|
|
||||||
% 6/24/01 MJ: added count of abs(eigenvalues) > 1
|
|
||||||
% 2/21/02 MJ: count eigenvalues > 1[+1e-5]
|
|
||||||
% 01/22/03 MJ: warning(warning_state) needs parentheses for Matlab 6.5
|
|
||||||
% name conflicts with parameters
|
|
||||||
% 05/21/03 MJ: replace computation by dr1.m and add rank check
|
|
||||||
% 06/05/03 MJ: corrected bug when M_.maximum_lag > 0
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -31,127 +31,127 @@ function varlist = check_list_of_variables(options_, M_, varlist)
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
msg = 0;
|
msg = 0;
|
||||||
if options_.bvar_dsge && options_.bayesian_irf
|
if options_.bvar_dsge && options_.bayesian_irf
|
||||||
if ~isempty(varlist)
|
if ~isempty(varlist)
|
||||||
for i=1:size(varlist,1)
|
for i=1:size(varlist,1)
|
||||||
idx = strmatch(deblank(varlist(i,:)),options_.varobs,'exact');
|
idx = strmatch(deblank(varlist(i,:)),options_.varobs,'exact');
|
||||||
if isempty(idx)
|
if isempty(idx)
|
||||||
disp([varlist(i,:) ' is not an observed variable!']);
|
disp([varlist(i,:) ' is not an observed variable!']);
|
||||||
msg = 1;
|
msg = 1;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if size(varlist,1)~=size(options_.varobs)
|
if size(varlist,1)~=size(options_.varobs)
|
||||||
msg = 1;
|
msg = 1;
|
||||||
end
|
end
|
||||||
if msg
|
if msg
|
||||||
disp(' ')
|
disp(' ')
|
||||||
disp('Posterior IRFs will be computed for all observed variables.')
|
disp('Posterior IRFs will be computed for all observed variables.')
|
||||||
disp(' ')
|
disp(' ')
|
||||||
end
|
end
|
||||||
end
|
|
||||||
varlist = options_.varobs;
|
|
||||||
return
|
|
||||||
end
|
end
|
||||||
|
varlist = options_.varobs;
|
||||||
if isempty(varlist)
|
return
|
||||||
disp(' ')
|
end
|
||||||
disp(['You did not declare endogenous variables after the estimation command.'])
|
|
||||||
cas = [];
|
if isempty(varlist)
|
||||||
if options_.bayesian_irf
|
disp(' ')
|
||||||
cas = 'Posterior IRFs';
|
disp(['You did not declare endogenous variables after the estimation command.'])
|
||||||
end
|
cas = [];
|
||||||
if options_.moments_varendo
|
if options_.bayesian_irf
|
||||||
if isempty(cas)
|
cas = 'Posterior IRFs';
|
||||||
cas = 'Posterior moments';
|
|
||||||
else
|
|
||||||
cas = [ cas , ', posterior moments'];
|
|
||||||
end
|
|
||||||
end
|
|
||||||
if options_.smoother
|
|
||||||
if isempty(cas)
|
|
||||||
cas = 'Posterior smoothed variables';
|
|
||||||
else
|
|
||||||
cas = [ cas , ', posterior smoothed variables'];
|
|
||||||
end
|
|
||||||
end
|
|
||||||
if options_.smoother
|
|
||||||
if isempty(cas)
|
|
||||||
cas = 'Posterior smoothed variables';
|
|
||||||
else
|
|
||||||
cas = [ cas , ', posterior smoothed variables'];
|
|
||||||
end
|
|
||||||
end
|
|
||||||
if ~isempty(options_.filter_step_ahead)
|
|
||||||
if isempty(cas)
|
|
||||||
cas = 'Posterior k-step ahead filtered variables';
|
|
||||||
else
|
|
||||||
cas = [ cas , ', posterior k-step ahead filtered variables'];
|
|
||||||
end
|
|
||||||
end
|
|
||||||
if options_.forecast
|
|
||||||
if isempty(cas)
|
|
||||||
cas = 'Posterior forecasts';
|
|
||||||
else
|
|
||||||
cas = [ cas , ' and posterior forecats'];
|
|
||||||
end
|
|
||||||
end
|
|
||||||
if ~isempty(cas)
|
|
||||||
string = [ cas , ' will be computed for the ' num2str(M_.endo_nbr) ' endogenous variables'];
|
|
||||||
string = [ string ' of your model, this can be very long....'];
|
|
||||||
format_text(string, 10)
|
|
||||||
choice = [];
|
|
||||||
while isempty(choice)
|
|
||||||
disp(' ')
|
|
||||||
disp(' ')
|
|
||||||
disp('Choose one of the following options:')
|
|
||||||
disp(' ')
|
|
||||||
disp(' [1] Consider all the endogenous variables.')
|
|
||||||
disp(' [2] Consider all the observed endogenous variables.')
|
|
||||||
disp(' [3] Stop Dynare and change the mod file.')
|
|
||||||
disp(' ')
|
|
||||||
choice = input('options [default is 1] = ');
|
|
||||||
if isempty(choice)
|
|
||||||
choice=1;
|
|
||||||
end
|
|
||||||
if choice==1
|
|
||||||
varlist = M_.endo_names(1:M_.orig_endo_nbr, :);
|
|
||||||
elseif choice==2
|
|
||||||
varlist = options_.varobs;
|
|
||||||
elseif choice==3
|
|
||||||
varlist = NaN;
|
|
||||||
else
|
|
||||||
disp('')
|
|
||||||
disp('YOU HAVE TO ANSWER 1, 2 or 3!')
|
|
||||||
disp('')
|
|
||||||
end
|
|
||||||
end
|
|
||||||
end
|
|
||||||
if isnan(varlist)
|
|
||||||
edit([M_.fname '.mod'])
|
|
||||||
end
|
|
||||||
disp('')
|
|
||||||
end
|
end
|
||||||
|
if options_.moments_varendo
|
||||||
|
if isempty(cas)
|
||||||
|
cas = 'Posterior moments';
|
||||||
function format_text(remain, max_number_of_words_per_line)
|
|
||||||
index = 0;
|
|
||||||
line_of_text = [];
|
|
||||||
while ~isempty(remain)
|
|
||||||
[token, remain] = strtok(remain);
|
|
||||||
index = index+1;
|
|
||||||
if isempty(line_of_text)
|
|
||||||
line_of_text = token;
|
|
||||||
else
|
else
|
||||||
line_of_text = [line_of_text , ' ' , token];
|
cas = [ cas , ', posterior moments'];
|
||||||
end
|
|
||||||
if index==max_number_of_words_per_line
|
|
||||||
disp(line_of_text)
|
|
||||||
index = 0;
|
|
||||||
line_of_text = [];
|
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if index<max_number_of_words_per_line
|
if options_.smoother
|
||||||
|
if isempty(cas)
|
||||||
|
cas = 'Posterior smoothed variables';
|
||||||
|
else
|
||||||
|
cas = [ cas , ', posterior smoothed variables'];
|
||||||
|
end
|
||||||
|
end
|
||||||
|
if options_.smoother
|
||||||
|
if isempty(cas)
|
||||||
|
cas = 'Posterior smoothed variables';
|
||||||
|
else
|
||||||
|
cas = [ cas , ', posterior smoothed variables'];
|
||||||
|
end
|
||||||
|
end
|
||||||
|
if ~isempty(options_.filter_step_ahead)
|
||||||
|
if isempty(cas)
|
||||||
|
cas = 'Posterior k-step ahead filtered variables';
|
||||||
|
else
|
||||||
|
cas = [ cas , ', posterior k-step ahead filtered variables'];
|
||||||
|
end
|
||||||
|
end
|
||||||
|
if options_.forecast
|
||||||
|
if isempty(cas)
|
||||||
|
cas = 'Posterior forecasts';
|
||||||
|
else
|
||||||
|
cas = [ cas , ' and posterior forecats'];
|
||||||
|
end
|
||||||
|
end
|
||||||
|
if ~isempty(cas)
|
||||||
|
string = [ cas , ' will be computed for the ' num2str(M_.endo_nbr) ' endogenous variables'];
|
||||||
|
string = [ string ' of your model, this can be very long....'];
|
||||||
|
format_text(string, 10)
|
||||||
|
choice = [];
|
||||||
|
while isempty(choice)
|
||||||
|
disp(' ')
|
||||||
|
disp(' ')
|
||||||
|
disp('Choose one of the following options:')
|
||||||
|
disp(' ')
|
||||||
|
disp(' [1] Consider all the endogenous variables.')
|
||||||
|
disp(' [2] Consider all the observed endogenous variables.')
|
||||||
|
disp(' [3] Stop Dynare and change the mod file.')
|
||||||
|
disp(' ')
|
||||||
|
choice = input('options [default is 1] = ');
|
||||||
|
if isempty(choice)
|
||||||
|
choice=1;
|
||||||
|
end
|
||||||
|
if choice==1
|
||||||
|
varlist = M_.endo_names(1:M_.orig_endo_nbr, :);
|
||||||
|
elseif choice==2
|
||||||
|
varlist = options_.varobs;
|
||||||
|
elseif choice==3
|
||||||
|
varlist = NaN;
|
||||||
|
else
|
||||||
|
disp('')
|
||||||
|
disp('YOU HAVE TO ANSWER 1, 2 or 3!')
|
||||||
|
disp('')
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
if isnan(varlist)
|
||||||
|
edit([M_.fname '.mod'])
|
||||||
|
end
|
||||||
|
disp('')
|
||||||
|
end
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
function format_text(remain, max_number_of_words_per_line)
|
||||||
|
index = 0;
|
||||||
|
line_of_text = [];
|
||||||
|
while ~isempty(remain)
|
||||||
|
[token, remain] = strtok(remain);
|
||||||
|
index = index+1;
|
||||||
|
if isempty(line_of_text)
|
||||||
|
line_of_text = token;
|
||||||
|
else
|
||||||
|
line_of_text = [line_of_text , ' ' , token];
|
||||||
|
end
|
||||||
|
if index==max_number_of_words_per_line
|
||||||
disp(line_of_text)
|
disp(line_of_text)
|
||||||
end
|
index = 0;
|
||||||
|
line_of_text = [];
|
||||||
|
end
|
||||||
|
end
|
||||||
|
if index<max_number_of_words_per_line
|
||||||
|
disp(line_of_text)
|
||||||
|
end
|
||||||
|
|
@ -1,36 +1,36 @@
|
||||||
function check_model()
|
function check_model()
|
||||||
|
|
||||||
% Copyright (C) 2005-2006 Dynare Team
|
% Copyright (C) 2005-2006 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
% Dynare is free software: you can redistribute it and/or modify
|
||||||
% it under the terms of the GNU General Public License as published by
|
% it under the terms of the GNU General Public License as published by
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
% the Free Software Foundation, either version 3 of the License, or
|
||||||
% (at your option) any later version.
|
% (at your option) any later version.
|
||||||
%
|
%
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
% Dynare is distributed in the hope that it will be useful,
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
global M_
|
global M_
|
||||||
|
|
||||||
xlen = M_.maximum_exo_lag+M_.maximum_exo_lead + 1;
|
xlen = M_.maximum_exo_lag+M_.maximum_exo_lead + 1;
|
||||||
if ~ M_.lead_lag_incidence(M_.maximum_lag+1,:) > 0
|
if ~ M_.lead_lag_incidence(M_.maximum_lag+1,:) > 0
|
||||||
error ('RESOL: Error in model specification: some variables don"t appear as current') ;
|
error ('RESOL: Error in model specification: some variables don"t appear as current') ;
|
||||||
end
|
end
|
||||||
|
|
||||||
if xlen > 1
|
if xlen > 1
|
||||||
error (['RESOL: stochastic exogenous variables must appear only at the' ...
|
error (['RESOL: stochastic exogenous variables must appear only at the' ...
|
||||||
' current period. Use additional endogenous variables']) ;
|
' current period. Use additional endogenous variables']) ;
|
||||||
end
|
end
|
||||||
|
|
||||||
if (M_.exo_det_nbr > 0) & (M_.maximum_lag > 1 | M_.maximum_lead > 1)
|
if (M_.exo_det_nbr > 0) & (M_.maximum_lag > 1 | M_.maximum_lead > 1)
|
||||||
error(['Exogenous deterministic variables are currently only allowed in' ...
|
error(['Exogenous deterministic variables are currently only allowed in' ...
|
||||||
' models with leads and lags on only one period'])
|
' models with leads and lags on only one period'])
|
||||||
end
|
end
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -17,4 +17,4 @@ function n = check_name(vartan,varname)
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
n = strmatch(varname,vartan,'exact');
|
n = strmatch(varname,vartan,'exact');
|
||||||
|
|
@ -16,85 +16,85 @@ function [info,description] = check_posterior_analysis_data(type,M_)
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
info = 0;
|
info = 0;
|
||||||
|
if nargout>1
|
||||||
|
description = '';
|
||||||
|
end
|
||||||
|
|
||||||
|
%% Get informations about mcmc files.
|
||||||
|
if ~exist([ M_.dname '/metropolis'],'dir')
|
||||||
|
disp('check_posterior_analysis_data:: Can''t find any mcmc file!')
|
||||||
|
return
|
||||||
|
end
|
||||||
|
mhname = get_name_of_the_last_mh_file(M_);
|
||||||
|
mhdate = get_date_of_a_file(mhname);
|
||||||
|
|
||||||
|
%% Get informations about _posterior_draws files.
|
||||||
|
drawsinfo = dir([ M_.dname '/metropolis/' M_.fname '_posterior_draws*.mat']);
|
||||||
|
if isempty(drawsinfo)
|
||||||
|
info = 1; % select_posterior_draws has to be called first.
|
||||||
if nargout>1
|
if nargout>1
|
||||||
description = '';
|
description = 'select_posterior_draws has to be called.';
|
||||||
end
|
end
|
||||||
|
return
|
||||||
%% Get informations about mcmc files.
|
else
|
||||||
if ~exist([ M_.dname '/metropolis'],'dir')
|
number_of_last_posterior_draws_file = length(drawsinfo);
|
||||||
disp('check_posterior_analysis_data:: Can''t find any mcmc file!')
|
pddate = get_date_of_a_file([ M_.dname '/metropolis/' M_.fname '_posterior_draws'...
|
||||||
return
|
int2str(number_of_last_posterior_draws_file) '.mat']);
|
||||||
end
|
if pddate<mhdate
|
||||||
mhname = get_name_of_the_last_mh_file(M_);
|
info = 2; % _posterior_draws files have to be updated.
|
||||||
mhdate = get_date_of_a_file(mhname);
|
|
||||||
|
|
||||||
%% Get informations about _posterior_draws files.
|
|
||||||
drawsinfo = dir([ M_.dname '/metropolis/' M_.fname '_posterior_draws*.mat']);
|
|
||||||
if isempty(drawsinfo)
|
|
||||||
info = 1; % select_posterior_draws has to be called first.
|
|
||||||
if nargout>1
|
if nargout>1
|
||||||
description = 'select_posterior_draws has to be called.';
|
description = 'posterior draws files have to be updated.';
|
||||||
end
|
end
|
||||||
return
|
return
|
||||||
else
|
else
|
||||||
number_of_last_posterior_draws_file = length(drawsinfo);
|
info = 3; % Ok!
|
||||||
pddate = get_date_of_a_file([ M_.dname '/metropolis/' M_.fname '_posterior_draws'...
|
|
||||||
int2str(number_of_last_posterior_draws_file) '.mat']);
|
|
||||||
if pddate<mhdate
|
|
||||||
info = 2; % _posterior_draws files have to be updated.
|
|
||||||
if nargout>1
|
|
||||||
description = 'posterior draws files have to be updated.';
|
|
||||||
end
|
|
||||||
return
|
|
||||||
else
|
|
||||||
info = 3; % Ok!
|
|
||||||
if nargout>1
|
|
||||||
description = 'posterior draws files are up to date.';
|
|
||||||
end
|
|
||||||
end
|
|
||||||
end
|
|
||||||
|
|
||||||
%% Get informations about posterior data files.
|
|
||||||
switch type
|
|
||||||
case 'variance'
|
|
||||||
generic_post_data_file_name = 'Posterior2ndOrderMoments';
|
|
||||||
case 'decomposition'
|
|
||||||
generic_post_data_file_name = 'PosteriorVarianceDecomposition';
|
|
||||||
case 'correlation'
|
|
||||||
generic_post_data_file_name = 'PosteriorCorrelations';
|
|
||||||
case 'conditional decomposition'
|
|
||||||
generic_post_data_file_name = 'PosteriorConditionalVarianceDecomposition';
|
|
||||||
otherwise
|
|
||||||
disp('This feature is not yest implemented!')
|
|
||||||
end
|
|
||||||
pdfinfo = dir([ M_.dname '/metropolis/' M_.fname '_' generic_post_data_file_name '*']);
|
|
||||||
if isempty(pdfinfo)
|
|
||||||
info = 4; % posterior draws have to be processed.
|
|
||||||
if nargout>1
|
if nargout>1
|
||||||
description = 'posterior draws files have to be processed.';
|
description = 'posterior draws files are up to date.';
|
||||||
end
|
end
|
||||||
return
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
%% Get informations about posterior data files.
|
||||||
|
switch type
|
||||||
|
case 'variance'
|
||||||
|
generic_post_data_file_name = 'Posterior2ndOrderMoments';
|
||||||
|
case 'decomposition'
|
||||||
|
generic_post_data_file_name = 'PosteriorVarianceDecomposition';
|
||||||
|
case 'correlation'
|
||||||
|
generic_post_data_file_name = 'PosteriorCorrelations';
|
||||||
|
case 'conditional decomposition'
|
||||||
|
generic_post_data_file_name = 'PosteriorConditionalVarianceDecomposition';
|
||||||
|
otherwise
|
||||||
|
disp('This feature is not yest implemented!')
|
||||||
|
end
|
||||||
|
pdfinfo = dir([ M_.dname '/metropolis/' M_.fname '_' generic_post_data_file_name '*']);
|
||||||
|
if isempty(pdfinfo)
|
||||||
|
info = 4; % posterior draws have to be processed.
|
||||||
|
if nargout>1
|
||||||
|
description = 'posterior draws files have to be processed.';
|
||||||
|
end
|
||||||
|
return
|
||||||
|
else
|
||||||
|
number_of_the_last_post_data_file = length(pdfinfo);
|
||||||
|
name_of_the_last_post_data_file = ...
|
||||||
|
[ './' M_.dname ...
|
||||||
|
'/metropolis/' ...
|
||||||
|
M_.fname '_' ...
|
||||||
|
generic_post_data_file_name ...
|
||||||
|
int2str(number_of_the_last_post_data_file) ...
|
||||||
|
'.mat' ];
|
||||||
|
pdfdate = get_date_of_a_file(name_of_the_last_post_data_file);
|
||||||
|
if pdfdate<pddate
|
||||||
|
info = 5; % posterior data files have to be updated.
|
||||||
|
if nargout>1
|
||||||
|
description = 'posterior data files have to be updated.';
|
||||||
|
end
|
||||||
else
|
else
|
||||||
number_of_the_last_post_data_file = length(pdfinfo);
|
info = 6; % Ok (nothing to do ;-)
|
||||||
name_of_the_last_post_data_file = ...
|
if nargout>1
|
||||||
[ './' M_.dname ...
|
description = 'There is nothing to do';
|
||||||
'/metropolis/' ...
|
end
|
||||||
M_.fname '_' ...
|
end
|
||||||
generic_post_data_file_name ...
|
end
|
||||||
int2str(number_of_the_last_post_data_file) ...
|
|
||||||
'.mat' ];
|
|
||||||
pdfdate = get_date_of_a_file(name_of_the_last_post_data_file);
|
|
||||||
if pdfdate<pddate
|
|
||||||
info = 5; % posterior data files have to be updated.
|
|
||||||
if nargout>1
|
|
||||||
description = 'posterior data files have to be updated.';
|
|
||||||
end
|
|
||||||
else
|
|
||||||
info = 6; % Ok (nothing to do ;-)
|
|
||||||
if nargout>1
|
|
||||||
description = 'There is nothing to do';
|
|
||||||
end
|
|
||||||
end
|
|
||||||
end
|
|
||||||
|
|
@ -15,84 +15,84 @@ function [info,description] = check_prior_analysis_data(type,M_)
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
info = 0;
|
|
||||||
if nargout>1
|
|
||||||
description = '';
|
|
||||||
end
|
|
||||||
|
|
||||||
%% Get informations about prior draws files.
|
|
||||||
if ~exist([ M_.dname '/prior/draws'],'dir')
|
|
||||||
disp('check_prior_analysis_data:: Can''t find any prior draws file!')
|
|
||||||
return
|
|
||||||
end
|
|
||||||
|
|
||||||
prior_draws_info = dir([ M_.dname '/prior/draws/prior_draws*.mat']);
|
info = 0;
|
||||||
name_of_the_last_prior_draw_file = prior_draws_info(end).name;
|
if nargout>1
|
||||||
date_of_the_last_prior_draw_file = prior_draws_info(end).datenum;
|
description = '';
|
||||||
|
end
|
||||||
%% Get informations about _posterior_draws files.
|
|
||||||
if isempty(prior_draws_info)
|
%% Get informations about prior draws files.
|
||||||
info = 1;
|
if ~exist([ M_.dname '/prior/draws'],'dir')
|
||||||
|
disp('check_prior_analysis_data:: Can''t find any prior draws file!')
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
prior_draws_info = dir([ M_.dname '/prior/draws/prior_draws*.mat']);
|
||||||
|
name_of_the_last_prior_draw_file = prior_draws_info(end).name;
|
||||||
|
date_of_the_last_prior_draw_file = prior_draws_info(end).datenum;
|
||||||
|
|
||||||
|
%% Get informations about _posterior_draws files.
|
||||||
|
if isempty(prior_draws_info)
|
||||||
|
info = 1;
|
||||||
|
if nargout>1
|
||||||
|
description = 'prior_sampler has to be called.';
|
||||||
|
end
|
||||||
|
return
|
||||||
|
else
|
||||||
|
number_of_last_prior_draws_file = length(prior_draws_info);
|
||||||
|
date_of_the_prior_definition = get_date_of_a_file([ M_.dname '/prior/definition.mat']);
|
||||||
|
if date_of_the_prior_definition>date_of_the_last_prior_draw_file
|
||||||
|
info = 2;
|
||||||
if nargout>1
|
if nargout>1
|
||||||
description = 'prior_sampler has to be called.';
|
description = 'prior draws files have to be updated.';
|
||||||
end
|
end
|
||||||
return
|
return
|
||||||
else
|
else
|
||||||
number_of_last_prior_draws_file = length(prior_draws_info);
|
info = 3; % Nothing to do!
|
||||||
date_of_the_prior_definition = get_date_of_a_file([ M_.dname '/prior/definition.mat']);
|
|
||||||
if date_of_the_prior_definition>date_of_the_last_prior_draw_file
|
|
||||||
info = 2;
|
|
||||||
if nargout>1
|
|
||||||
description = 'prior draws files have to be updated.';
|
|
||||||
end
|
|
||||||
return
|
|
||||||
else
|
|
||||||
info = 3; % Nothing to do!
|
|
||||||
if nargout>1
|
|
||||||
description = 'prior draws files are up to date.';
|
|
||||||
end
|
|
||||||
end
|
|
||||||
end
|
|
||||||
|
|
||||||
%% Get informations about prior data files.
|
|
||||||
switch type
|
|
||||||
case 'variance'
|
|
||||||
generic_prior_data_file_name = 'Prior2ndOrderMoments';
|
|
||||||
case 'decomposition'
|
|
||||||
generic_prior_data_file_name = 'PriorVarianceDecomposition';
|
|
||||||
case 'correlation'
|
|
||||||
generic_prior_data_file_name = 'PriorCorrelations';
|
|
||||||
case 'conditional decomposition'
|
|
||||||
generic_prior_data_file_name = 'PriorConditionalVarianceDecomposition';
|
|
||||||
otherwise
|
|
||||||
disp(['This feature is not yet implemented!'])
|
|
||||||
end
|
|
||||||
CheckPath('prior/moments');
|
|
||||||
pdfinfo = dir([ M_.dname '/prior/' generic_prior_data_file_name '*']);
|
|
||||||
if isempty(pdfinfo)
|
|
||||||
info = 4;
|
|
||||||
if nargout>1
|
if nargout>1
|
||||||
description = 'prior draws files have to be processed.';
|
description = 'prior draws files are up to date.';
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
%% Get informations about prior data files.
|
||||||
|
switch type
|
||||||
|
case 'variance'
|
||||||
|
generic_prior_data_file_name = 'Prior2ndOrderMoments';
|
||||||
|
case 'decomposition'
|
||||||
|
generic_prior_data_file_name = 'PriorVarianceDecomposition';
|
||||||
|
case 'correlation'
|
||||||
|
generic_prior_data_file_name = 'PriorCorrelations';
|
||||||
|
case 'conditional decomposition'
|
||||||
|
generic_prior_data_file_name = 'PriorConditionalVarianceDecomposition';
|
||||||
|
otherwise
|
||||||
|
disp(['This feature is not yet implemented!'])
|
||||||
|
end
|
||||||
|
CheckPath('prior/moments');
|
||||||
|
pdfinfo = dir([ M_.dname '/prior/' generic_prior_data_file_name '*']);
|
||||||
|
if isempty(pdfinfo)
|
||||||
|
info = 4;
|
||||||
|
if nargout>1
|
||||||
|
description = 'prior draws files have to be processed.';
|
||||||
|
end
|
||||||
|
return
|
||||||
|
else
|
||||||
|
number_of_the_last_prior_data_file = length(pdfinfo);
|
||||||
|
name_of_the_last_prior_data_file = pdinfo(end).name;
|
||||||
|
pdfdate = pdinfo(end).datenum;
|
||||||
|
% /!\ REMARK /!\
|
||||||
|
% The user can change the model or the value of a calibrated
|
||||||
|
% parameter without changing the prior. In this case the (prior)
|
||||||
|
% moments should be computed. But this case cannot be detected!!!
|
||||||
|
if pdfdate<date_of_the_last_prior_draw_file
|
||||||
|
info = 5; % prior data files have to be updated.
|
||||||
|
if nargout>1
|
||||||
|
description = 'prior data files have to be updated.';
|
||||||
end
|
end
|
||||||
return
|
|
||||||
else
|
else
|
||||||
number_of_the_last_prior_data_file = length(pdfinfo);
|
info = 6; % Ok (nothing to do ;-)
|
||||||
name_of_the_last_prior_data_file = pdinfo(end).name;
|
if nargout>1
|
||||||
pdfdate = pdinfo(end).datenum;
|
description = 'prior data files are up to date.';
|
||||||
% /!\ REMARK /!\
|
|
||||||
% The user can change the model or the value of a calibrated
|
|
||||||
% parameter without changing the prior. In this case the (prior)
|
|
||||||
% moments should be computed. But this case cannot be detected!!!
|
|
||||||
if pdfdate<date_of_the_last_prior_draw_file
|
|
||||||
info = 5; % prior data files have to be updated.
|
|
||||||
if nargout>1
|
|
||||||
description = 'prior data files have to be updated.';
|
|
||||||
end
|
|
||||||
else
|
|
||||||
info = 6; % Ok (nothing to do ;-)
|
|
||||||
if nargout>1
|
|
||||||
description = 'prior data files are up to date.';
|
|
||||||
end
|
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
end
|
||||||
|
|
@ -36,10 +36,10 @@ global M_ options_ estim_params_
|
||||||
|
|
||||||
|
|
||||||
n = estim_params_.np + ...
|
n = estim_params_.np + ...
|
||||||
estim_params_.nvn+ ...
|
estim_params_.nvn+ ...
|
||||||
estim_params_.ncx+ ...
|
estim_params_.ncx+ ...
|
||||||
estim_params_.ncn+ ...
|
estim_params_.ncn+ ...
|
||||||
estim_params_.nvx;
|
estim_params_.nvx;
|
||||||
nblck = options_.mh_nblck;
|
nblck = options_.mh_nblck;
|
||||||
|
|
||||||
MhDirectoryName = CheckPath('metropolis');
|
MhDirectoryName = CheckPath('metropolis');
|
||||||
|
|
|
||||||
|
|
@ -1,31 +1,31 @@
|
||||||
function moments=compute_model_moments(dr,M_,options_)
|
function moments=compute_model_moments(dr,M_,options_)
|
||||||
%
|
%
|
||||||
% INPUTS
|
% INPUTS
|
||||||
% dr: structure describing model solution
|
% dr: structure describing model solution
|
||||||
% M_: structure of Dynare options
|
% M_: structure of Dynare options
|
||||||
% options_
|
% options_
|
||||||
%
|
%
|
||||||
% OUTPUTS
|
% OUTPUTS
|
||||||
% moments: a cell array containing requested moments
|
% moments: a cell array containing requested moments
|
||||||
%
|
%
|
||||||
% SPECIAL REQUIREMENTS
|
% SPECIAL REQUIREMENTS
|
||||||
% none
|
% none
|
||||||
|
|
||||||
% Copyright (C) 2008 Dynare Team
|
% Copyright (C) 2008 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
% Dynare is free software: you can redistribute it and/or modify
|
||||||
% it under the terms of the GNU General Public License as published by
|
% it under the terms of the GNU General Public License as published by
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
% the Free Software Foundation, either version 3 of the License, or
|
||||||
% (at your option) any later version.
|
% (at your option) any later version.
|
||||||
%
|
%
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
% Dynare is distributed in the hope that it will be useful,
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
moments = th_autocovariances(dr,options_.varlist,M_,options_);
|
moments = th_autocovariances(dr,options_.varlist,M_,options_);
|
||||||
|
|
|
||||||
|
|
@ -32,88 +32,88 @@ function oo_ = compute_moments_varendo(type,options_,M_,oo_,var_list_)
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
if strcmpi(type,'posterior')
|
|
||||||
posterior = 1;
|
|
||||||
if nargin==4
|
|
||||||
var_list_ = options_.varobs;
|
|
||||||
end
|
|
||||||
elseif strcmpi(type,'prior')
|
|
||||||
posterior = 0;
|
|
||||||
if nargin==4
|
|
||||||
var_list_ = options_.prior_analysis_endo_var_list;
|
|
||||||
if isempty(var_list_)
|
|
||||||
options_.prior_analysis_var_list = options_.varobs;
|
|
||||||
end
|
|
||||||
end
|
|
||||||
else
|
|
||||||
disp('compute_moments_varendo:: Unknown type!')
|
|
||||||
error()
|
|
||||||
end
|
|
||||||
|
|
||||||
NumberOfEndogenousVariables = rows(var_list_);
|
if strcmpi(type,'posterior')
|
||||||
NumberOfExogenousVariables = M_.exo_nbr;
|
posterior = 1;
|
||||||
list_of_exogenous_variables = M_.exo_names;
|
if nargin==4
|
||||||
NumberOfLags = options_.ar;
|
var_list_ = options_.varobs;
|
||||||
Steps = options_.conditional_variance_decomposition;
|
end
|
||||||
|
elseif strcmpi(type,'prior')
|
||||||
|
posterior = 0;
|
||||||
|
if nargin==4
|
||||||
|
var_list_ = options_.prior_analysis_endo_var_list;
|
||||||
|
if isempty(var_list_)
|
||||||
|
options_.prior_analysis_var_list = options_.varobs;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
else
|
||||||
|
disp('compute_moments_varendo:: Unknown type!')
|
||||||
|
error()
|
||||||
|
end
|
||||||
|
|
||||||
% COVARIANCE MATRIX.
|
NumberOfEndogenousVariables = rows(var_list_);
|
||||||
if posterior
|
NumberOfExogenousVariables = M_.exo_nbr;
|
||||||
for i=1:NumberOfEndogenousVariables
|
list_of_exogenous_variables = M_.exo_names;
|
||||||
for j=i:NumberOfEndogenousVariables
|
NumberOfLags = options_.ar;
|
||||||
oo_ = posterior_analysis('variance',var_list_(i,:),var_list_(j,:),[],options_,M_,oo_);
|
Steps = options_.conditional_variance_decomposition;
|
||||||
end
|
|
||||||
|
% COVARIANCE MATRIX.
|
||||||
|
if posterior
|
||||||
|
for i=1:NumberOfEndogenousVariables
|
||||||
|
for j=i:NumberOfEndogenousVariables
|
||||||
|
oo_ = posterior_analysis('variance',var_list_(i,:),var_list_(j,:),[],options_,M_,oo_);
|
||||||
end
|
end
|
||||||
else
|
end
|
||||||
|
else
|
||||||
|
for i=1:NumberOfEndogenousVariables
|
||||||
|
for j=i:NumberOfEndogenousVariables
|
||||||
|
oo_ = prior_analysis('variance',var_list_(i,:),var_list_(j,:),[],options_,M_,oo_);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
% CORRELATION FUNCTION.
|
||||||
|
if posterior
|
||||||
|
for h=NumberOfLags:-1:1
|
||||||
for i=1:NumberOfEndogenousVariables
|
for i=1:NumberOfEndogenousVariables
|
||||||
for j=i:NumberOfEndogenousVariables
|
for j=1:NumberOfEndogenousVariables
|
||||||
oo_ = prior_analysis('variance',var_list_(i,:),var_list_(j,:),[],options_,M_,oo_);
|
oo_ = posterior_analysis('correlation',var_list_(i,:),var_list_(j,:),h,options_,M_,oo_);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
% CORRELATION FUNCTION.
|
else
|
||||||
if posterior
|
for h=NumberOfLags:-1:1
|
||||||
for h=NumberOfLags:-1:1
|
for i=1:NumberOfEndogenousVariables
|
||||||
for i=1:NumberOfEndogenousVariables
|
for j=1:NumberOfEndogenousVariables
|
||||||
for j=1:NumberOfEndogenousVariables
|
oo_ = prior_analysis('correlation',var_list_(i,:),var_list_(j,:),h,options_,M_,oo_);
|
||||||
oo_ = posterior_analysis('correlation',var_list_(i,:),var_list_(j,:),h,options_,M_,oo_);
|
|
||||||
end
|
|
||||||
end
|
|
||||||
end
|
|
||||||
else
|
|
||||||
for h=NumberOfLags:-1:1
|
|
||||||
for i=1:NumberOfEndogenousVariables
|
|
||||||
for j=1:NumberOfEndogenousVariables
|
|
||||||
oo_ = prior_analysis('correlation',var_list_(i,:),var_list_(j,:),h,options_,M_,oo_);
|
|
||||||
end
|
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
% VARIANCE DECOMPOSITION.
|
end
|
||||||
if posterior
|
% VARIANCE DECOMPOSITION.
|
||||||
for i=1:NumberOfEndogenousVariables
|
if posterior
|
||||||
for j=1:NumberOfExogenousVariables
|
for i=1:NumberOfEndogenousVariables
|
||||||
oo_ = posterior_analysis('decomposition',var_list_(i,:),M_.exo_names(j,:),[],options_,M_,oo_);
|
for j=1:NumberOfExogenousVariables
|
||||||
end
|
oo_ = posterior_analysis('decomposition',var_list_(i,:),M_.exo_names(j,:),[],options_,M_,oo_);
|
||||||
end
|
end
|
||||||
else
|
|
||||||
for i=1:NumberOfEndogenousVariables
|
|
||||||
for j=1:NumberOfExogenousVariables
|
|
||||||
oo_ = prior_analysis('decomposition',var_list_(i,:),M_.exo_names(j,:),[],options_,M_,oo_);
|
|
||||||
end
|
|
||||||
end
|
|
||||||
end
|
end
|
||||||
% CONDITIONAL VARIANCE DECOMPOSITION.
|
else
|
||||||
if posterior
|
for i=1:NumberOfEndogenousVariables
|
||||||
for i=1:NumberOfEndogenousVariables
|
for j=1:NumberOfExogenousVariables
|
||||||
for j=1:NumberOfExogenousVariables
|
oo_ = prior_analysis('decomposition',var_list_(i,:),M_.exo_names(j,:),[],options_,M_,oo_);
|
||||||
oo_ = posterior_analysis('conditional decomposition',var_list_(i,:),M_.exo_names(j,:),Steps,options_,M_,oo_);
|
|
||||||
end
|
|
||||||
end
|
end
|
||||||
else
|
end
|
||||||
for i=1:NumberOfEndogenousVariables
|
end
|
||||||
for j=1:NumberOfExogenousVariables
|
% CONDITIONAL VARIANCE DECOMPOSITION.
|
||||||
oo_ = prior_analysis('conditional decomposition',var_list_(i,:),M_.exo_names(j,:),Steps,options_,M_,oo_);
|
if posterior
|
||||||
end
|
for i=1:NumberOfEndogenousVariables
|
||||||
|
for j=1:NumberOfExogenousVariables
|
||||||
|
oo_ = posterior_analysis('conditional decomposition',var_list_(i,:),M_.exo_names(j,:),Steps,options_,M_,oo_);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
else
|
||||||
|
for i=1:NumberOfEndogenousVariables
|
||||||
|
for j=1:NumberOfExogenousVariables
|
||||||
|
oo_ = prior_analysis('conditional decomposition',var_list_(i,:),M_.exo_names(j,:),Steps,options_,M_,oo_);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
@ -31,43 +31,43 @@ function PackedConditionalVarianceDecomposition = conditional_variance_decomposi
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
number_of_state_innovations = ...
|
number_of_state_innovations = ...
|
||||||
StateSpaceModel.number_of_state_innovations;
|
StateSpaceModel.number_of_state_innovations;
|
||||||
transition_matrix = StateSpaceModel.transition_matrix;
|
transition_matrix = StateSpaceModel.transition_matrix;
|
||||||
number_of_state_equations = ...
|
number_of_state_equations = ...
|
||||||
StateSpaceModel.number_of_state_equations;
|
StateSpaceModel.number_of_state_equations;
|
||||||
nSteps = length(Steps);
|
nSteps = length(Steps);
|
||||||
|
|
||||||
ConditionalVariance = zeros(number_of_state_equations,number_of_state_equations);
|
|
||||||
ConditionalVariance = repmat(ConditionalVariance,[1 1 nSteps ...
|
|
||||||
number_of_state_innovations]);
|
|
||||||
|
|
||||||
if StateSpaceModel.sigma_e_is_diagonal
|
ConditionalVariance = zeros(number_of_state_equations,number_of_state_equations);
|
||||||
B = StateSpaceModel.impulse_matrix.* ...
|
ConditionalVariance = repmat(ConditionalVariance,[1 1 nSteps ...
|
||||||
repmat(sqrt(diag(StateSpaceModel.state_innovations_covariance_matrix)'),...
|
number_of_state_innovations]);
|
||||||
number_of_state_equations,1);
|
|
||||||
else
|
if StateSpaceModel.sigma_e_is_diagonal
|
||||||
B = StateSpaceModel.impulse_matrix*chol(StateSpaceModel.state_innovations_covariance_matrix)';
|
B = StateSpaceModel.impulse_matrix.* ...
|
||||||
end
|
repmat(sqrt(diag(StateSpaceModel.state_innovations_covariance_matrix)'),...
|
||||||
|
number_of_state_equations,1);
|
||||||
for i=1:number_of_state_innovations
|
else
|
||||||
BB = B(:,i)*B(:,i)';
|
B = StateSpaceModel.impulse_matrix*chol(StateSpaceModel.state_innovations_covariance_matrix)';
|
||||||
V = zeros(number_of_state_equations,number_of_state_equations);
|
end
|
||||||
m = 1;
|
|
||||||
for h = 1:max(Steps)
|
for i=1:number_of_state_innovations
|
||||||
V = transition_matrix*V*transition_matrix'+BB;
|
BB = B(:,i)*B(:,i)';
|
||||||
if h == Steps(m)
|
V = zeros(number_of_state_equations,number_of_state_equations);
|
||||||
ConditionalVariance(:,:,m,i) = V;
|
m = 1;
|
||||||
m = m+1;
|
for h = 1:max(Steps)
|
||||||
end
|
V = transition_matrix*V*transition_matrix'+BB;
|
||||||
|
if h == Steps(m)
|
||||||
|
ConditionalVariance(:,:,m,i) = V;
|
||||||
|
m = m+1;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
end
|
||||||
ConditionalVariance = ConditionalVariance(SubsetOfVariables,SubsetOfVariables,:,:);
|
|
||||||
NumberOfVariables = length(SubsetOfVariables);
|
ConditionalVariance = ConditionalVariance(SubsetOfVariables,SubsetOfVariables,:,:);
|
||||||
PackedConditionalVarianceDecomposition = zeros(NumberOfVariables*(NumberOfVariables+1)/2,length(Steps),StateSpaceModel.number_of_state_innovations);
|
NumberOfVariables = length(SubsetOfVariables);
|
||||||
for i=1:number_of_state_innovations
|
PackedConditionalVarianceDecomposition = zeros(NumberOfVariables*(NumberOfVariables+1)/2,length(Steps),StateSpaceModel.number_of_state_innovations);
|
||||||
for h = 1:length(Steps)
|
for i=1:number_of_state_innovations
|
||||||
PackedConditionalVarianceDecomposition(:,h,i) = vech(ConditionalVariance(:,:,h,i));
|
for h = 1:length(Steps)
|
||||||
end
|
PackedConditionalVarianceDecomposition(:,h,i) = vech(ConditionalVariance(:,:,h,i));
|
||||||
end
|
end
|
||||||
|
end
|
||||||
|
|
@ -19,78 +19,78 @@ function oo_ = conditional_variance_decomposition_mc_analysis(NumberOfSimulation
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
if strcmpi(type,'posterior')
|
if strcmpi(type,'posterior')
|
||||||
TYPE = 'Posterior';
|
TYPE = 'Posterior';
|
||||||
PATH = [dname '/metropolis/'];
|
PATH = [dname '/metropolis/'];
|
||||||
else
|
else
|
||||||
TYPE = 'Prior';
|
TYPE = 'Prior';
|
||||||
PATH = [dname '/prior/moments/'];
|
PATH = [dname '/prior/moments/'];
|
||||||
end
|
end
|
||||||
|
|
||||||
indx = check_name(vartan,var);
|
indx = check_name(vartan,var);
|
||||||
if isempty(indx)
|
if isempty(indx)
|
||||||
disp([ type '_analysis:: ' var ' is not a stationary endogenous variable!'])
|
disp([ type '_analysis:: ' var ' is not a stationary endogenous variable!'])
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
endogenous_variable_index = sum(1:indx);
|
endogenous_variable_index = sum(1:indx);
|
||||||
exogenous_variable_index = check_name(exonames,exo);
|
exogenous_variable_index = check_name(exonames,exo);
|
||||||
if isempty(exogenous_variable_index)
|
if isempty(exogenous_variable_index)
|
||||||
disp([ type '_analysis:: ' exo ' is not a declared exogenous variable!'])
|
disp([ type '_analysis:: ' exo ' is not a declared exogenous variable!'])
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
|
|
||||||
name = [ var '.' exo ];
|
name = [ var '.' exo ];
|
||||||
if isfield(oo_, [ TYPE 'TheoreticalMoments' ])
|
if isfield(oo_, [ TYPE 'TheoreticalMoments' ])
|
||||||
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments;'])
|
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments;'])
|
||||||
if isfield(temporary_structure,'dsge')
|
if isfield(temporary_structure,'dsge')
|
||||||
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge;'])
|
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge;'])
|
||||||
if isfield(temporary_structure,'ConditionalVarianceDecomposition')
|
if isfield(temporary_structure,'ConditionalVarianceDecomposition')
|
||||||
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge.ConditionalVarianceDecomposition.mean;'])
|
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge.ConditionalVarianceDecomposition.mean;'])
|
||||||
if isfield(temporary_structure,name)
|
if isfield(temporary_structure,name)
|
||||||
if sum(Steps-temporary_structure.(name)(1,:)) == 0
|
if sum(Steps-temporary_structure.(name)(1,:)) == 0
|
||||||
% Nothing (new) to do here...
|
% Nothing (new) to do here...
|
||||||
return
|
return
|
||||||
end
|
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
end
|
||||||
ListOfFiles = dir([ PATH fname '_' TYPE 'ConditionalVarianceDecomposition*.mat']);
|
|
||||||
i1 = 1; tmp = zeros(NumberOfSimulations,length(Steps));
|
|
||||||
for file = 1:length(ListOfFiles)
|
|
||||||
load([ PATH ListOfFiles(file).name ]);
|
|
||||||
% 4D-array (endovar,time,exovar,simul)
|
|
||||||
i2 = i1 + size(Conditional_decomposition_array,4) - 1;
|
|
||||||
tmp(i1:i2,:) = transpose(dynare_squeeze(Conditional_decomposition_array(endogenous_variable_index,:,exogenous_variable_index,:)));
|
|
||||||
i1 = i2+1;
|
|
||||||
end
|
|
||||||
|
|
||||||
p_mean = NaN(2,length(Steps));
|
ListOfFiles = dir([ PATH fname '_' TYPE 'ConditionalVarianceDecomposition*.mat']);
|
||||||
p_mean(1,:) = Steps;
|
i1 = 1; tmp = zeros(NumberOfSimulations,length(Steps));
|
||||||
p_median = NaN(1,length(Steps));
|
for file = 1:length(ListOfFiles)
|
||||||
p_variance = NaN(1,length(Steps));
|
load([ PATH ListOfFiles(file).name ]);
|
||||||
p_deciles = NaN(9,length(Steps));
|
% 4D-array (endovar,time,exovar,simul)
|
||||||
p_density = NaN(2^9,2,length(Steps));
|
i2 = i1 + size(Conditional_decomposition_array,4) - 1;
|
||||||
p_hpdinf = NaN(1,length(Steps));
|
tmp(i1:i2,:) = transpose(dynare_squeeze(Conditional_decomposition_array(endogenous_variable_index,:,exogenous_variable_index,:)));
|
||||||
p_hpdsup = NaN(1,length(Steps));
|
i1 = i2+1;
|
||||||
for i=1:length(Steps)
|
end
|
||||||
if ~isconst(tmp(:,i))
|
|
||||||
[pp_mean, pp_median, pp_var, hpd_interval, pp_deciles, pp_density] = ...
|
p_mean = NaN(2,length(Steps));
|
||||||
posterior_moments(tmp(:,i),1,mh_conf_sig);
|
p_mean(1,:) = Steps;
|
||||||
p_mean(2,i) = pp_mean;
|
p_median = NaN(1,length(Steps));
|
||||||
p_median(i) = pp_median;
|
p_variance = NaN(1,length(Steps));
|
||||||
p_variance(i) = pp_var;
|
p_deciles = NaN(9,length(Steps));
|
||||||
p_deciles(:,i) = pp_deciles;
|
p_density = NaN(2^9,2,length(Steps));
|
||||||
p_hpdinf(i) = hpd_interval(1);
|
p_hpdinf = NaN(1,length(Steps));
|
||||||
p_hpdinf(i) = hpd_interval(2);
|
p_hpdsup = NaN(1,length(Steps));
|
||||||
p_density(:,:,i) = pp_density;
|
for i=1:length(Steps)
|
||||||
end
|
if ~isconst(tmp(:,i))
|
||||||
|
[pp_mean, pp_median, pp_var, hpd_interval, pp_deciles, pp_density] = ...
|
||||||
|
posterior_moments(tmp(:,i),1,mh_conf_sig);
|
||||||
|
p_mean(2,i) = pp_mean;
|
||||||
|
p_median(i) = pp_median;
|
||||||
|
p_variance(i) = pp_var;
|
||||||
|
p_deciles(:,i) = pp_deciles;
|
||||||
|
p_hpdinf(i) = hpd_interval(1);
|
||||||
|
p_hpdinf(i) = hpd_interval(2);
|
||||||
|
p_density(:,:,i) = pp_density;
|
||||||
end
|
end
|
||||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.ConditionalVarianceDecomposition.mean.' name ' = p_mean;']);
|
end
|
||||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.ConditionalVarianceDecomposition.median.' name ' = p_median;']);
|
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.ConditionalVarianceDecomposition.mean.' name ' = p_mean;']);
|
||||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.ConditionalVarianceDecomposition.variance.' name ' = p_variance;']);
|
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.ConditionalVarianceDecomposition.median.' name ' = p_median;']);
|
||||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.ConditionalVarianceDecomposition.hpdinf.' name ' = p_hpdinf;']);
|
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.ConditionalVarianceDecomposition.variance.' name ' = p_variance;']);
|
||||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.ConditionalVarianceDecomposition.hpdsup.' name ' = p_hpdsup;']);
|
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.ConditionalVarianceDecomposition.hpdinf.' name ' = p_hpdinf;']);
|
||||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.ConditionalVarianceDecomposition.deciles.' name ' = p_deciles;']);
|
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.ConditionalVarianceDecomposition.hpdsup.' name ' = p_hpdsup;']);
|
||||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.ConditionalVarianceDecomposition.density.' name ' = p_density;']);
|
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.ConditionalVarianceDecomposition.deciles.' name ' = p_deciles;']);
|
||||||
|
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.ConditionalVarianceDecomposition.density.' name ' = p_density;']);
|
||||||
|
|
@ -19,55 +19,52 @@ function oo_ = correlation_mc_analysis(SampleSize,type,dname,fname,vartan,nvar,v
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
if strcmpi(type,'posterior')
|
if strcmpi(type,'posterior')
|
||||||
TYPE = 'Posterior';
|
TYPE = 'Posterior';
|
||||||
PATH = [dname '/metropolis/'];
|
PATH = [dname '/metropolis/'];
|
||||||
else
|
else
|
||||||
TYPE = 'Prior';
|
TYPE = 'Prior';
|
||||||
PATH = [dname '/prior/moments/'];
|
PATH = [dname '/prior/moments/'];
|
||||||
end
|
end
|
||||||
|
|
||||||
indx1 = check_name(vartan,var1);
|
indx1 = check_name(vartan,var1);
|
||||||
if isempty(indx1)
|
if isempty(indx1)
|
||||||
disp([ type '_analysis:: ' var1 ' is not a stationary endogenous variable!'])
|
disp([ type '_analysis:: ' var1 ' is not a stationary endogenous variable!'])
|
||||||
|
return
|
||||||
|
end
|
||||||
|
if ~isempty(var2)
|
||||||
|
indx2 = check_name(vartan,var2);
|
||||||
|
if isempty(indx2)
|
||||||
|
disp([ type '_analysis:: ' var2 ' is not a stationary endogenous variable!'])
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
if ~isempty(var2)
|
else
|
||||||
indx2 = check_name(vartan,var2);
|
indx2 = indx1;
|
||||||
if isempty(indx2)
|
var2 = var1;
|
||||||
disp([ type '_analysis:: ' var2 ' is not a stationary endogenous variable!'])
|
end
|
||||||
return
|
|
||||||
end
|
|
||||||
else
|
|
||||||
indx2 = indx1;
|
|
||||||
var2 = var1;
|
|
||||||
end
|
|
||||||
|
|
||||||
if isfield(oo_,[TYPE 'TheoreticalMoments'])
|
if isfield(oo_,[TYPE 'TheoreticalMoments'])
|
||||||
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments;'])
|
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments;'])
|
||||||
if isfield(temporary_structure,'dsge')
|
if isfield(temporary_structure,'dsge')
|
||||||
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge;'])
|
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge;'])
|
||||||
if isfield(temporary_structure,'correlation')
|
if isfield(temporary_structure,'correlation')
|
||||||
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge.correlation.mean;'])
|
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge.correlation.mean;'])
|
||||||
if isfield(temporary_structure,var1)
|
if isfield(temporary_structure,var1)
|
||||||
eval(['temporary_structure_1 = oo_.' TYPE 'TheoreticalMoments.dsge.correlation.mean.' var1 ';'])
|
eval(['temporary_structure_1 = oo_.' TYPE 'TheoreticalMoments.dsge.correlation.mean.' var1 ';'])
|
||||||
if isfield(temporary_structure_1,var2)
|
if isfield(temporary_structure_1,var2)
|
||||||
eval(['temporary_structure_2 = temporary_structure_1.' var2 ';'])
|
eval(['temporary_structure_2 = temporary_structure_1.' var2 ';'])
|
||||||
l1 = length(temporary_structure_2);
|
l1 = length(temporary_structure_2);
|
||||||
if l1<nar
|
if l1<nar
|
||||||
% INITIALIZATION:
|
% INITIALIZATION:
|
||||||
oo_ = initialize_output_structure(var1,var2,nar,type,oo_);
|
oo_ = initialize_output_structure(var1,var2,nar,type,oo_);
|
||||||
delete([PATH fname '_' TYPE 'Correlations*'])
|
delete([PATH fname '_' TYPE 'Correlations*'])
|
||||||
[nvar,vartan,NumberOfFiles] = ...
|
[nvar,vartan,NumberOfFiles] = ...
|
||||||
dsge_simulated_theoretical_correlation(SampleSize,nar,M_,options_,oo_,type);
|
dsge_simulated_theoretical_correlation(SampleSize,nar,M_,options_,oo_,type);
|
||||||
else
|
|
||||||
if ~isnan(temporary_structure_2(nar))
|
|
||||||
%Nothing to do.
|
|
||||||
return
|
|
||||||
end
|
|
||||||
end
|
|
||||||
else
|
else
|
||||||
oo_ = initialize_output_structure(var1,var2,nar,TYPE,oo_);
|
if ~isnan(temporary_structure_2(nar))
|
||||||
|
%Nothing to do.
|
||||||
|
return
|
||||||
|
end
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
oo_ = initialize_output_structure(var1,var2,nar,TYPE,oo_);
|
oo_ = initialize_output_structure(var1,var2,nar,TYPE,oo_);
|
||||||
|
|
@ -81,72 +78,75 @@ function oo_ = correlation_mc_analysis(SampleSize,type,dname,fname,vartan,nvar,v
|
||||||
else
|
else
|
||||||
oo_ = initialize_output_structure(var1,var2,nar,TYPE,oo_);
|
oo_ = initialize_output_structure(var1,var2,nar,TYPE,oo_);
|
||||||
end
|
end
|
||||||
ListOfFiles = dir([ PATH fname '_' TYPE 'Correlations*.mat']);
|
else
|
||||||
i1 = 1; tmp = zeros(SampleSize,1);
|
oo_ = initialize_output_structure(var1,var2,nar,TYPE,oo_);
|
||||||
for file = 1:length(ListOfFiles)
|
end
|
||||||
load([ PATH ListOfFiles(file).name ]);
|
ListOfFiles = dir([ PATH fname '_' TYPE 'Correlations*.mat']);
|
||||||
i2 = i1 + rows(Correlation_array) - 1;
|
i1 = 1; tmp = zeros(SampleSize,1);
|
||||||
tmp(i1:i2) = Correlation_array(:,indx1,indx2,nar);
|
for file = 1:length(ListOfFiles)
|
||||||
i1 = i2+1;
|
load([ PATH ListOfFiles(file).name ]);
|
||||||
end
|
i2 = i1 + rows(Correlation_array) - 1;
|
||||||
name = [ var1 '.' var2 ];
|
tmp(i1:i2) = Correlation_array(:,indx1,indx2,nar);
|
||||||
if ~isconst(tmp)
|
i1 = i2+1;
|
||||||
[p_mean, p_median, p_var, hpd_interval, p_deciles, density] = ...
|
end
|
||||||
posterior_moments(tmp,1,mh_conf_sig);
|
name = [ var1 '.' var2 ];
|
||||||
if isfield(oo_,[ TYPE 'TheoreticalMoments'])
|
if ~isconst(tmp)
|
||||||
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments;'])
|
[p_mean, p_median, p_var, hpd_interval, p_deciles, density] = ...
|
||||||
if isfield(temporary_structure,'dsge')
|
posterior_moments(tmp,1,mh_conf_sig);
|
||||||
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge;'])
|
if isfield(oo_,[ TYPE 'TheoreticalMoments'])
|
||||||
if isfield(temporary_structure,'correlation')
|
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments;'])
|
||||||
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'mean',nar,p_mean);
|
if isfield(temporary_structure,'dsge')
|
||||||
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'median',nar,p_median);
|
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge;'])
|
||||||
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'variance',nar,p_var);
|
if isfield(temporary_structure,'correlation')
|
||||||
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'hpdinf',nar,hpd_interval(1));
|
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'mean',nar,p_mean);
|
||||||
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'hpdsup',nar,hpd_interval(2));
|
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'median',nar,p_median);
|
||||||
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'deciles',nar,p_deciles);
|
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'variance',nar,p_var);
|
||||||
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'density',nar,density);
|
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'hpdinf',nar,hpd_interval(1));
|
||||||
end
|
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'hpdsup',nar,hpd_interval(2));
|
||||||
end
|
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'deciles',nar,p_deciles);
|
||||||
end
|
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'density',nar,density);
|
||||||
else
|
|
||||||
if isfield(oo_,'PosteriorTheoreticalMoments')
|
|
||||||
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments;'])
|
|
||||||
if isfield(temporary_structure,'dsge')
|
|
||||||
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge;'])
|
|
||||||
if isfield(temporary_structure,'correlation')
|
|
||||||
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'mean',nar,NaN);
|
|
||||||
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'median',nar,NaN);
|
|
||||||
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'variance',nar,NaN);
|
|
||||||
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'hpdinf',nar,NaN);
|
|
||||||
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'hpdsup',nar,NaN);
|
|
||||||
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'deciles',nar,NaN);
|
|
||||||
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'density',nar,NaN);
|
|
||||||
end
|
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
else
|
||||||
|
if isfield(oo_,'PosteriorTheoreticalMoments')
|
||||||
|
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments;'])
|
||||||
|
if isfield(temporary_structure,'dsge')
|
||||||
|
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge;'])
|
||||||
|
if isfield(temporary_structure,'correlation')
|
||||||
|
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'mean',nar,NaN);
|
||||||
|
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'median',nar,NaN);
|
||||||
|
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'variance',nar,NaN);
|
||||||
|
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'hpdinf',nar,NaN);
|
||||||
|
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'hpdsup',nar,NaN);
|
||||||
|
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'deciles',nar,NaN);
|
||||||
|
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'density',nar,NaN);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
function oo_ = initialize_output_structure(var1,var2,nar,type,oo_)
|
function oo_ = initialize_output_structure(var1,var2,nar,type,oo_)
|
||||||
name = [ var1 '.' var2 ];
|
name = [ var1 '.' var2 ];
|
||||||
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.mean.' name ' = NaN(' int2str(nar) ',1);']);
|
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.mean.' name ' = NaN(' int2str(nar) ',1);']);
|
||||||
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.median.' name ' = NaN(' int2str(nar) ',1);']);
|
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.median.' name ' = NaN(' int2str(nar) ',1);']);
|
||||||
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.variance.' name ' = NaN(' int2str(nar) ',1);']);
|
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.variance.' name ' = NaN(' int2str(nar) ',1);']);
|
||||||
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.hpdinf.' name ' = NaN(' int2str(nar) ',1);']);
|
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.hpdinf.' name ' = NaN(' int2str(nar) ',1);']);
|
||||||
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.hpdsup.' name ' = NaN(' int2str(nar) ',1);']);
|
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.hpdsup.' name ' = NaN(' int2str(nar) ',1);']);
|
||||||
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.deciles.' name ' = cell(' int2str(nar) ',1);']);
|
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.deciles.' name ' = cell(' int2str(nar) ',1);']);
|
||||||
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.density.' name ' = cell(' int2str(nar) ',1);']);
|
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.density.' name ' = cell(' int2str(nar) ',1);']);
|
||||||
for i=1:nar
|
for i=1:nar
|
||||||
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.density.' name '(' int2str(i) ',1) = {NaN};']);
|
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.density.' name '(' int2str(i) ',1) = {NaN};']);
|
||||||
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.deciles.' name '(' int2str(i) ',1) = {NaN};']);
|
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.deciles.' name '(' int2str(i) ',1) = {NaN};']);
|
||||||
end
|
end
|
||||||
|
|
||||||
function oo_ = fill_output_structure(var1,var2,type,oo_,moment,lag,result)
|
function oo_ = fill_output_structure(var1,var2,type,oo_,moment,lag,result)
|
||||||
name = [ var1 '.' var2 ];
|
name = [ var1 '.' var2 ];
|
||||||
switch moment
|
switch moment
|
||||||
case {'mean','median','variance','hpdinf','hpdsup'}
|
case {'mean','median','variance','hpdinf','hpdsup'}
|
||||||
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.' moment '.' name '(' int2str(lag) ',1) = result;']);
|
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.' moment '.' name '(' int2str(lag) ',1) = result;']);
|
||||||
case {'deciles','density'}
|
case {'deciles','density'}
|
||||||
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.' moment '.' name '(' int2str(lag) ',1) = {result};']);
|
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.' moment '.' name '(' int2str(lag) ',1) = {result};']);
|
||||||
otherwise
|
otherwise
|
||||||
disp('fill_output_structure:: Unknown field!')
|
disp('fill_output_structure:: Unknown field!')
|
||||||
end
|
end
|
||||||
|
|
@ -1,41 +1,41 @@
|
||||||
function co = cosn(H);
|
function co = cosn(H);
|
||||||
|
|
||||||
% function co = cosn(H);
|
% function co = cosn(H);
|
||||||
% computes the cosine of the angle between the H(:,1) and its
|
% computes the cosine of the angle between the H(:,1) and its
|
||||||
% projection onto the span of H(:,2:end)
|
% projection onto the span of H(:,2:end)
|
||||||
%
|
%
|
||||||
% Not the same as multiple correlation coefficient since the means are not
|
% Not the same as multiple correlation coefficient since the means are not
|
||||||
% zero
|
% zero
|
||||||
%
|
%
|
||||||
% Copyright (C) 2008 Dynare Team
|
% Copyright (C) 2008 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
% Dynare is free software: you can redistribute it and/or modify
|
||||||
% it under the terms of the GNU General Public License as published by
|
% it under the terms of the GNU General Public License as published by
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
% the Free Software Foundation, either version 3 of the License, or
|
||||||
% (at your option) any later version.
|
% (at your option) any later version.
|
||||||
%
|
%
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
% Dynare is distributed in the hope that it will be useful,
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
y = H(:,1);
|
y = H(:,1);
|
||||||
X = H(:,2:end);
|
X = H(:,2:end);
|
||||||
|
|
||||||
% y = H(:,1);
|
% y = H(:,1);
|
||||||
% X = H(:,2:end);
|
% X = H(:,2:end);
|
||||||
|
|
||||||
yhat = X*(X\y);
|
yhat = X*(X\y);
|
||||||
if rank(yhat),
|
if rank(yhat),
|
||||||
co = y'*yhat/sqrt((y'*y)*(yhat'*yhat));
|
co = y'*yhat/sqrt((y'*y)*(yhat'*yhat));
|
||||||
else
|
else
|
||||||
co=0;
|
co=0;
|
||||||
end
|
end
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -19,80 +19,80 @@ function oo_ = covariance_mc_analysis(NumberOfSimulations,type,dname,fname,varta
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
if strcmpi(type,'posterior')
|
if strcmpi(type,'posterior')
|
||||||
TYPE = 'Posterior';
|
TYPE = 'Posterior';
|
||||||
PATH = [dname '/metropolis/'];
|
PATH = [dname '/metropolis/'];
|
||||||
else
|
else
|
||||||
TYPE = 'Prior';
|
TYPE = 'Prior';
|
||||||
PATH = [dname '/prior/moments/'];
|
PATH = [dname '/prior/moments/'];
|
||||||
end
|
end
|
||||||
|
|
||||||
indx1 = check_name(vartan,var1);
|
indx1 = check_name(vartan,var1);
|
||||||
if isempty(indx1)
|
if isempty(indx1)
|
||||||
disp([ type '_analysis:: ' var1 ' is not a stationary endogenous variable!'])
|
disp([ type '_analysis:: ' var1 ' is not a stationary endogenous variable!'])
|
||||||
|
return
|
||||||
|
end
|
||||||
|
if ~isempty(var2)
|
||||||
|
indx2 = check_name(vartan,var2);
|
||||||
|
if isempty(indx2)
|
||||||
|
disp([ type '_analysis:: ' var2 ' is not a stationary endogenous variable!'])
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
if ~isempty(var2)
|
else
|
||||||
indx2 = check_name(vartan,var2);
|
indx2 = indx1;
|
||||||
if isempty(indx2)
|
var2 = var1;
|
||||||
disp([ type '_analysis:: ' var2 ' is not a stationary endogenous variable!'])
|
end
|
||||||
return
|
|
||||||
end
|
|
||||||
else
|
|
||||||
indx2 = indx1;
|
|
||||||
var2 = var1;
|
|
||||||
end
|
|
||||||
|
|
||||||
if isfield(oo_,[ TYPE 'TheoreticalMoments'])
|
if isfield(oo_,[ TYPE 'TheoreticalMoments'])
|
||||||
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments;'])
|
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments;'])
|
||||||
if isfield(temporary_structure,'dsge')
|
if isfield(temporary_structure,'dsge')
|
||||||
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge;'])
|
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge;'])
|
||||||
if isfield(temporary_structure,'covariance')
|
if isfield(temporary_structure,'covariance')
|
||||||
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge.covariance.mean;'])
|
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge.covariance.mean;'])
|
||||||
if isfield(temporary_structure,var1)
|
if isfield(temporary_structure,var1)
|
||||||
eval(['temporary_structure_1 = oo_.' TYPE 'TheoreticalMoments.dsge.covariance.mean.' var1 ';'])
|
eval(['temporary_structure_1 = oo_.' TYPE 'TheoreticalMoments.dsge.covariance.mean.' var1 ';'])
|
||||||
if isfield(temporary_structure_1,var2)
|
if isfield(temporary_structure_1,var2)
|
||||||
|
% Nothing to do (the covariance matrix is symmetric!).
|
||||||
|
return
|
||||||
|
end
|
||||||
|
else
|
||||||
|
if isfield(temporary_structure,var2)
|
||||||
|
eval(['temporary_structure_2 = oo_.' TYPE 'TheoreticalMoments.dsge.covariance.mean.' var2 ';'])
|
||||||
|
if isfield(temporary_structure_2,var1)
|
||||||
% Nothing to do (the covariance matrix is symmetric!).
|
% Nothing to do (the covariance matrix is symmetric!).
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
else
|
|
||||||
if isfield(temporary_structure,var2)
|
|
||||||
eval(['temporary_structure_2 = oo_.' TYPE 'TheoreticalMoments.dsge.covariance.mean.' var2 ';'])
|
|
||||||
if isfield(temporary_structure_2,var1)
|
|
||||||
% Nothing to do (the covariance matrix is symmetric!).
|
|
||||||
return
|
|
||||||
end
|
|
||||||
end
|
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
end
|
||||||
|
|
||||||
ListOfFiles = dir([ PATH fname '_' TYPE '2ndOrderMoments*.mat']);
|
ListOfFiles = dir([ PATH fname '_' TYPE '2ndOrderMoments*.mat']);
|
||||||
i1 = 1; tmp = zeros(NumberOfSimulations,1);
|
i1 = 1; tmp = zeros(NumberOfSimulations,1);
|
||||||
for file = 1:length(ListOfFiles)
|
for file = 1:length(ListOfFiles)
|
||||||
load([ PATH ListOfFiles(file).name ]);
|
load([ PATH ListOfFiles(file).name ]);
|
||||||
i2 = i1 + rows(Covariance_matrix) - 1;
|
i2 = i1 + rows(Covariance_matrix) - 1;
|
||||||
tmp(i1:i2) = Covariance_matrix(:,symmetric_matrix_index(indx1,indx2,nvar));
|
tmp(i1:i2) = Covariance_matrix(:,symmetric_matrix_index(indx1,indx2,nvar));
|
||||||
i1 = i2+1;
|
i1 = i2+1;
|
||||||
end
|
end
|
||||||
name = [var1 '.' var2];
|
name = [var1 '.' var2];
|
||||||
if ~isconst(tmp)
|
if ~isconst(tmp)
|
||||||
[p_mean, p_median, p_var, hpd_interval, p_deciles, density] = ...
|
[p_mean, p_median, p_var, hpd_interval, p_deciles, density] = ...
|
||||||
posterior_moments(tmp,1,mh_conf_sig);
|
posterior_moments(tmp,1,mh_conf_sig);
|
||||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.mean.' name ' = p_mean;']);
|
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.mean.' name ' = p_mean;']);
|
||||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.median.' name ' = p_median;']);
|
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.median.' name ' = p_median;']);
|
||||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.variance.' name ' = p_var;']);
|
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.variance.' name ' = p_var;']);
|
||||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.hpdinf.' name ' = hpd_interval(1);']);
|
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.hpdinf.' name ' = hpd_interval(1);']);
|
||||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.hpdsup.' name ' = hpd_interval(2);']);
|
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.hpdsup.' name ' = hpd_interval(2);']);
|
||||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.deciles.' name ' = p_deciles;']);
|
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.deciles.' name ' = p_deciles;']);
|
||||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.density.' name ' = density;']);
|
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.density.' name ' = density;']);
|
||||||
else
|
else
|
||||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.mean.' name ' = NaN;']);
|
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.mean.' name ' = NaN;']);
|
||||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.median.' name ' = NaN;']);
|
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.median.' name ' = NaN;']);
|
||||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.variance.' name ' = NaN;']);
|
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.variance.' name ' = NaN;']);
|
||||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.hpdinf.' name ' = NaN;']);
|
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.hpdinf.' name ' = NaN;']);
|
||||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.hpdsup.' name ' = NaN;']);
|
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.hpdsup.' name ' = NaN;']);
|
||||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.deciles.' name ' = NaN;']);
|
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.deciles.' name ' = NaN;']);
|
||||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.density.' name ' = NaN;']);
|
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.density.' name ' = NaN;']);
|
||||||
end
|
end
|
||||||
284
matlab/csminit.m
284
matlab/csminit.m
|
|
@ -60,152 +60,152 @@ g = g0;
|
||||||
gnorm = norm(g);
|
gnorm = norm(g);
|
||||||
%
|
%
|
||||||
if (gnorm < 1.e-12) & ~badg % put ~badg 8/4/94
|
if (gnorm < 1.e-12) & ~badg % put ~badg 8/4/94
|
||||||
retcode =1;
|
retcode =1;
|
||||||
dxnorm=0;
|
dxnorm=0;
|
||||||
% gradient convergence
|
% gradient convergence
|
||||||
else
|
else
|
||||||
% with badg true, we don't try to match rate of improvement to directional
|
% with badg true, we don't try to match rate of improvement to directional
|
||||||
% derivative. We're satisfied just to get some improvement in f.
|
% derivative. We're satisfied just to get some improvement in f.
|
||||||
%
|
%
|
||||||
%if(badg)
|
%if(badg)
|
||||||
% dx = -g*FCHANGE/(gnorm*gnorm);
|
% dx = -g*FCHANGE/(gnorm*gnorm);
|
||||||
% dxnorm = norm(dx);
|
% dxnorm = norm(dx);
|
||||||
% if dxnorm > 1e12
|
% if dxnorm > 1e12
|
||||||
% disp('Bad, small gradient problem.')
|
% disp('Bad, small gradient problem.')
|
||||||
% dx = dx*FCHANGE/dxnorm;
|
% dx = dx*FCHANGE/dxnorm;
|
||||||
% end
|
% end
|
||||||
%else
|
%else
|
||||||
% Gauss-Newton step;
|
% Gauss-Newton step;
|
||||||
%---------- Start of 7/19/93 mod ---------------
|
%---------- Start of 7/19/93 mod ---------------
|
||||||
%[v d] = eig(H0);
|
%[v d] = eig(H0);
|
||||||
%toc
|
%toc
|
||||||
%d=max(1e-10,abs(diag(d)));
|
%d=max(1e-10,abs(diag(d)));
|
||||||
%d=abs(diag(d));
|
%d=abs(diag(d));
|
||||||
%dx = -(v.*(ones(size(v,1),1)*d'))*(v'*g);
|
%dx = -(v.*(ones(size(v,1),1)*d'))*(v'*g);
|
||||||
% toc
|
% toc
|
||||||
dx = -H0*g;
|
dx = -H0*g;
|
||||||
% toc
|
% toc
|
||||||
dxnorm = norm(dx);
|
dxnorm = norm(dx);
|
||||||
if dxnorm > 1e12
|
if dxnorm > 1e12
|
||||||
disp('Near-singular H problem.')
|
disp('Near-singular H problem.')
|
||||||
dx = dx*FCHANGE/dxnorm;
|
dx = dx*FCHANGE/dxnorm;
|
||||||
end
|
end
|
||||||
dfhat = dx'*g0;
|
dfhat = dx'*g0;
|
||||||
%end
|
%end
|
||||||
%
|
%
|
||||||
%
|
%
|
||||||
if ~badg
|
if ~badg
|
||||||
% test for alignment of dx with gradient and fix if necessary
|
% test for alignment of dx with gradient and fix if necessary
|
||||||
a = -dfhat/(gnorm*dxnorm);
|
a = -dfhat/(gnorm*dxnorm);
|
||||||
if a<ANGLE
|
if a<ANGLE
|
||||||
dx = dx - (ANGLE*dxnorm/gnorm+dfhat/(gnorm*gnorm))*g;
|
dx = dx - (ANGLE*dxnorm/gnorm+dfhat/(gnorm*gnorm))*g;
|
||||||
dfhat = dx'*g;
|
dfhat = dx'*g;
|
||||||
dxnorm = norm(dx);
|
dxnorm = norm(dx);
|
||||||
disp(sprintf('Correct for low angle: %g',a))
|
disp(sprintf('Correct for low angle: %g',a))
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
disp(sprintf('Predicted improvement: %18.9f',-dfhat/2))
|
disp(sprintf('Predicted improvement: %18.9f',-dfhat/2))
|
||||||
%
|
%
|
||||||
% Have OK dx, now adjust length of step (lambda) until min and
|
% Have OK dx, now adjust length of step (lambda) until min and
|
||||||
% max improvement rate criteria are met.
|
% max improvement rate criteria are met.
|
||||||
done=0;
|
done=0;
|
||||||
factor=3;
|
factor=3;
|
||||||
shrink=1;
|
shrink=1;
|
||||||
lambdaMin=0;
|
lambdaMin=0;
|
||||||
lambdaMax=inf;
|
lambdaMax=inf;
|
||||||
lambdaPeak=0;
|
lambdaPeak=0;
|
||||||
fPeak=f0;
|
fPeak=f0;
|
||||||
lambdahat=0;
|
lambdahat=0;
|
||||||
while ~done
|
while ~done
|
||||||
if size(x0,2)>1
|
if size(x0,2)>1
|
||||||
dxtest=x0+dx'*lambda;
|
dxtest=x0+dx'*lambda;
|
||||||
else
|
else
|
||||||
dxtest=x0+dx*lambda;
|
dxtest=x0+dx*lambda;
|
||||||
end
|
end
|
||||||
% home
|
% home
|
||||||
f = feval(fcn,dxtest,varargin{:});
|
f = feval(fcn,dxtest,varargin{:});
|
||||||
%ARGLIST
|
%ARGLIST
|
||||||
%f = feval(fcn,dxtest,P1,P2,P3,P4,P5,P6,P7,P8,P9,P10,P11,P12,P13);
|
%f = feval(fcn,dxtest,P1,P2,P3,P4,P5,P6,P7,P8,P9,P10,P11,P12,P13);
|
||||||
% f = feval(fcn,x0+dx*lambda,P1,P2,P3,P4,P5,P6,P7,P8);
|
% f = feval(fcn,x0+dx*lambda,P1,P2,P3,P4,P5,P6,P7,P8);
|
||||||
disp(sprintf('lambda = %10.5g; f = %20.7f',lambda,f ))
|
disp(sprintf('lambda = %10.5g; f = %20.7f',lambda,f ))
|
||||||
%debug
|
%debug
|
||||||
%disp(sprintf('Improvement too great? f0-f: %g, criterion: %g',f0-f,-(1-THETA)*dfhat*lambda))
|
%disp(sprintf('Improvement too great? f0-f: %g, criterion: %g',f0-f,-(1-THETA)*dfhat*lambda))
|
||||||
if f<fhat
|
if f<fhat
|
||||||
fhat=f;
|
fhat=f;
|
||||||
xhat=dxtest;
|
xhat=dxtest;
|
||||||
lambdahat = lambda;
|
lambdahat = lambda;
|
||||||
end
|
end
|
||||||
fcount=fcount+1;
|
fcount=fcount+1;
|
||||||
shrinkSignal = (~badg & (f0-f < max([-THETA*dfhat*lambda 0]))) | (badg & (f0-f) < 0) ;
|
shrinkSignal = (~badg & (f0-f < max([-THETA*dfhat*lambda 0]))) | (badg & (f0-f) < 0) ;
|
||||||
growSignal = ~badg & ( (lambda > 0) & (f0-f > -(1-THETA)*dfhat*lambda) );
|
growSignal = ~badg & ( (lambda > 0) & (f0-f > -(1-THETA)*dfhat*lambda) );
|
||||||
if shrinkSignal & ( (lambda>lambdaPeak) | (lambda<0) )
|
if shrinkSignal & ( (lambda>lambdaPeak) | (lambda<0) )
|
||||||
if (lambda>0) & ((~shrink) | (lambda/factor <= lambdaPeak))
|
if (lambda>0) & ((~shrink) | (lambda/factor <= lambdaPeak))
|
||||||
shrink=1;
|
shrink=1;
|
||||||
factor=factor^.6;
|
factor=factor^.6;
|
||||||
while lambda/factor <= lambdaPeak
|
while lambda/factor <= lambdaPeak
|
||||||
factor=factor^.6;
|
factor=factor^.6;
|
||||||
|
end
|
||||||
|
%if (abs(lambda)*(factor-1)*dxnorm < MINDX) | (abs(lambda)*(factor-1) < MINLAMB)
|
||||||
|
if abs(factor-1)<MINDFAC
|
||||||
|
if abs(lambda)<4
|
||||||
|
retcode=2;
|
||||||
|
else
|
||||||
|
retcode=7;
|
||||||
|
end
|
||||||
|
done=1;
|
||||||
|
end
|
||||||
end
|
end
|
||||||
%if (abs(lambda)*(factor-1)*dxnorm < MINDX) | (abs(lambda)*(factor-1) < MINLAMB)
|
if (lambda<lambdaMax) & (lambda>lambdaPeak)
|
||||||
if abs(factor-1)<MINDFAC
|
lambdaMax=lambda;
|
||||||
if abs(lambda)<4
|
|
||||||
retcode=2;
|
|
||||||
else
|
|
||||||
retcode=7;
|
|
||||||
end
|
|
||||||
done=1;
|
|
||||||
end
|
end
|
||||||
end
|
lambda=lambda/factor;
|
||||||
if (lambda<lambdaMax) & (lambda>lambdaPeak)
|
if abs(lambda) < MINLAMB
|
||||||
lambdaMax=lambda;
|
if (lambda > 0) & (f0 <= fhat)
|
||||||
end
|
% try going against gradient, which may be inaccurate
|
||||||
lambda=lambda/factor;
|
lambda = -lambda*factor^6
|
||||||
if abs(lambda) < MINLAMB
|
else
|
||||||
if (lambda > 0) & (f0 <= fhat)
|
if lambda < 0
|
||||||
% try going against gradient, which may be inaccurate
|
retcode = 6;
|
||||||
lambda = -lambda*factor^6
|
else
|
||||||
|
retcode = 3;
|
||||||
|
end
|
||||||
|
done = 1;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
elseif (growSignal & lambda>0) | (shrinkSignal & ((lambda <= lambdaPeak) & (lambda>0)))
|
||||||
|
if shrink
|
||||||
|
shrink=0;
|
||||||
|
factor = factor^.6;
|
||||||
|
%if ( abs(lambda)*(factor-1)*dxnorm< MINDX ) | ( abs(lambda)*(factor-1)< MINLAMB)
|
||||||
|
if abs(factor-1)<MINDFAC
|
||||||
|
if abs(lambda)<4
|
||||||
|
retcode=4;
|
||||||
|
else
|
||||||
|
retcode=7;
|
||||||
|
end
|
||||||
|
done=1;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
if ( f<fPeak ) & (lambda>0)
|
||||||
|
fPeak=f;
|
||||||
|
lambdaPeak=lambda;
|
||||||
|
if lambdaMax<=lambdaPeak
|
||||||
|
lambdaMax=lambdaPeak*factor*factor;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
lambda=lambda*factor;
|
||||||
|
if abs(lambda) > 1e20;
|
||||||
|
retcode = 5;
|
||||||
|
done =1;
|
||||||
|
end
|
||||||
|
else
|
||||||
|
done=1;
|
||||||
|
if factor < 1.2
|
||||||
|
retcode=7;
|
||||||
else
|
else
|
||||||
if lambda < 0
|
retcode=0;
|
||||||
retcode = 6;
|
|
||||||
else
|
|
||||||
retcode = 3;
|
|
||||||
end
|
|
||||||
done = 1;
|
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
elseif (growSignal & lambda>0) | (shrinkSignal & ((lambda <= lambdaPeak) & (lambda>0)))
|
end
|
||||||
if shrink
|
|
||||||
shrink=0;
|
|
||||||
factor = factor^.6;
|
|
||||||
%if ( abs(lambda)*(factor-1)*dxnorm< MINDX ) | ( abs(lambda)*(factor-1)< MINLAMB)
|
|
||||||
if abs(factor-1)<MINDFAC
|
|
||||||
if abs(lambda)<4
|
|
||||||
retcode=4;
|
|
||||||
else
|
|
||||||
retcode=7;
|
|
||||||
end
|
|
||||||
done=1;
|
|
||||||
end
|
|
||||||
end
|
|
||||||
if ( f<fPeak ) & (lambda>0)
|
|
||||||
fPeak=f;
|
|
||||||
lambdaPeak=lambda;
|
|
||||||
if lambdaMax<=lambdaPeak
|
|
||||||
lambdaMax=lambdaPeak*factor*factor;
|
|
||||||
end
|
|
||||||
end
|
|
||||||
lambda=lambda*factor;
|
|
||||||
if abs(lambda) > 1e20;
|
|
||||||
retcode = 5;
|
|
||||||
done =1;
|
|
||||||
end
|
|
||||||
else
|
|
||||||
done=1;
|
|
||||||
if factor < 1.2
|
|
||||||
retcode=7;
|
|
||||||
else
|
|
||||||
retcode=0;
|
|
||||||
end
|
|
||||||
end
|
|
||||||
end
|
|
||||||
end
|
end
|
||||||
disp(sprintf('Norm of dx %10.5g', dxnorm))
|
disp(sprintf('Norm of dx %10.5g', dxnorm))
|
||||||
|
|
|
||||||
|
|
@ -1,309 +1,309 @@
|
||||||
function [fh,xh,gh,H,itct,fcount,retcodeh] = csminwel(fcn,x0,H0,grad,crit,nit,method,varargin)
|
function [fh,xh,gh,H,itct,fcount,retcodeh] = csminwel(fcn,x0,H0,grad,crit,nit,method,varargin)
|
||||||
%[fhat,xhat,ghat,Hhat,itct,fcount,retcodehat] = csminwel(fcn,x0,H0,grad,crit,nit,method,varargin)
|
%[fhat,xhat,ghat,Hhat,itct,fcount,retcodehat] = csminwel(fcn,x0,H0,grad,crit,nit,method,varargin)
|
||||||
% fcn: string naming the objective function to be minimized
|
% fcn: string naming the objective function to be minimized
|
||||||
% x0: initial value of the parameter vector
|
% x0: initial value of the parameter vector
|
||||||
% H0: initial value for the inverse Hessian. Must be positive definite.
|
% H0: initial value for the inverse Hessian. Must be positive definite.
|
||||||
% grad: Either a string naming a function that calculates the gradient, or the null matrix.
|
% grad: Either a string naming a function that calculates the gradient, or the null matrix.
|
||||||
% If it's null, the program calculates a numerical gradient. In this case fcn must
|
% If it's null, the program calculates a numerical gradient. In this case fcn must
|
||||||
% be written so that it can take a matrix argument and produce a row vector of values.
|
% be written so that it can take a matrix argument and produce a row vector of values.
|
||||||
% crit: Convergence criterion. Iteration will cease when it proves impossible to improve the
|
% crit: Convergence criterion. Iteration will cease when it proves impossible to improve the
|
||||||
% function value by more than crit.
|
% function value by more than crit.
|
||||||
% nit: Maximum number of iterations.
|
% nit: Maximum number of iterations.
|
||||||
% method: integer scalar, 2, 3 or 5 points formula.
|
% method: integer scalar, 2, 3 or 5 points formula.
|
||||||
% penalty: scalar double, size of the penality.
|
% penalty: scalar double, size of the penality.
|
||||||
% varargin: A list of optional length of additional parameters that get handed off to fcn each
|
% varargin: A list of optional length of additional parameters that get handed off to fcn each
|
||||||
% time it is called.
|
% time it is called.
|
||||||
% Note that if the program ends abnormally, it is possible to retrieve the current x,
|
% Note that if the program ends abnormally, it is possible to retrieve the current x,
|
||||||
% f, and H from the files g1.mat and H.mat that are written at each iteration and at each
|
% f, and H from the files g1.mat and H.mat that are written at each iteration and at each
|
||||||
% hessian update, respectively. (When the routine hits certain kinds of difficulty, it
|
% hessian update, respectively. (When the routine hits certain kinds of difficulty, it
|
||||||
% write g2.mat and g3.mat as well. If all were written at about the same time, any of them
|
% write g2.mat and g3.mat as well. If all were written at about the same time, any of them
|
||||||
% may be a decent starting point. One can also start from the one with best function value.)
|
% may be a decent starting point. One can also start from the one with best function value.)
|
||||||
|
|
||||||
% Original file downloaded from:
|
% Original file downloaded from:
|
||||||
% http://sims.princeton.edu/yftp/optimize/mfiles/csminwel.m
|
% http://sims.princeton.edu/yftp/optimize/mfiles/csminwel.m
|
||||||
|
|
||||||
% Copyright (C) 1993-2007 Christopher Sims
|
% Copyright (C) 1993-2007 Christopher Sims
|
||||||
% Copyright (C) 2006-2008 Dynare Team
|
% Copyright (C) 2006-2008 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
% Dynare is free software: you can redistribute it and/or modify
|
||||||
% it under the terms of the GNU General Public License as published by
|
% it under the terms of the GNU General Public License as published by
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
% the Free Software Foundation, either version 3 of the License, or
|
||||||
% (at your option) any later version.
|
% (at your option) any later version.
|
||||||
%
|
%
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
% Dynare is distributed in the hope that it will be useful,
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
global bayestopt_
|
global bayestopt_
|
||||||
fh = [];
|
fh = [];
|
||||||
xh = [];
|
xh = [];
|
||||||
[nx,no]=size(x0);
|
[nx,no]=size(x0);
|
||||||
nx=max(nx,no);
|
nx=max(nx,no);
|
||||||
Verbose=1;
|
Verbose=1;
|
||||||
NumGrad= isempty(grad);
|
NumGrad= isempty(grad);
|
||||||
done=0;
|
done=0;
|
||||||
itct=0;
|
itct=0;
|
||||||
fcount=0;
|
fcount=0;
|
||||||
snit=100;
|
snit=100;
|
||||||
%tailstr = ')';
|
%tailstr = ')';
|
||||||
%stailstr = [];
|
%stailstr = [];
|
||||||
% Lines below make the number of Pi's optional. This is inefficient, though, and precludes
|
% Lines below make the number of Pi's optional. This is inefficient, though, and precludes
|
||||||
% use of the matlab compiler. Without them, we use feval and the number of Pi's must be
|
% use of the matlab compiler. Without them, we use feval and the number of Pi's must be
|
||||||
% changed with the editor for each application. Places where this is required are marked
|
% changed with the editor for each application. Places where this is required are marked
|
||||||
% with ARGLIST comments
|
% with ARGLIST comments
|
||||||
%for i=nargin-6:-1:1
|
%for i=nargin-6:-1:1
|
||||||
% tailstr=[ ',P' num2str(i) tailstr];
|
% tailstr=[ ',P' num2str(i) tailstr];
|
||||||
% stailstr=[' P' num2str(i) stailstr];
|
% stailstr=[' P' num2str(i) stailstr];
|
||||||
%end
|
%end
|
||||||
|
|
||||||
[f0,cost_flag] = feval(fcn,x0,varargin{:});
|
[f0,cost_flag] = feval(fcn,x0,varargin{:});
|
||||||
if ~cost_flag
|
if ~cost_flag
|
||||||
disp('Bad initial parameter.')
|
disp('Bad initial parameter.')
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
|
|
||||||
if NumGrad
|
if NumGrad
|
||||||
switch method
|
switch method
|
||||||
case 2
|
case 2
|
||||||
[g,badg] = numgrad(fcn,x0, varargin{:});
|
[g,badg] = numgrad(fcn,x0, varargin{:});
|
||||||
case 3
|
case 3
|
||||||
[g,badg] = numgrad3(fcn,x0, varargin{:});
|
[g,badg] = numgrad3(fcn,x0, varargin{:});
|
||||||
case 5
|
case 5
|
||||||
[g,badg] = numgrad5(fcn,x0, varargin{:});
|
[g,badg] = numgrad5(fcn,x0, varargin{:});
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
[g,badg] = feval(grad,x0,varargin{:});
|
[g,badg] = feval(grad,x0,varargin{:});
|
||||||
end
|
end
|
||||||
retcode3=101;
|
retcode3=101;
|
||||||
x=x0;
|
x=x0;
|
||||||
f=f0;
|
f=f0;
|
||||||
H=H0;
|
H=H0;
|
||||||
cliff=0;
|
cliff=0;
|
||||||
while ~done
|
while ~done
|
||||||
bayestopt_.penalty = f;
|
bayestopt_.penalty = f;
|
||||||
g1=[]; g2=[]; g3=[];
|
g1=[]; g2=[]; g3=[];
|
||||||
%addition fj. 7/6/94 for control
|
%addition fj. 7/6/94 for control
|
||||||
disp('-----------------')
|
disp('-----------------')
|
||||||
disp('-----------------')
|
disp('-----------------')
|
||||||
%disp('f and x at the beginning of new iteration')
|
%disp('f and x at the beginning of new iteration')
|
||||||
disp(sprintf('f at the beginning of new iteration, %20.10f',f))
|
disp(sprintf('f at the beginning of new iteration, %20.10f',f))
|
||||||
%-----------Comment out this line if the x vector is long----------------
|
%-----------Comment out this line if the x vector is long----------------
|
||||||
% disp([sprintf('x = ') sprintf('%15.8g %15.8g %15.8g %15.8g\n',x)]);
|
% disp([sprintf('x = ') sprintf('%15.8g %15.8g %15.8g %15.8g\n',x)]);
|
||||||
%-------------------------
|
%-------------------------
|
||||||
itct=itct+1;
|
itct=itct+1;
|
||||||
[f1 x1 fc retcode1] = csminit(fcn,x,f,g,badg,H,varargin{:});
|
[f1 x1 fc retcode1] = csminit(fcn,x,f,g,badg,H,varargin{:});
|
||||||
%ARGLIST
|
%ARGLIST
|
||||||
%[f1 x1 fc retcode1] = csminit(fcn,x,f,g,badg,H,P1,P2,P3,P4,P5,P6,P7,...
|
%[f1 x1 fc retcode1] = csminit(fcn,x,f,g,badg,H,P1,P2,P3,P4,P5,P6,P7,...
|
||||||
% P8,P9,P10,P11,P12,P13);
|
% P8,P9,P10,P11,P12,P13);
|
||||||
% itct=itct+1;
|
% itct=itct+1;
|
||||||
fcount = fcount+fc;
|
fcount = fcount+fc;
|
||||||
% erased on 8/4/94
|
% erased on 8/4/94
|
||||||
% if (retcode == 1) | (abs(f1-f) < crit)
|
% if (retcode == 1) | (abs(f1-f) < crit)
|
||||||
% done=1;
|
% done=1;
|
||||||
% end
|
% end
|
||||||
% if itct > nit
|
% if itct > nit
|
||||||
% done = 1;
|
% done = 1;
|
||||||
% retcode = -retcode;
|
% retcode = -retcode;
|
||||||
% end
|
% end
|
||||||
if retcode1 ~= 1
|
if retcode1 ~= 1
|
||||||
if retcode1==2 | retcode1==4
|
if retcode1==2 | retcode1==4
|
||||||
wall1=1; badg1=1;
|
wall1=1; badg1=1;
|
||||||
else
|
else
|
||||||
if NumGrad
|
if NumGrad
|
||||||
switch method
|
switch method
|
||||||
case 2
|
case 2
|
||||||
[g1 badg1] = numgrad(fcn, x1,varargin{:});
|
[g1 badg1] = numgrad(fcn, x1,varargin{:});
|
||||||
case 3
|
case 3
|
||||||
[g1 badg1] = numgrad3(fcn, x1,varargin{:});
|
[g1 badg1] = numgrad3(fcn, x1,varargin{:});
|
||||||
case 5
|
case 5
|
||||||
[g1,badg1] = numgrad5(fcn,x0, varargin{:});
|
[g1,badg1] = numgrad5(fcn,x0, varargin{:});
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
[g1 badg1] = feval(grad,x1,varargin{:});
|
[g1 badg1] = feval(grad,x1,varargin{:});
|
||||||
end
|
end
|
||||||
wall1=badg1;
|
wall1=badg1;
|
||||||
% g1
|
% g1
|
||||||
save g1.mat g1 x1 f1 varargin;
|
save g1.mat g1 x1 f1 varargin;
|
||||||
%ARGLIST
|
%ARGLIST
|
||||||
%save g1 g1 x1 f1 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13;
|
%save g1 g1 x1 f1 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13;
|
||||||
end
|
end
|
||||||
if wall1 % & (~done) by Jinill
|
if wall1 % & (~done) by Jinill
|
||||||
% Bad gradient or back and forth on step length. Possibly at
|
% Bad gradient or back and forth on step length. Possibly at
|
||||||
% cliff edge. Try perturbing search direction.
|
% cliff edge. Try perturbing search direction.
|
||||||
%
|
%
|
||||||
%fcliff=fh;xcliff=xh;
|
%fcliff=fh;xcliff=xh;
|
||||||
Hcliff=H+diag(diag(H).*rand(nx,1));
|
Hcliff=H+diag(diag(H).*rand(nx,1));
|
||||||
disp('Cliff. Perturbing search direction.')
|
disp('Cliff. Perturbing search direction.')
|
||||||
[f2 x2 fc retcode2] = csminit(fcn,x,f,g,badg,Hcliff,varargin{:});
|
[f2 x2 fc retcode2] = csminit(fcn,x,f,g,badg,Hcliff,varargin{:});
|
||||||
%ARGLIST
|
%ARGLIST
|
||||||
%[f2 x2 fc retcode2] = csminit(fcn,x,f,g,badg,Hcliff,P1,P2,P3,P4,...
|
%[f2 x2 fc retcode2] = csminit(fcn,x,f,g,badg,Hcliff,P1,P2,P3,P4,...
|
||||||
% P5,P6,P7,P8,P9,P10,P11,P12,P13);
|
% P5,P6,P7,P8,P9,P10,P11,P12,P13);
|
||||||
fcount = fcount+fc; % put by Jinill
|
fcount = fcount+fc; % put by Jinill
|
||||||
if f2 < f
|
if f2 < f
|
||||||
if retcode2==2 | retcode2==4
|
if retcode2==2 | retcode2==4
|
||||||
wall2=1; badg2=1;
|
wall2=1; badg2=1;
|
||||||
else
|
else
|
||||||
if NumGrad
|
if NumGrad
|
||||||
switch method
|
switch method
|
||||||
case 2
|
case 2
|
||||||
[g2 badg2] = numgrad(fcn, x2,varargin{:});
|
[g2 badg2] = numgrad(fcn, x2,varargin{:});
|
||||||
case 3
|
case 3
|
||||||
[g2 badg2] = numgrad3(fcn, x2,varargin{:});
|
[g2 badg2] = numgrad3(fcn, x2,varargin{:});
|
||||||
case 5
|
case 5
|
||||||
[g2,badg2] = numgrad5(fcn,x0, varargin{:});
|
[g2,badg2] = numgrad5(fcn,x0, varargin{:});
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
[g2 badg2] = feval(grad,x2,varargin{:});
|
[g2 badg2] = feval(grad,x2,varargin{:});
|
||||||
end
|
end
|
||||||
wall2=badg2;
|
wall2=badg2;
|
||||||
% g2
|
% g2
|
||||||
badg2
|
badg2
|
||||||
save g2.mat g2 x2 f2 varargin
|
save g2.mat g2 x2 f2 varargin
|
||||||
%ARGLIST
|
%ARGLIST
|
||||||
%save g2 g2 x2 f2 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13;
|
%save g2 g2 x2 f2 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13;
|
||||||
end
|
end
|
||||||
if wall2
|
if wall2
|
||||||
disp('Cliff again. Try traversing')
|
disp('Cliff again. Try traversing')
|
||||||
if norm(x2-x1) < 1e-13
|
if norm(x2-x1) < 1e-13
|
||||||
f3=f; x3=x; badg3=1;retcode3=101;
|
f3=f; x3=x; badg3=1;retcode3=101;
|
||||||
else
|
else
|
||||||
gcliff=((f2-f1)/((norm(x2-x1))^2))*(x2-x1);
|
gcliff=((f2-f1)/((norm(x2-x1))^2))*(x2-x1);
|
||||||
if(size(x0,2)>1), gcliff=gcliff', end
|
if(size(x0,2)>1), gcliff=gcliff', end
|
||||||
[f3 x3 fc retcode3] = csminit(fcn,x,f,gcliff,0,eye(nx),varargin{:});
|
[f3 x3 fc retcode3] = csminit(fcn,x,f,gcliff,0,eye(nx),varargin{:});
|
||||||
%ARGLIST
|
%ARGLIST
|
||||||
%[f3 x3 fc retcode3] = csminit(fcn,x,f,gcliff,0,eye(nx),P1,P2,P3,...
|
%[f3 x3 fc retcode3] = csminit(fcn,x,f,gcliff,0,eye(nx),P1,P2,P3,...
|
||||||
% P4,P5,P6,P7,P8,...
|
% P4,P5,P6,P7,P8,...
|
||||||
% P9,P10,P11,P12,P13);
|
% P9,P10,P11,P12,P13);
|
||||||
fcount = fcount+fc; % put by Jinill
|
fcount = fcount+fc; % put by Jinill
|
||||||
if retcode3==2 | retcode3==4
|
if retcode3==2 | retcode3==4
|
||||||
wall3=1; badg3=1;
|
wall3=1; badg3=1;
|
||||||
else
|
else
|
||||||
if NumGrad
|
if NumGrad
|
||||||
switch method
|
switch method
|
||||||
case 2
|
case 2
|
||||||
[g3 badg3] = numgrad(fcn, x3,varargin{:});
|
[g3 badg3] = numgrad(fcn, x3,varargin{:});
|
||||||
case 3
|
case 3
|
||||||
[g3 badg3] = numgrad3(fcn, x3,varargin{:});
|
[g3 badg3] = numgrad3(fcn, x3,varargin{:});
|
||||||
case 5
|
case 5
|
||||||
[g3,badg3] = numgrad5(fcn,x0, varargin{:});
|
[g3,badg3] = numgrad5(fcn,x0, varargin{:});
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
[g3 badg3] = feval(grad,x3,varargin{:});
|
[g3 badg3] = feval(grad,x3,varargin{:});
|
||||||
end
|
end
|
||||||
wall3=badg3;
|
wall3=badg3;
|
||||||
% g3
|
% g3
|
||||||
save g3.mat g3 x3 f3 varargin;
|
save g3.mat g3 x3 f3 varargin;
|
||||||
%ARGLIST
|
%ARGLIST
|
||||||
%save g3 g3 x3 f3 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13;
|
%save g3 g3 x3 f3 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
f3=f; x3=x; badg3=1; retcode3=101;
|
f3=f; x3=x; badg3=1; retcode3=101;
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
f3=f; x3=x; badg3=1;retcode3=101;
|
f3=f; x3=x; badg3=1;retcode3=101;
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
% normal iteration, no walls, or else we're finished here.
|
% normal iteration, no walls, or else we're finished here.
|
||||||
f2=f; f3=f; badg2=1; badg3=1; retcode2=101; retcode3=101;
|
f2=f; f3=f; badg2=1; badg3=1; retcode2=101; retcode3=101;
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
f2=f;f3=f;f1=f;retcode2=retcode1;retcode3=retcode1;
|
f2=f;f3=f;f1=f;retcode2=retcode1;retcode3=retcode1;
|
||||||
end
|
end
|
||||||
%how to pick gh and xh
|
%how to pick gh and xh
|
||||||
if f3 < f - crit & badg3==0 & f3 < f2 & f3 < f1
|
if f3 < f - crit & badg3==0 & f3 < f2 & f3 < f1
|
||||||
ih=3;
|
ih=3;
|
||||||
fh=f3;xh=x3;gh=g3;badgh=badg3;retcodeh=retcode3;
|
fh=f3;xh=x3;gh=g3;badgh=badg3;retcodeh=retcode3;
|
||||||
elseif f2 < f - crit & badg2==0 & f2 < f1
|
elseif f2 < f - crit & badg2==0 & f2 < f1
|
||||||
ih=2;
|
ih=2;
|
||||||
fh=f2;xh=x2;gh=g2;badgh=badg2;retcodeh=retcode2;
|
fh=f2;xh=x2;gh=g2;badgh=badg2;retcodeh=retcode2;
|
||||||
elseif f1 < f - crit & badg1==0
|
elseif f1 < f - crit & badg1==0
|
||||||
ih=1;
|
ih=1;
|
||||||
fh=f1;xh=x1;gh=g1;badgh=badg1;retcodeh=retcode1;
|
fh=f1;xh=x1;gh=g1;badgh=badg1;retcodeh=retcode1;
|
||||||
else
|
else
|
||||||
[fh,ih] = min([f1,f2,f3]);
|
[fh,ih] = min([f1,f2,f3]);
|
||||||
%disp(sprintf('ih = %d',ih))
|
%disp(sprintf('ih = %d',ih))
|
||||||
%eval(['xh=x' num2str(ih) ';'])
|
%eval(['xh=x' num2str(ih) ';'])
|
||||||
switch ih
|
switch ih
|
||||||
case 1
|
case 1
|
||||||
xh=x1;
|
xh=x1;
|
||||||
case 2
|
case 2
|
||||||
xh=x2;
|
xh=x2;
|
||||||
case 3
|
case 3
|
||||||
xh=x3;
|
xh=x3;
|
||||||
end %case
|
end %case
|
||||||
%eval(['gh=g' num2str(ih) ';'])
|
%eval(['gh=g' num2str(ih) ';'])
|
||||||
%eval(['retcodeh=retcode' num2str(ih) ';'])
|
%eval(['retcodeh=retcode' num2str(ih) ';'])
|
||||||
retcodei=[retcode1,retcode2,retcode3];
|
retcodei=[retcode1,retcode2,retcode3];
|
||||||
retcodeh=retcodei(ih);
|
retcodeh=retcodei(ih);
|
||||||
if exist('gh')
|
if exist('gh')
|
||||||
nogh=isempty(gh);
|
nogh=isempty(gh);
|
||||||
else
|
else
|
||||||
nogh=1;
|
nogh=1;
|
||||||
end
|
end
|
||||||
if nogh
|
if nogh
|
||||||
if NumGrad
|
if NumGrad
|
||||||
switch method
|
switch method
|
||||||
case 2
|
case 2
|
||||||
[gh,badgh] = numgrad(fcn,xh,varargin{:});
|
[gh,badgh] = numgrad(fcn,xh,varargin{:});
|
||||||
case 3
|
case 3
|
||||||
[gh,badgh] = numgrad3(fcn,xh,varargin{:});
|
[gh,badgh] = numgrad3(fcn,xh,varargin{:});
|
||||||
case 5
|
case 5
|
||||||
[gh,badgh] = numgrad5(fcn,xh,varargin{:});
|
[gh,badgh] = numgrad5(fcn,xh,varargin{:});
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
[gh badgh] = feval(grad, xh,varargin{:});
|
[gh badgh] = feval(grad, xh,varargin{:});
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
badgh=1;
|
badgh=1;
|
||||||
end
|
end
|
||||||
%end of picking
|
%end of picking
|
||||||
%ih
|
%ih
|
||||||
%fh
|
%fh
|
||||||
%xh
|
%xh
|
||||||
%gh
|
%gh
|
||||||
%badgh
|
%badgh
|
||||||
stuck = (abs(fh-f) < crit);
|
stuck = (abs(fh-f) < crit);
|
||||||
if (~badg)&(~badgh)&(~stuck)
|
if (~badg)&(~badgh)&(~stuck)
|
||||||
H = bfgsi(H,gh-g,xh-x);
|
H = bfgsi(H,gh-g,xh-x);
|
||||||
end
|
end
|
||||||
if Verbose
|
if Verbose
|
||||||
disp('----')
|
disp('----')
|
||||||
disp(sprintf('Improvement on iteration %d = %18.9f',itct,f-fh))
|
disp(sprintf('Improvement on iteration %d = %18.9f',itct,f-fh))
|
||||||
end
|
end
|
||||||
% if Verbose
|
% if Verbose
|
||||||
if itct > nit
|
if itct > nit
|
||||||
disp('iteration count termination')
|
disp('iteration count termination')
|
||||||
done = 1;
|
done = 1;
|
||||||
elseif stuck
|
elseif stuck
|
||||||
disp('improvement < crit termination')
|
disp('improvement < crit termination')
|
||||||
done = 1;
|
done = 1;
|
||||||
end
|
end
|
||||||
rc=retcodeh;
|
rc=retcodeh;
|
||||||
if rc == 1
|
if rc == 1
|
||||||
disp('zero gradient')
|
disp('zero gradient')
|
||||||
elseif rc == 6
|
elseif rc == 6
|
||||||
disp('smallest step still improving too slow, reversed gradient')
|
disp('smallest step still improving too slow, reversed gradient')
|
||||||
elseif rc == 5
|
elseif rc == 5
|
||||||
disp('largest step still improving too fast')
|
disp('largest step still improving too fast')
|
||||||
elseif (rc == 4) | (rc==2)
|
elseif (rc == 4) | (rc==2)
|
||||||
disp('back and forth on step length never finished')
|
disp('back and forth on step length never finished')
|
||||||
elseif rc == 3
|
elseif rc == 3
|
||||||
disp('smallest step still improving too slow')
|
disp('smallest step still improving too slow')
|
||||||
elseif rc == 7
|
elseif rc == 7
|
||||||
disp('warning: possible inaccuracy in H matrix')
|
disp('warning: possible inaccuracy in H matrix')
|
||||||
end
|
end
|
||||||
% end
|
% end
|
||||||
f=fh;
|
f=fh;
|
||||||
x=xh;
|
x=xh;
|
||||||
g=gh;
|
g=gh;
|
||||||
badg=badgh;
|
badg=badgh;
|
||||||
end
|
end
|
||||||
% what about making an m-file of 10 lines including numgrad.m
|
% what about making an m-file of 10 lines including numgrad.m
|
||||||
% since it appears three times in csminwel.m
|
% since it appears three times in csminwel.m
|
||||||
200
matlab/csolve.m
200
matlab/csolve.m
|
|
@ -49,121 +49,121 @@ alpha=1e-3;
|
||||||
%---------------------------------------
|
%---------------------------------------
|
||||||
%------------ verbose ----------------
|
%------------ verbose ----------------
|
||||||
verbose=0;% if this is set to zero, all screen output is suppressed
|
verbose=0;% if this is set to zero, all screen output is suppressed
|
||||||
%-------------------------------------
|
%-------------------------------------
|
||||||
%------------ analyticg --------------
|
%------------ analyticg --------------
|
||||||
analyticg=1-isempty(gradfun); %if the grad argument is [], numerical derivatives are used.
|
analyticg=1-isempty(gradfun); %if the grad argument is [], numerical derivatives are used.
|
||||||
%-------------------------------------
|
%-------------------------------------
|
||||||
nv=length(x);
|
nv=length(x);
|
||||||
tvec=delta*eye(nv);
|
tvec=delta*eye(nv);
|
||||||
done=0;
|
done=0;
|
||||||
if isempty(varargin)
|
if isempty(varargin)
|
||||||
f0=feval(FUN,x);
|
f0=feval(FUN,x);
|
||||||
else
|
else
|
||||||
f0=feval(FUN,x,varargin{:});
|
f0=feval(FUN,x,varargin{:});
|
||||||
end
|
end
|
||||||
af0=sum(abs(f0));
|
af0=sum(abs(f0));
|
||||||
af00=af0;
|
af00=af0;
|
||||||
itct=0;
|
itct=0;
|
||||||
while ~done
|
while ~done
|
||||||
% disp([af00-af0 crit*max(1,af0)])
|
% disp([af00-af0 crit*max(1,af0)])
|
||||||
if itct>3 & af00-af0<crit*max(1,af0) & rem(itct,2)==1
|
if itct>3 & af00-af0<crit*max(1,af0) & rem(itct,2)==1
|
||||||
randomize=1;
|
randomize=1;
|
||||||
else
|
else
|
||||||
if ~analyticg
|
if ~analyticg
|
||||||
% $$$ if isempty(varargin)
|
% $$$ if isempty(varargin)
|
||||||
% $$$ grad = (feval(FUN,x*ones(1,nv)+tvec)-f0*ones(1,nv))/delta;
|
% $$$ grad = (feval(FUN,x*ones(1,nv)+tvec)-f0*ones(1,nv))/delta;
|
||||||
% $$$ else
|
% $$$ else
|
||||||
% $$$ grad = (feval(FUN,x*ones(1,nv)+tvec,varargin{:})-f0*ones(1,nv))/delta;
|
% $$$ grad = (feval(FUN,x*ones(1,nv)+tvec,varargin{:})-f0*ones(1,nv))/delta;
|
||||||
% $$$ end
|
% $$$ end
|
||||||
grad = zeros(nv,nv);
|
grad = zeros(nv,nv);
|
||||||
for i=1:nv
|
for i=1:nv
|
||||||
grad(:,i) = (feval(FUN,x+tvec(:,i),varargin{:})-f0)/delta;
|
grad(:,i) = (feval(FUN,x+tvec(:,i),varargin{:})-f0)/delta;
|
||||||
end
|
|
||||||
else % use analytic gradient
|
|
||||||
% grad=feval(gradfun,x,varargin{:});
|
|
||||||
[f0,grad] = feval(gradfun,x,varargin{:});
|
|
||||||
end
|
|
||||||
if isreal(grad)
|
|
||||||
if rcond(grad)<1e-12
|
|
||||||
grad=grad+tvec;
|
|
||||||
end
|
|
||||||
dx0=-grad\f0;
|
|
||||||
randomize=0;
|
|
||||||
else
|
|
||||||
if(verbose),disp('gradient imaginary'),end
|
|
||||||
randomize=1;
|
|
||||||
end
|
|
||||||
end
|
|
||||||
if randomize
|
|
||||||
if(verbose),fprintf(1,'\n Random Search'),end
|
|
||||||
dx0=norm(x)./randn(size(x));
|
|
||||||
end
|
|
||||||
lambda=1;
|
|
||||||
lambdamin=1;
|
|
||||||
fmin=f0;
|
|
||||||
xmin=x;
|
|
||||||
afmin=af0;
|
|
||||||
dxSize=norm(dx0);
|
|
||||||
factor=.6;
|
|
||||||
shrink=1;
|
|
||||||
subDone=0;
|
|
||||||
while ~subDone
|
|
||||||
dx=lambda*dx0;
|
|
||||||
f=feval(FUN,x+dx,varargin{:});
|
|
||||||
af=sum(abs(f));
|
|
||||||
if af<afmin
|
|
||||||
afmin=af;
|
|
||||||
fmin=f;
|
|
||||||
lambdamin=lambda;
|
|
||||||
xmin=x+dx;
|
|
||||||
end
|
|
||||||
if ((lambda >0) & (af0-af < alpha*lambda*af0)) | ((lambda<0) & (af0-af < 0) )
|
|
||||||
if ~shrink
|
|
||||||
factor=factor^.6;
|
|
||||||
shrink=1;
|
|
||||||
end
|
|
||||||
if abs(lambda*(1-factor))*dxSize > .1*delta;
|
|
||||||
lambda = factor*lambda;
|
|
||||||
elseif (lambda > 0) & (factor==.6) %i.e., we've only been shrinking
|
|
||||||
lambda=-.3;
|
|
||||||
else %
|
|
||||||
subDone=1;
|
|
||||||
if lambda > 0
|
|
||||||
if factor==.6
|
|
||||||
rc = 2;
|
|
||||||
else
|
|
||||||
rc = 1;
|
|
||||||
end
|
|
||||||
else
|
|
||||||
rc=3;
|
|
||||||
end
|
end
|
||||||
end
|
else % use analytic gradient
|
||||||
elseif (lambda >0) & (af-af0 > (1-alpha)*lambda*af0)
|
% grad=feval(gradfun,x,varargin{:});
|
||||||
if shrink
|
[f0,grad] = feval(gradfun,x,varargin{:});
|
||||||
factor=factor^.6;
|
end
|
||||||
shrink=0;
|
if isreal(grad)
|
||||||
end
|
if rcond(grad)<1e-12
|
||||||
lambda=lambda/factor;
|
grad=grad+tvec;
|
||||||
else % good value found
|
end
|
||||||
subDone=1;
|
dx0=-grad\f0;
|
||||||
rc=0;
|
randomize=0;
|
||||||
end
|
else
|
||||||
end % while ~subDone
|
if(verbose),disp('gradient imaginary'),end
|
||||||
itct=itct+1;
|
randomize=1;
|
||||||
if(verbose)
|
end
|
||||||
fprintf(1,'\nitct %d, af %g, lambda %g, rc %g',itct,afmin,lambdamin,rc)
|
end
|
||||||
fprintf(1,'\n x %10g %10g %10g %10g',xmin);
|
if randomize
|
||||||
fprintf(1,'\n f %10g %10g %10g %10g',fmin);
|
if(verbose),fprintf(1,'\n Random Search'),end
|
||||||
end
|
dx0=norm(x)./randn(size(x));
|
||||||
x=xmin;
|
end
|
||||||
f0=fmin;
|
lambda=1;
|
||||||
af00=af0;
|
lambdamin=1;
|
||||||
af0=afmin;
|
fmin=f0;
|
||||||
if itct >= itmax
|
xmin=x;
|
||||||
done=1;
|
afmin=af0;
|
||||||
rc=4;
|
dxSize=norm(dx0);
|
||||||
elseif af0<crit;
|
factor=.6;
|
||||||
done=1;
|
shrink=1;
|
||||||
rc=0;
|
subDone=0;
|
||||||
end
|
while ~subDone
|
||||||
|
dx=lambda*dx0;
|
||||||
|
f=feval(FUN,x+dx,varargin{:});
|
||||||
|
af=sum(abs(f));
|
||||||
|
if af<afmin
|
||||||
|
afmin=af;
|
||||||
|
fmin=f;
|
||||||
|
lambdamin=lambda;
|
||||||
|
xmin=x+dx;
|
||||||
|
end
|
||||||
|
if ((lambda >0) & (af0-af < alpha*lambda*af0)) | ((lambda<0) & (af0-af < 0) )
|
||||||
|
if ~shrink
|
||||||
|
factor=factor^.6;
|
||||||
|
shrink=1;
|
||||||
|
end
|
||||||
|
if abs(lambda*(1-factor))*dxSize > .1*delta;
|
||||||
|
lambda = factor*lambda;
|
||||||
|
elseif (lambda > 0) & (factor==.6) %i.e., we've only been shrinking
|
||||||
|
lambda=-.3;
|
||||||
|
else %
|
||||||
|
subDone=1;
|
||||||
|
if lambda > 0
|
||||||
|
if factor==.6
|
||||||
|
rc = 2;
|
||||||
|
else
|
||||||
|
rc = 1;
|
||||||
|
end
|
||||||
|
else
|
||||||
|
rc=3;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
elseif (lambda >0) & (af-af0 > (1-alpha)*lambda*af0)
|
||||||
|
if shrink
|
||||||
|
factor=factor^.6;
|
||||||
|
shrink=0;
|
||||||
|
end
|
||||||
|
lambda=lambda/factor;
|
||||||
|
else % good value found
|
||||||
|
subDone=1;
|
||||||
|
rc=0;
|
||||||
|
end
|
||||||
|
end % while ~subDone
|
||||||
|
itct=itct+1;
|
||||||
|
if(verbose)
|
||||||
|
fprintf(1,'\nitct %d, af %g, lambda %g, rc %g',itct,afmin,lambdamin,rc)
|
||||||
|
fprintf(1,'\n x %10g %10g %10g %10g',xmin);
|
||||||
|
fprintf(1,'\n f %10g %10g %10g %10g',fmin);
|
||||||
|
end
|
||||||
|
x=xmin;
|
||||||
|
f0=fmin;
|
||||||
|
af00=af0;
|
||||||
|
af0=afmin;
|
||||||
|
if itct >= itmax
|
||||||
|
done=1;
|
||||||
|
rc=4;
|
||||||
|
elseif af0<crit;
|
||||||
|
done=1;
|
||||||
|
rc=0;
|
||||||
|
end
|
||||||
end
|
end
|
||||||
|
|
|
||||||
|
|
@ -1,63 +1,63 @@
|
||||||
function datatomfile (s,var_list)
|
function datatomfile (s,var_list)
|
||||||
% function datatomfile (s,var_list)
|
% function datatomfile (s,var_list)
|
||||||
% This optional command saves the simulation results in a text file. The name of each
|
% This optional command saves the simulation results in a text file. The name of each
|
||||||
% variable preceeds the corresponding results. This command must follow SIMUL.
|
% variable preceeds the corresponding results. This command must follow SIMUL.
|
||||||
%
|
%
|
||||||
% INPUTS
|
% INPUTS
|
||||||
% s: data file name
|
% s: data file name
|
||||||
% var_list: vector of selected endogenous variables
|
% var_list: vector of selected endogenous variables
|
||||||
%
|
%
|
||||||
% OUTPUTS
|
% OUTPUTS
|
||||||
% none
|
% none
|
||||||
%
|
%
|
||||||
% SPECIAL REQUIREMENTS
|
% SPECIAL REQUIREMENTS
|
||||||
% none
|
% none
|
||||||
|
|
||||||
% Copyright (C) 2001-2009 Dynare Team
|
% Copyright (C) 2001-2009 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
% Dynare is free software: you can redistribute it and/or modify
|
||||||
% it under the terms of the GNU General Public License as published by
|
% it under the terms of the GNU General Public License as published by
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
% the Free Software Foundation, either version 3 of the License, or
|
||||||
% (at your option) any later version.
|
% (at your option) any later version.
|
||||||
%
|
%
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
% Dynare is distributed in the hope that it will be useful,
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
global M_ oo_
|
global M_ oo_
|
||||||
|
|
||||||
sm=[s,'.m'];
|
sm=[s,'.m'];
|
||||||
fid=fopen(sm,'w') ;
|
fid=fopen(sm,'w') ;
|
||||||
|
|
||||||
if size(var_list,1) == 0
|
if size(var_list,1) == 0
|
||||||
var_list = M_.endo_names(1:M_.orig_endo_nbr,:);
|
var_list = M_.endo_names(1:M_.orig_endo_nbr,:);
|
||||||
end
|
end
|
||||||
n = size(var_list,1);
|
n = size(var_list,1);
|
||||||
ivar=zeros(n,1);
|
ivar=zeros(n,1);
|
||||||
for i=1:n
|
for i=1:n
|
||||||
i_tmp = strmatch(var_list(i,:),M_.endo_names,'exact');
|
i_tmp = strmatch(var_list(i,:),M_.endo_names,'exact');
|
||||||
if isempty(i_tmp)
|
if isempty(i_tmp)
|
||||||
error (['One of the specified variables does not exist']) ;
|
error (['One of the specified variables does not exist']) ;
|
||||||
else
|
else
|
||||||
ivar(i) = i_tmp;
|
ivar(i) = i_tmp;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
for i = 1:n
|
for i = 1:n
|
||||||
fprintf(fid,[M_.endo_names(ivar(i),:), '=['],'\n') ;
|
fprintf(fid,[M_.endo_names(ivar(i),:), '=['],'\n') ;
|
||||||
fprintf(fid,'\n') ;
|
fprintf(fid,'\n') ;
|
||||||
fprintf(fid,'%15.8g\n',oo_.endo_simul(ivar(i),:)') ;
|
fprintf(fid,'%15.8g\n',oo_.endo_simul(ivar(i),:)') ;
|
||||||
fprintf(fid,'\n') ;
|
fprintf(fid,'\n') ;
|
||||||
fprintf(fid,'];\n') ;
|
fprintf(fid,'];\n') ;
|
||||||
fprintf(fid,'\n') ;
|
fprintf(fid,'\n') ;
|
||||||
end
|
end
|
||||||
fclose(fid) ;
|
fclose(fid) ;
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -24,27 +24,27 @@ ftest(s1,0) ;
|
||||||
i = [lag1(1):size(x,2)-lag1(2)+1]' ;
|
i = [lag1(1):size(x,2)-lag1(2)+1]' ;
|
||||||
|
|
||||||
if size(options_.smpl,1) == 1
|
if size(options_.smpl,1) == 1
|
||||||
error(['DSAMPLE not specified.']) ;
|
error(['DSAMPLE not specified.']) ;
|
||||||
end
|
end
|
||||||
|
|
||||||
if options_.smpl(3) > 0
|
if options_.smpl(3) > 0
|
||||||
if options_.smpl(3) == 2
|
if options_.smpl(3) == 2
|
||||||
if options_.smpl(1)<0 | options_.smpl(2)>size(x,2)-lag1(2)
|
if options_.smpl(1)<0 | options_.smpl(2)>size(x,2)-lag1(2)
|
||||||
error ('Wrong sample.') ;
|
error ('Wrong sample.') ;
|
||||||
end
|
end
|
||||||
i = [options_.smpl(1)+lag1(1):options_.smpl(2)+lag1(1)]' ;
|
i = [options_.smpl(1)+lag1(1):options_.smpl(2)+lag1(1)]' ;
|
||||||
elseif options_.smpl(3) == 1
|
elseif options_.smpl(3) == 1
|
||||||
if options_.smpl(1)>size(x,2)-lag1(2)
|
if options_.smpl(1)>size(x,2)-lag1(2)
|
||||||
error ('Wrong sample.') ;
|
error ('Wrong sample.') ;
|
||||||
end
|
end
|
||||||
i = [lag1(1):options_.smpl(1)+lag1(1)]' ;
|
i = [lag1(1):options_.smpl(1)+lag1(1)]' ;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
||||||
j = bseastr(nvx,nvy) ;
|
j = bseastr(nvx,nvy) ;
|
||||||
|
|
||||||
if stop
|
if stop
|
||||||
return ;
|
return ;
|
||||||
end
|
end
|
||||||
|
|
||||||
z = mean(mean(abs(x(j,i)-y(j,i)))) ;
|
z = mean(mean(abs(x(j,i)-y(j,i)))) ;
|
||||||
|
|
|
||||||
|
|
@ -25,7 +25,7 @@ function d = diag_vech(Vector)
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
m = length(Vector);
|
m = length(Vector);
|
||||||
n = (sqrt(1+8*m)-1)/2;
|
n = (sqrt(1+8*m)-1)/2;
|
||||||
k = cumsum(1:n);
|
k = cumsum(1:n);
|
||||||
d = Vector(k);
|
d = Vector(k);
|
||||||
|
|
|
||||||
312
matlab/disp_dr.m
312
matlab/disp_dr.m
|
|
@ -6,7 +6,7 @@ function disp_dr(dr,order,var_list)
|
||||||
% order [int]: order of approximation
|
% order [int]: order of approximation
|
||||||
% var_list [char array]: list of endogenous variables for which the
|
% var_list [char array]: list of endogenous variables for which the
|
||||||
% decision rules should be printed
|
% decision rules should be printed
|
||||||
|
|
||||||
% Copyright (C) 2001-2009 Dynare Team
|
% Copyright (C) 2001-2009 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
|
|
@ -24,190 +24,190 @@ function disp_dr(dr,order,var_list)
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
global M_
|
global M_
|
||||||
|
|
||||||
nx =size(dr.ghx,2);
|
|
||||||
nu =size(dr.ghu,2);
|
|
||||||
k = find(dr.kstate(:,2) <= M_.maximum_lag+1);
|
|
||||||
klag = dr.kstate(k,[1 2]);
|
|
||||||
|
|
||||||
k1 = dr.order_var;
|
|
||||||
|
|
||||||
if size(var_list,1) == 0
|
|
||||||
var_list = M_.endo_names(1:M_.orig_endo_nbr, :);
|
|
||||||
end
|
|
||||||
|
|
||||||
nvar = size(var_list,1);
|
nx =size(dr.ghx,2);
|
||||||
|
nu =size(dr.ghu,2);
|
||||||
|
k = find(dr.kstate(:,2) <= M_.maximum_lag+1);
|
||||||
|
klag = dr.kstate(k,[1 2]);
|
||||||
|
|
||||||
ivar=zeros(nvar,1);
|
k1 = dr.order_var;
|
||||||
for i=1:nvar
|
|
||||||
i_tmp = strmatch(var_list(i,:),M_.endo_names(k1,:),'exact');
|
if size(var_list,1) == 0
|
||||||
if isempty(i_tmp)
|
var_list = M_.endo_names(1:M_.orig_endo_nbr, :);
|
||||||
|
end
|
||||||
|
|
||||||
|
nvar = size(var_list,1);
|
||||||
|
|
||||||
|
ivar=zeros(nvar,1);
|
||||||
|
for i=1:nvar
|
||||||
|
i_tmp = strmatch(var_list(i,:),M_.endo_names(k1,:),'exact');
|
||||||
|
if isempty(i_tmp)
|
||||||
disp(var_list(i,:));
|
disp(var_list(i,:));
|
||||||
error (['One of the variable specified does not exist']) ;
|
error (['One of the variable specified does not exist']) ;
|
||||||
else
|
else
|
||||||
ivar(i) = i_tmp;
|
ivar(i) = i_tmp;
|
||||||
end
|
|
||||||
end
|
end
|
||||||
|
end
|
||||||
|
|
||||||
disp('POLICY AND TRANSITION FUNCTIONS')
|
disp('POLICY AND TRANSITION FUNCTIONS')
|
||||||
% variable names
|
% variable names
|
||||||
str = ' ';
|
str = ' ';
|
||||||
for i=1:nvar
|
for i=1:nvar
|
||||||
str = [str sprintf('%16s',M_.endo_names(k1(ivar(i)),:))];
|
str = [str sprintf('%16s',M_.endo_names(k1(ivar(i)),:))];
|
||||||
end
|
end
|
||||||
disp(str);
|
disp(str);
|
||||||
%
|
%
|
||||||
% constant
|
% constant
|
||||||
%
|
%
|
||||||
str = 'Constant ';
|
str = 'Constant ';
|
||||||
flag = 0;
|
flag = 0;
|
||||||
for i=1:nvar
|
for i=1:nvar
|
||||||
x = dr.ys(k1(ivar(i)));
|
x = dr.ys(k1(ivar(i)));
|
||||||
if order > 1
|
if order > 1
|
||||||
x = x + dr.ghs2(ivar(i))/2;
|
x = x + dr.ghs2(ivar(i))/2;
|
||||||
end
|
end
|
||||||
if abs(x) > 1e-6
|
if abs(x) > 1e-6
|
||||||
flag = 1;
|
flag = 1;
|
||||||
str = [str sprintf('%16.6f',x)];
|
str = [str sprintf('%16.6f',x)];
|
||||||
else
|
else
|
||||||
str = [str ' 0'];
|
str = [str ' 0'];
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if flag
|
if flag
|
||||||
disp(str)
|
disp(str)
|
||||||
end
|
end
|
||||||
if order > 1
|
if order > 1
|
||||||
str = '(correction) ';
|
str = '(correction) ';
|
||||||
flag = 0;
|
flag = 0;
|
||||||
for i=1:nvar
|
for i=1:nvar
|
||||||
x = dr.ghs2(ivar(i))/2;
|
x = dr.ghs2(ivar(i))/2;
|
||||||
if abs(x) > 1e-6
|
if abs(x) > 1e-6
|
||||||
flag = 1;
|
flag = 1;
|
||||||
str = [str sprintf('%16.6f',x)];
|
str = [str sprintf('%16.6f',x)];
|
||||||
else
|
else
|
||||||
str = [str ' 0'];
|
str = [str ' 0'];
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if flag
|
if flag
|
||||||
disp(str)
|
disp(str)
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
%
|
%
|
||||||
% ghx
|
% ghx
|
||||||
%
|
%
|
||||||
for k=1:nx
|
for k=1:nx
|
||||||
flag = 0;
|
flag = 0;
|
||||||
str1 = subst_auxvar(k1(klag(k,1)),klag(k,2)-M_.maximum_lag-2);
|
str1 = subst_auxvar(k1(klag(k,1)),klag(k,2)-M_.maximum_lag-2);
|
||||||
str = sprintf('%-20s',str1);
|
str = sprintf('%-20s',str1);
|
||||||
for i=1:nvar
|
for i=1:nvar
|
||||||
x = dr.ghx(ivar(i),k);
|
x = dr.ghx(ivar(i),k);
|
||||||
if abs(x) > 1e-6
|
if abs(x) > 1e-6
|
||||||
flag = 1;
|
flag = 1;
|
||||||
str = [str sprintf('%16.6f',x)];
|
str = [str sprintf('%16.6f',x)];
|
||||||
else
|
else
|
||||||
str = [str ' 0'];
|
str = [str ' 0'];
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if flag
|
if flag
|
||||||
disp(str)
|
disp(str)
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
%
|
%
|
||||||
% ghu
|
% ghu
|
||||||
%
|
%
|
||||||
for k=1:nu
|
for k=1:nu
|
||||||
flag = 0;
|
flag = 0;
|
||||||
str = sprintf('%-20s',M_.exo_names(k,:));
|
str = sprintf('%-20s',M_.exo_names(k,:));
|
||||||
for i=1:nvar
|
for i=1:nvar
|
||||||
x = dr.ghu(ivar(i),k);
|
x = dr.ghu(ivar(i),k);
|
||||||
if abs(x) > 1e-6
|
if abs(x) > 1e-6
|
||||||
flag = 1;
|
flag = 1;
|
||||||
str = [str sprintf('%16.6f',x)];
|
str = [str sprintf('%16.6f',x)];
|
||||||
else
|
else
|
||||||
str = [str ' 0'];
|
str = [str ' 0'];
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if flag
|
if flag
|
||||||
disp(str)
|
disp(str)
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
||||||
if order > 1
|
if order > 1
|
||||||
% ghxx
|
% ghxx
|
||||||
for k = 1:nx
|
for k = 1:nx
|
||||||
for j = 1:k
|
for j = 1:k
|
||||||
flag = 0;
|
flag = 0;
|
||||||
str1 = sprintf('%s,%s',subst_auxvar(k1(klag(k,1)),klag(k,2)-M_.maximum_lag-2), ...
|
str1 = sprintf('%s,%s',subst_auxvar(k1(klag(k,1)),klag(k,2)-M_.maximum_lag-2), ...
|
||||||
subst_auxvar(k1(klag(j,1)),klag(j,2)-M_.maximum_lag-2));
|
subst_auxvar(k1(klag(j,1)),klag(j,2)-M_.maximum_lag-2));
|
||||||
str = sprintf('%-20s',str1);
|
str = sprintf('%-20s',str1);
|
||||||
for i=1:nvar
|
for i=1:nvar
|
||||||
if k == j
|
if k == j
|
||||||
x = dr.ghxx(ivar(i),(k-1)*nx+j)/2;
|
x = dr.ghxx(ivar(i),(k-1)*nx+j)/2;
|
||||||
else
|
else
|
||||||
x = dr.ghxx(ivar(i),(k-1)*nx+j);
|
x = dr.ghxx(ivar(i),(k-1)*nx+j);
|
||||||
end
|
end
|
||||||
if abs(x) > 1e-6
|
if abs(x) > 1e-6
|
||||||
flag = 1;
|
flag = 1;
|
||||||
str = [str sprintf('%16.6f',x)];
|
str = [str sprintf('%16.6f',x)];
|
||||||
else
|
else
|
||||||
str = [str ' 0'];
|
str = [str ' 0'];
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if flag
|
if flag
|
||||||
disp(str)
|
disp(str)
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
%
|
%
|
||||||
% ghuu
|
% ghuu
|
||||||
%
|
%
|
||||||
for k = 1:nu
|
for k = 1:nu
|
||||||
for j = 1:k
|
for j = 1:k
|
||||||
flag = 0;
|
flag = 0;
|
||||||
str = sprintf('%-20s',[M_.exo_names(k,:) ',' M_.exo_names(j,:)] );
|
str = sprintf('%-20s',[M_.exo_names(k,:) ',' M_.exo_names(j,:)] );
|
||||||
for i=1:nvar
|
for i=1:nvar
|
||||||
if k == j
|
if k == j
|
||||||
x = dr.ghuu(ivar(i),(k-1)*nu+j)/2;
|
x = dr.ghuu(ivar(i),(k-1)*nu+j)/2;
|
||||||
else
|
else
|
||||||
x = dr.ghuu(ivar(i),(k-1)*nu+j);
|
x = dr.ghuu(ivar(i),(k-1)*nu+j);
|
||||||
end
|
end
|
||||||
if abs(x) > 1e-6
|
if abs(x) > 1e-6
|
||||||
flag = 1;
|
flag = 1;
|
||||||
str = [str sprintf('%16.6f',x)];
|
str = [str sprintf('%16.6f',x)];
|
||||||
else
|
else
|
||||||
str = [str ' 0'];
|
str = [str ' 0'];
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if flag
|
if flag
|
||||||
disp(str)
|
disp(str)
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
%
|
%
|
||||||
% ghxu
|
% ghxu
|
||||||
%
|
%
|
||||||
for k = 1:nx
|
for k = 1:nx
|
||||||
for j = 1:nu
|
for j = 1:nu
|
||||||
flag = 0;
|
flag = 0;
|
||||||
str1 = sprintf('%s,%s',subst_auxvar(k1(klag(k,1)),klag(k,2)-M_.maximum_lag-2), ...
|
str1 = sprintf('%s,%s',subst_auxvar(k1(klag(k,1)),klag(k,2)-M_.maximum_lag-2), ...
|
||||||
M_.exo_names(j,:));
|
M_.exo_names(j,:));
|
||||||
str = sprintf('%-20s',str1);
|
str = sprintf('%-20s',str1);
|
||||||
for i=1:nvar
|
for i=1:nvar
|
||||||
x = dr.ghxu(ivar(i),(k-1)*nu+j);
|
x = dr.ghxu(ivar(i),(k-1)*nu+j);
|
||||||
if abs(x) > 1e-6
|
if abs(x) > 1e-6
|
||||||
flag = 1;
|
flag = 1;
|
||||||
str = [str sprintf('%16.6f',x)];
|
str = [str sprintf('%16.6f',x)];
|
||||||
else
|
else
|
||||||
str = [str ' 0'];
|
str = [str ' 0'];
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if flag
|
if flag
|
||||||
disp(str)
|
disp(str)
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
||||||
% Given the index of an endogenous (possibly an auxiliary var), and a
|
% Given the index of an endogenous (possibly an auxiliary var), and a
|
||||||
|
|
@ -215,25 +215,25 @@ end
|
||||||
% In the case of auxiliary vars for lags, replace by the original variable
|
% In the case of auxiliary vars for lags, replace by the original variable
|
||||||
% name, and compute the lead/lag accordingly.
|
% name, and compute the lead/lag accordingly.
|
||||||
function str = subst_auxvar(aux_index, aux_lead_lag)
|
function str = subst_auxvar(aux_index, aux_lead_lag)
|
||||||
global M_
|
global M_
|
||||||
|
|
||||||
if aux_index <= M_.orig_endo_nbr
|
if aux_index <= M_.orig_endo_nbr
|
||||||
str = sprintf('%s(%d)', deblank(M_.endo_names(aux_index,:)), aux_lead_lag);
|
str = sprintf('%s(%d)', deblank(M_.endo_names(aux_index,:)), aux_lead_lag);
|
||||||
|
return
|
||||||
|
end
|
||||||
|
for i = 1:length(M_.aux_vars)
|
||||||
|
if M_.aux_vars(i).endo_index == aux_index
|
||||||
|
switch M_.aux_vars(i).type
|
||||||
|
case 1
|
||||||
|
orig_name = deblank(M_.endo_names(M_.aux_vars(i).orig_index, :));
|
||||||
|
case 3
|
||||||
|
orig_name = deblank(M_.exo_names(M_.aux_vars(i).orig_index, :));
|
||||||
|
otherwise
|
||||||
|
error(sprintf('Invalid auxiliary type: %s', M_.endo_names(aux_index, :)))
|
||||||
|
end
|
||||||
|
str = sprintf('%s(%d)', orig_name, M_.aux_vars(i).orig_lead_lag+aux_lead_lag);
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
for i = 1:length(M_.aux_vars)
|
end
|
||||||
if M_.aux_vars(i).endo_index == aux_index
|
error(sprintf('Could not find aux var: %s', M_.endo_names(aux_index, :)))
|
||||||
switch M_.aux_vars(i).type
|
|
||||||
case 1
|
|
||||||
orig_name = deblank(M_.endo_names(M_.aux_vars(i).orig_index, :));
|
|
||||||
case 3
|
|
||||||
orig_name = deblank(M_.exo_names(M_.aux_vars(i).orig_index, :));
|
|
||||||
otherwise
|
|
||||||
error(sprintf('Invalid auxiliary type: %s', M_.endo_names(aux_index, :)))
|
|
||||||
end
|
|
||||||
str = sprintf('%s(%d)', orig_name, M_.aux_vars(i).orig_lead_lag+aux_lead_lag);
|
|
||||||
return
|
|
||||||
end
|
|
||||||
end
|
|
||||||
error(sprintf('Could not find aux var: %s', M_.endo_names(aux_index, :)))
|
|
||||||
end
|
end
|
||||||
|
|
|
||||||
|
|
@ -25,209 +25,209 @@ function disp_dr_sparse(dr,order,var_list)
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
global M_
|
global M_
|
||||||
nx = 0;
|
nx = 0;
|
||||||
nu = 0;
|
nu = 0;
|
||||||
k = [];
|
k = [];
|
||||||
klag = [];
|
klag = [];
|
||||||
k1 = [];
|
k1 = [];
|
||||||
nspred = 0;
|
nspred = 0;
|
||||||
for i=1:length(M_.block_structure.block)
|
for i=1:length(M_.block_structure.block)
|
||||||
nspred = nspred + M_.block_structure.block(i).dr.nspred;
|
nspred = nspred + M_.block_structure.block(i).dr.nspred;
|
||||||
end;
|
end;
|
||||||
ghu = zeros(M_.endo_nbr, M_.exo_nbr*(M_.maximum_exo_lag+M_.maximum_exo_lead+1));
|
ghu = zeros(M_.endo_nbr, M_.exo_nbr*(M_.maximum_exo_lag+M_.maximum_exo_lead+1));
|
||||||
ghx = zeros(M_.endo_nbr, nspred);
|
ghx = zeros(M_.endo_nbr, nspred);
|
||||||
for i=1:length(M_.block_structure.block)
|
for i=1:length(M_.block_structure.block)
|
||||||
nx = nx + size(M_.block_structure.block(i).dr.ghx,2);
|
nx = nx + size(M_.block_structure.block(i).dr.ghx,2);
|
||||||
% M_.block_structure.block(i).dr.ghx
|
% M_.block_structure.block(i).dr.ghx
|
||||||
% M_.block_structure.block(i).equation
|
% M_.block_structure.block(i).equation
|
||||||
% M_.block_structure.block(i).variable
|
% M_.block_structure.block(i).variable
|
||||||
ghx(M_.block_structure.block(i).equation, M_.block_structure.block(i).variable(find(M_.block_structure.block(i).lead_lag_incidence(1: M_.block_structure.block(i).maximum_endo_lag,:))) ) = M_.block_structure.block(i).dr.ghx;
|
ghx(M_.block_structure.block(i).equation, M_.block_structure.block(i).variable(find(M_.block_structure.block(i).lead_lag_incidence(1: M_.block_structure.block(i).maximum_endo_lag,:))) ) = M_.block_structure.block(i).dr.ghx;
|
||||||
if(M_.block_structure.block(i).exo_nbr)
|
if(M_.block_structure.block(i).exo_nbr)
|
||||||
nu = nu + size(M_.block_structure.block(i).dr.ghu,2);
|
nu = nu + size(M_.block_structure.block(i).dr.ghu,2);
|
||||||
ghu(M_.block_structure.block(i).equation, M_.block_structure.block(i).exogenous) = M_.block_structure.block(i).dr.ghu;
|
ghu(M_.block_structure.block(i).equation, M_.block_structure.block(i).exogenous) = M_.block_structure.block(i).dr.ghu;
|
||||||
end
|
end
|
||||||
k_tmp = find(M_.block_structure.block(i).dr.kstate(:,2) <= M_.block_structure.block(i).maximum_lag+1);
|
k_tmp = find(M_.block_structure.block(i).dr.kstate(:,2) <= M_.block_structure.block(i).maximum_lag+1);
|
||||||
k = [k ; k_tmp];
|
k = [k ; k_tmp];
|
||||||
klag = [klag ; M_.block_structure.block(i).dr.kstate(k_tmp,[1 2])];
|
klag = [klag ; M_.block_structure.block(i).dr.kstate(k_tmp,[1 2])];
|
||||||
k1 = [k1 ; M_.block_structure.block(i).variable(M_.block_structure.block(i).dr.order_var)'];
|
k1 = [k1 ; M_.block_structure.block(i).variable(M_.block_structure.block(i).dr.order_var)'];
|
||||||
end
|
end
|
||||||
|
|
||||||
if size(var_list,1) == 0
|
if size(var_list,1) == 0
|
||||||
var_list = M_.endo_names(1:M_.orig_endo_nbr, :);
|
var_list = M_.endo_names(1:M_.orig_endo_nbr, :);
|
||||||
end
|
end
|
||||||
|
|
||||||
nvar = size(var_list,1);
|
nvar = size(var_list,1);
|
||||||
|
|
||||||
ivar=zeros(nvar,1);
|
ivar=zeros(nvar,1);
|
||||||
for i=1:nvar
|
for i=1:nvar
|
||||||
i_tmp = strmatch(var_list(i,:),M_.endo_names(k1,:),'exact');
|
i_tmp = strmatch(var_list(i,:),M_.endo_names(k1,:),'exact');
|
||||||
if isempty(i_tmp)
|
if isempty(i_tmp)
|
||||||
disp(var_list(i,:));
|
disp(var_list(i,:));
|
||||||
error (['One of the variable specified does not exist']) ;
|
error (['One of the variable specified does not exist']) ;
|
||||||
else
|
else
|
||||||
ivar(i) = i_tmp;
|
ivar(i) = i_tmp;
|
||||||
end
|
|
||||||
end
|
end
|
||||||
|
end
|
||||||
|
|
||||||
disp('POLICY AND TRANSITION FUNCTIONS')
|
disp('POLICY AND TRANSITION FUNCTIONS')
|
||||||
% variable names
|
% variable names
|
||||||
str = ' ';
|
str = ' ';
|
||||||
for i=1:nvar
|
for i=1:nvar
|
||||||
str = [str sprintf('%16s',M_.endo_names(k1(ivar(i)),:))];
|
str = [str sprintf('%16s',M_.endo_names(k1(ivar(i)),:))];
|
||||||
end
|
end
|
||||||
disp(str);
|
disp(str);
|
||||||
%
|
%
|
||||||
% constant
|
% constant
|
||||||
%
|
%
|
||||||
str = 'Constant ';
|
str = 'Constant ';
|
||||||
flag = 0;
|
flag = 0;
|
||||||
for i=1:nvar
|
for i=1:nvar
|
||||||
x = dr.ys(k1(ivar(i)));
|
x = dr.ys(k1(ivar(i)));
|
||||||
if order > 1
|
if order > 1
|
||||||
x = x + dr.ghs2(ivar(i))/2;
|
x = x + dr.ghs2(ivar(i))/2;
|
||||||
end
|
end
|
||||||
if abs(x) > 1e-6
|
if abs(x) > 1e-6
|
||||||
flag = 1;
|
flag = 1;
|
||||||
str = [str sprintf('%16.6f',x)];
|
str = [str sprintf('%16.6f',x)];
|
||||||
else
|
else
|
||||||
str = [str ' 0'];
|
str = [str ' 0'];
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if flag
|
if flag
|
||||||
disp(str)
|
disp(str)
|
||||||
end
|
end
|
||||||
if order > 1
|
if order > 1
|
||||||
str = '(correction) ';
|
str = '(correction) ';
|
||||||
flag = 0;
|
flag = 0;
|
||||||
for i=1:nvar
|
for i=1:nvar
|
||||||
x = dr.ghs2(ivar(i))/2;
|
x = dr.ghs2(ivar(i))/2;
|
||||||
if abs(x) > 1e-6
|
if abs(x) > 1e-6
|
||||||
flag = 1;
|
flag = 1;
|
||||||
str = [str sprintf('%16.6f',x)];
|
str = [str sprintf('%16.6f',x)];
|
||||||
else
|
else
|
||||||
str = [str ' 0'];
|
str = [str ' 0'];
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if flag
|
if flag
|
||||||
disp(str)
|
disp(str)
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
%
|
%
|
||||||
% ghx
|
% ghx
|
||||||
%
|
%
|
||||||
for k=1:nx
|
for k=1:nx
|
||||||
flag = 0;
|
flag = 0;
|
||||||
str1 = subst_auxvar(k1(klag(k,1)),klag(k,2)-M_.maximum_lag-2);
|
str1 = subst_auxvar(k1(klag(k,1)),klag(k,2)-M_.maximum_lag-2);
|
||||||
str = sprintf('%-20s',str1);
|
str = sprintf('%-20s',str1);
|
||||||
for i=1:nvar
|
for i=1:nvar
|
||||||
x = ghx(ivar(i),k);
|
x = ghx(ivar(i),k);
|
||||||
if abs(x) > 1e-6
|
if abs(x) > 1e-6
|
||||||
flag = 1;
|
flag = 1;
|
||||||
str = [str sprintf('%16.6f',x)];
|
str = [str sprintf('%16.6f',x)];
|
||||||
else
|
else
|
||||||
str = [str ' 0'];
|
str = [str ' 0'];
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if flag
|
if flag
|
||||||
disp(str)
|
disp(str)
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
%
|
%
|
||||||
% ghu
|
% ghu
|
||||||
%
|
%
|
||||||
for k=1:nu
|
for k=1:nu
|
||||||
flag = 0;
|
flag = 0;
|
||||||
str = sprintf('%-20s',M_.exo_names(k,:));
|
str = sprintf('%-20s',M_.exo_names(k,:));
|
||||||
for i=1:nvar
|
for i=1:nvar
|
||||||
x = ghu(ivar(i),k);
|
x = ghu(ivar(i),k);
|
||||||
if abs(x) > 1e-6
|
if abs(x) > 1e-6
|
||||||
flag = 1;
|
flag = 1;
|
||||||
str = [str sprintf('%16.6f',x)];
|
str = [str sprintf('%16.6f',x)];
|
||||||
else
|
else
|
||||||
str = [str ' 0'];
|
str = [str ' 0'];
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if flag
|
if flag
|
||||||
disp(str)
|
disp(str)
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
||||||
if order > 1
|
if order > 1
|
||||||
% ghxx
|
% ghxx
|
||||||
for k = 1:nx
|
for k = 1:nx
|
||||||
for j = 1:k
|
for j = 1:k
|
||||||
flag = 0;
|
flag = 0;
|
||||||
str1 = sprintf('%s,%s',subst_auxvar(k1(klag(k,1)),klag(k,2)-M_.maximum_lag-2), ...
|
str1 = sprintf('%s,%s',subst_auxvar(k1(klag(k,1)),klag(k,2)-M_.maximum_lag-2), ...
|
||||||
subst_auxvar(k1(klag(j,1)),klag(j,2)-M_.maximum_lag-2));
|
subst_auxvar(k1(klag(j,1)),klag(j,2)-M_.maximum_lag-2));
|
||||||
str = sprintf('%-20s',str1);
|
str = sprintf('%-20s',str1);
|
||||||
for i=1:nvar
|
for i=1:nvar
|
||||||
if k == j
|
if k == j
|
||||||
x = dr.ghxx(ivar(i),(k-1)*nx+j)/2;
|
x = dr.ghxx(ivar(i),(k-1)*nx+j)/2;
|
||||||
else
|
else
|
||||||
x = dr.ghxx(ivar(i),(k-1)*nx+j);
|
x = dr.ghxx(ivar(i),(k-1)*nx+j);
|
||||||
end
|
end
|
||||||
if abs(x) > 1e-6
|
if abs(x) > 1e-6
|
||||||
flag = 1;
|
flag = 1;
|
||||||
str = [str sprintf('%16.6f',x)];
|
str = [str sprintf('%16.6f',x)];
|
||||||
else
|
else
|
||||||
str = [str ' 0'];
|
str = [str ' 0'];
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if flag
|
if flag
|
||||||
disp(str)
|
disp(str)
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
%
|
%
|
||||||
% ghuu
|
% ghuu
|
||||||
%
|
%
|
||||||
for k = 1:nu
|
for k = 1:nu
|
||||||
for j = 1:k
|
for j = 1:k
|
||||||
flag = 0;
|
flag = 0;
|
||||||
str = sprintf('%-20s',[M_.exo_names(k,:) ',' M_.exo_names(j,:)] );
|
str = sprintf('%-20s',[M_.exo_names(k,:) ',' M_.exo_names(j,:)] );
|
||||||
for i=1:nvar
|
for i=1:nvar
|
||||||
if k == j
|
if k == j
|
||||||
x = dr.ghuu(ivar(i),(k-1)*nu+j)/2;
|
x = dr.ghuu(ivar(i),(k-1)*nu+j)/2;
|
||||||
else
|
else
|
||||||
x = dr.ghuu(ivar(i),(k-1)*nu+j);
|
x = dr.ghuu(ivar(i),(k-1)*nu+j);
|
||||||
end
|
end
|
||||||
if abs(x) > 1e-6
|
if abs(x) > 1e-6
|
||||||
flag = 1;
|
flag = 1;
|
||||||
str = [str sprintf('%16.6f',x)];
|
str = [str sprintf('%16.6f',x)];
|
||||||
else
|
else
|
||||||
str = [str ' 0'];
|
str = [str ' 0'];
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if flag
|
if flag
|
||||||
disp(str)
|
disp(str)
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
%
|
%
|
||||||
% ghxu
|
% ghxu
|
||||||
%
|
%
|
||||||
for k = 1:nx
|
for k = 1:nx
|
||||||
for j = 1:nu
|
for j = 1:nu
|
||||||
flag = 0;
|
flag = 0;
|
||||||
str1 = sprintf('%s,%s',subst_auxvar(k1(klag(k,1)),klag(k,2)-M_.maximum_lag-2), ...
|
str1 = sprintf('%s,%s',subst_auxvar(k1(klag(k,1)),klag(k,2)-M_.maximum_lag-2), ...
|
||||||
M_.exo_names(j,:));
|
M_.exo_names(j,:));
|
||||||
str = sprintf('%-20s',str1);
|
str = sprintf('%-20s',str1);
|
||||||
for i=1:nvar
|
for i=1:nvar
|
||||||
x = dr.ghxu(ivar(i),(k-1)*nu+j);
|
x = dr.ghxu(ivar(i),(k-1)*nu+j);
|
||||||
if abs(x) > 1e-6
|
if abs(x) > 1e-6
|
||||||
flag = 1;
|
flag = 1;
|
||||||
str = [str sprintf('%16.6f',x)];
|
str = [str sprintf('%16.6f',x)];
|
||||||
else
|
else
|
||||||
str = [str ' 0'];
|
str = [str ' 0'];
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if flag
|
if flag
|
||||||
disp(str)
|
disp(str)
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
||||||
% Given the index of an endogenous (possibly an auxiliary var), and a
|
% Given the index of an endogenous (possibly an auxiliary var), and a
|
||||||
|
|
@ -235,25 +235,25 @@ end
|
||||||
% In the case of auxiliary vars for lags, replace by the original variable
|
% In the case of auxiliary vars for lags, replace by the original variable
|
||||||
% name, and compute the lead/lag accordingly.
|
% name, and compute the lead/lag accordingly.
|
||||||
function str = subst_auxvar(aux_index, aux_lead_lag)
|
function str = subst_auxvar(aux_index, aux_lead_lag)
|
||||||
global M_
|
global M_
|
||||||
|
|
||||||
if aux_index <= M_.orig_endo_nbr
|
if aux_index <= M_.orig_endo_nbr
|
||||||
str = sprintf('%s(%d)', deblank(M_.endo_names(aux_index,:)), aux_lead_lag);
|
str = sprintf('%s(%d)', deblank(M_.endo_names(aux_index,:)), aux_lead_lag);
|
||||||
|
return
|
||||||
|
end
|
||||||
|
for i = 1:length(M_.aux_vars)
|
||||||
|
if M_.aux_vars(i).endo_index == aux_index
|
||||||
|
switch M_.aux_vars(i).type
|
||||||
|
case 1
|
||||||
|
orig_name = deblank(M_.endo_names(M_.aux_vars(i).orig_index, :));
|
||||||
|
case 3
|
||||||
|
orig_name = deblank(M_.exo_names(M_.aux_vars(i).orig_index, :));
|
||||||
|
otherwise
|
||||||
|
error(sprintf('Invalid auxiliary type: %s', M_.endo_names(aux_index, :)))
|
||||||
|
end
|
||||||
|
str = sprintf('%s(%d)', orig_name, M_.aux_vars(i).orig_lead_lag+aux_lead_lag);
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
for i = 1:length(M_.aux_vars)
|
end
|
||||||
if M_.aux_vars(i).endo_index == aux_index
|
error(sprintf('Could not find aux var: %s', M_.endo_names(aux_index, :)))
|
||||||
switch M_.aux_vars(i).type
|
|
||||||
case 1
|
|
||||||
orig_name = deblank(M_.endo_names(M_.aux_vars(i).orig_index, :));
|
|
||||||
case 3
|
|
||||||
orig_name = deblank(M_.exo_names(M_.aux_vars(i).orig_index, :));
|
|
||||||
otherwise
|
|
||||||
error(sprintf('Invalid auxiliary type: %s', M_.endo_names(aux_index, :)))
|
|
||||||
end
|
|
||||||
str = sprintf('%s(%d)', orig_name, M_.aux_vars(i).orig_lead_lag+aux_lead_lag);
|
|
||||||
return
|
|
||||||
end
|
|
||||||
end
|
|
||||||
error(sprintf('Could not find aux var: %s', M_.endo_names(aux_index, :)))
|
|
||||||
end
|
end
|
||||||
|
|
|
||||||
|
|
@ -1,107 +1,107 @@
|
||||||
function disp_identification(pdraws, idemodel, idemoments, disp_pcorr)
|
function disp_identification(pdraws, idemodel, idemoments, disp_pcorr)
|
||||||
|
|
||||||
% Copyright (C) 2008 Dynare Team
|
% Copyright (C) 2008 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
% Dynare is free software: you can redistribute it and/or modify
|
||||||
% it under the terms of the GNU General Public License as published by
|
% it under the terms of the GNU General Public License as published by
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
% the Free Software Foundation, either version 3 of the License, or
|
||||||
% (at your option) any later version.
|
% (at your option) any later version.
|
||||||
%
|
%
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
% Dynare is distributed in the hope that it will be useful,
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
global bayestopt_
|
global bayestopt_
|
||||||
|
|
||||||
if nargin<4 | isempty(disp_pcorr),
|
if nargin<4 | isempty(disp_pcorr),
|
||||||
disp_pcorr=0;
|
disp_pcorr=0;
|
||||||
end
|
end
|
||||||
|
|
||||||
[SampleSize, npar] = size(pdraws);
|
[SampleSize, npar] = size(pdraws);
|
||||||
jok = 0;
|
jok = 0;
|
||||||
jokP = 0;
|
jokP = 0;
|
||||||
jokJ = 0;
|
jokJ = 0;
|
||||||
jokPJ = 0;
|
jokPJ = 0;
|
||||||
if ~any(any(idemodel.ind==0))
|
if ~any(any(idemodel.ind==0))
|
||||||
disp(['All parameters are identified in the model in the MC sample (rank of H).' ]),
|
disp(['All parameters are identified in the model in the MC sample (rank of H).' ]),
|
||||||
disp(' ')
|
disp(' ')
|
||||||
end
|
end
|
||||||
if ~any(any(idemoments.ind==0))
|
if ~any(any(idemoments.ind==0))
|
||||||
disp(['All parameters are identified by J moments in the MC sample (rank of J)' ]),
|
disp(['All parameters are identified by J moments in the MC sample (rank of J)' ]),
|
||||||
end
|
end
|
||||||
for j=1:npar,
|
for j=1:npar,
|
||||||
if any(idemodel.ind(j,:)==0),
|
if any(idemodel.ind(j,:)==0),
|
||||||
pno = 100*length(find(idemodel.ind(j,:)==0))/SampleSize;
|
pno = 100*length(find(idemodel.ind(j,:)==0))/SampleSize;
|
||||||
disp(['Parameter ',bayestopt_.name{j},' is not identified in the model for ',num2str(pno),'% of MC runs!' ])
|
disp(['Parameter ',bayestopt_.name{j},' is not identified in the model for ',num2str(pno),'% of MC runs!' ])
|
||||||
disp(' ')
|
disp(' ')
|
||||||
end
|
end
|
||||||
if any(idemoments.ind(j,:)==0),
|
if any(idemoments.ind(j,:)==0),
|
||||||
pno = 100*length(find(idemoments.ind(j,:)==0))/SampleSize;
|
pno = 100*length(find(idemoments.ind(j,:)==0))/SampleSize;
|
||||||
disp(['Parameter ',bayestopt_.name{j},' is not identified by J moments for ',num2str(pno),'% of MC runs!' ])
|
disp(['Parameter ',bayestopt_.name{j},' is not identified by J moments for ',num2str(pno),'% of MC runs!' ])
|
||||||
disp(' ')
|
disp(' ')
|
||||||
end
|
end
|
||||||
if any(idemodel.ind(j,:)==1),
|
if any(idemodel.ind(j,:)==1),
|
||||||
iok = find(idemodel.ind(j,:)==1);
|
iok = find(idemodel.ind(j,:)==1);
|
||||||
jok = jok+1;
|
jok = jok+1;
|
||||||
kok(jok) = j;
|
kok(jok) = j;
|
||||||
mmin(jok,1) = min(idemodel.Mco(j,iok));
|
mmin(jok,1) = min(idemodel.Mco(j,iok));
|
||||||
mmean(jok,1) = mean(idemodel.Mco(j,iok));
|
mmean(jok,1) = mean(idemodel.Mco(j,iok));
|
||||||
mmax(jok,1) = max(idemodel.Mco(j,iok));
|
mmax(jok,1) = max(idemodel.Mco(j,iok));
|
||||||
[ipmax, jpmax] = find(abs(squeeze(idemodel.Pco(j,[1:j-1,j+1:end],iok)))>0.95);
|
[ipmax, jpmax] = find(abs(squeeze(idemodel.Pco(j,[1:j-1,j+1:end],iok)))>0.95);
|
||||||
if ~isempty(ipmax)
|
if ~isempty(ipmax)
|
||||||
jokP = jokP+1;
|
jokP = jokP+1;
|
||||||
kokP(jokP) = j;
|
kokP(jokP) = j;
|
||||||
ipmax(find(ipmax>=j))=ipmax(find(ipmax>=j))+1;
|
ipmax(find(ipmax>=j))=ipmax(find(ipmax>=j))+1;
|
||||||
[N,X]=hist(ipmax,[1:npar]);
|
[N,X]=hist(ipmax,[1:npar]);
|
||||||
jpM(jokP)={find(N)};
|
jpM(jokP)={find(N)};
|
||||||
NPM(jokP)={N(find(N))./SampleSize.*100};
|
NPM(jokP)={N(find(N))./SampleSize.*100};
|
||||||
pmeanM(jokP)={mean(squeeze(idemodel.Pco(j,find(N),iok))')};
|
pmeanM(jokP)={mean(squeeze(idemodel.Pco(j,find(N),iok))')};
|
||||||
pminM(jokP)={min(squeeze(idemodel.Pco(j,find(N),iok))')};
|
pminM(jokP)={min(squeeze(idemodel.Pco(j,find(N),iok))')};
|
||||||
pmaxM(jokP)={max(squeeze(idemodel.Pco(j,find(N),iok))')};
|
pmaxM(jokP)={max(squeeze(idemodel.Pco(j,find(N),iok))')};
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if any(idemoments.ind(j,:)==1),
|
if any(idemoments.ind(j,:)==1),
|
||||||
iok = find(idemoments.ind(j,:)==1);
|
iok = find(idemoments.ind(j,:)==1);
|
||||||
jokJ = jokJ+1;
|
jokJ = jokJ+1;
|
||||||
kokJ(jokJ) = j;
|
kokJ(jokJ) = j;
|
||||||
mminJ(jokJ,1) = min(idemoments.Mco(j,iok));
|
mminJ(jokJ,1) = min(idemoments.Mco(j,iok));
|
||||||
mmeanJ(jokJ,1) = mean(idemoments.Mco(j,iok));
|
mmeanJ(jokJ,1) = mean(idemoments.Mco(j,iok));
|
||||||
mmaxJ(jokJ,1) = max(idemoments.Mco(j,iok));
|
mmaxJ(jokJ,1) = max(idemoments.Mco(j,iok));
|
||||||
[ipmax, jpmax] = find(abs(squeeze(idemoments.Pco(j,[1:j-1,j+1:end],iok)))>0.95);
|
[ipmax, jpmax] = find(abs(squeeze(idemoments.Pco(j,[1:j-1,j+1:end],iok)))>0.95);
|
||||||
if ~isempty(ipmax)
|
if ~isempty(ipmax)
|
||||||
jokPJ = jokPJ+1;
|
jokPJ = jokPJ+1;
|
||||||
kokPJ(jokPJ) = j;
|
kokPJ(jokPJ) = j;
|
||||||
ipmax(find(ipmax>=j))=ipmax(find(ipmax>=j))+1;
|
ipmax(find(ipmax>=j))=ipmax(find(ipmax>=j))+1;
|
||||||
[N,X]=hist(ipmax,[1:npar]);
|
[N,X]=hist(ipmax,[1:npar]);
|
||||||
jpJ(jokPJ)={find(N)};
|
jpJ(jokPJ)={find(N)};
|
||||||
NPJ(jokPJ)={N(find(N))./SampleSize.*100};
|
NPJ(jokPJ)={N(find(N))./SampleSize.*100};
|
||||||
pmeanJ(jokPJ)={mean(squeeze(idemoments.Pco(j,find(N),iok))')};
|
pmeanJ(jokPJ)={mean(squeeze(idemoments.Pco(j,find(N),iok))')};
|
||||||
pminJ(jokPJ)={min(squeeze(idemoments.Pco(j,find(N),iok))')};
|
pminJ(jokPJ)={min(squeeze(idemoments.Pco(j,find(N),iok))')};
|
||||||
pmaxJ(jokPJ)={max(squeeze(idemoments.Pco(j,find(N),iok))')};
|
pmaxJ(jokPJ)={max(squeeze(idemoments.Pco(j,find(N),iok))')};
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
||||||
dyntable('Multi collinearity in the model:',strvcat('param','min','mean','max'), ...
|
dyntable('Multi collinearity in the model:',strvcat('param','min','mean','max'), ...
|
||||||
strvcat(bayestopt_.name(kok)),[mmin, mmean, mmax],10,10,6);
|
strvcat(bayestopt_.name(kok)),[mmin, mmean, mmax],10,10,6);
|
||||||
|
|
||||||
dyntable('Multi collinearity for moments in J:',strvcat('param','min','mean','max'), ...
|
dyntable('Multi collinearity for moments in J:',strvcat('param','min','mean','max'), ...
|
||||||
strvcat(bayestopt_.name(kokJ)),[mminJ, mmeanJ, mmaxJ],10,10,6);
|
strvcat(bayestopt_.name(kokJ)),[mminJ, mmeanJ, mmaxJ],10,10,6);
|
||||||
|
|
||||||
if disp_pcorr,
|
if disp_pcorr,
|
||||||
for j=1:length(kokP),
|
for j=1:length(kokP),
|
||||||
dyntable([bayestopt_.name{kokP(j)},' pairwise correlations in the model'],strvcat(' ','min','mean','max'), ...
|
dyntable([bayestopt_.name{kokP(j)},' pairwise correlations in the model'],strvcat(' ','min','mean','max'), ...
|
||||||
strvcat(bayestopt_.name{jpM{j}}),[pminM{j}' pmeanM{j}' pmaxM{j}'],10,10,3);
|
strvcat(bayestopt_.name{jpM{j}}),[pminM{j}' pmeanM{j}' pmaxM{j}'],10,10,3);
|
||||||
end
|
end
|
||||||
|
|
||||||
for j=1:length(kokPJ),
|
for j=1:length(kokPJ),
|
||||||
dyntable([bayestopt_.name{kokPJ(j)},' pairwise correlations in J moments'],strvcat(' ','min','mean','max'), ...
|
dyntable([bayestopt_.name{kokPJ(j)},' pairwise correlations in J moments'],strvcat(' ','min','mean','max'), ...
|
||||||
strvcat(bayestopt_.name{jpJ{j}}),[pminJ{j}' pmeanJ{j}' pmaxJ{j}'],10,10,3);
|
strvcat(bayestopt_.name{jpJ{j}}),[pminJ{j}' pmeanJ{j}' pmaxJ{j}'],10,10,3);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
|
||||||
|
|
@ -24,19 +24,19 @@ function disp_model_summary(M,dr)
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
disp(' ')
|
disp(' ')
|
||||||
disp('MODEL SUMMARY')
|
disp('MODEL SUMMARY')
|
||||||
disp(' ')
|
disp(' ')
|
||||||
disp([' Number of variables: ' int2str(M.endo_nbr)])
|
disp([' Number of variables: ' int2str(M.endo_nbr)])
|
||||||
disp([' Number of stochastic shocks: ' int2str(M.exo_nbr)])
|
disp([' Number of stochastic shocks: ' int2str(M.exo_nbr)])
|
||||||
disp([' Number of state variables: ' ...
|
disp([' Number of state variables: ' ...
|
||||||
int2str(length(find(dr.kstate(:,2) <= M.maximum_lag+1)))])
|
int2str(length(find(dr.kstate(:,2) <= M.maximum_lag+1)))])
|
||||||
disp([' Number of jumpers: ' ...
|
disp([' Number of jumpers: ' ...
|
||||||
int2str(length(find(dr.kstate(:,2) == M.maximum_lag+2)))])
|
int2str(length(find(dr.kstate(:,2) == M.maximum_lag+2)))])
|
||||||
disp([' Number of static variables: ' int2str(dr.nstatic)])
|
disp([' Number of static variables: ' int2str(dr.nstatic)])
|
||||||
my_title='MATRIX OF COVARIANCE OF EXOGENOUS SHOCKS';
|
my_title='MATRIX OF COVARIANCE OF EXOGENOUS SHOCKS';
|
||||||
labels = deblank(M.exo_names);
|
labels = deblank(M.exo_names);
|
||||||
headers = strvcat('Variables',labels);
|
headers = strvcat('Variables',labels);
|
||||||
lh = size(labels,2)+2;
|
lh = size(labels,2)+2;
|
||||||
dyntable(my_title,headers,labels,M.Sigma_e,lh,10,6);
|
dyntable(my_title,headers,labels,M.Sigma_e,lh,10,6);
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -18,81 +18,81 @@ function disp_moments(y,var_list)
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
global M_ options_ oo_
|
global M_ options_ oo_
|
||||||
|
|
||||||
warning_old_state = warning;
|
|
||||||
warning off
|
|
||||||
|
|
||||||
if options_.hp_filter
|
warning_old_state = warning;
|
||||||
error('STOCH_SIMUL: HP filter is not yet implemented for empirical moments')
|
warning off
|
||||||
end
|
|
||||||
|
if options_.hp_filter
|
||||||
if size(var_list,1) == 0
|
error('STOCH_SIMUL: HP filter is not yet implemented for empirical moments')
|
||||||
var_list = M_.endo_names(1:M_.orig_endo_nbr, :);
|
end
|
||||||
end
|
|
||||||
|
if size(var_list,1) == 0
|
||||||
nvar = size(var_list,1);
|
var_list = M_.endo_names(1:M_.orig_endo_nbr, :);
|
||||||
ivar=zeros(nvar,1);
|
end
|
||||||
for i=1:nvar
|
|
||||||
i_tmp = strmatch(var_list(i,:),M_.endo_names,'exact');
|
nvar = size(var_list,1);
|
||||||
if isempty(i_tmp)
|
ivar=zeros(nvar,1);
|
||||||
|
for i=1:nvar
|
||||||
|
i_tmp = strmatch(var_list(i,:),M_.endo_names,'exact');
|
||||||
|
if isempty(i_tmp)
|
||||||
error (['One of the variable specified does not exist']) ;
|
error (['One of the variable specified does not exist']) ;
|
||||||
else
|
else
|
||||||
ivar(i) = i_tmp;
|
ivar(i) = i_tmp;
|
||||||
end
|
|
||||||
end
|
end
|
||||||
|
end
|
||||||
|
|
||||||
y = y(ivar,options_.drop+M_.maximum_lag+1:end)';
|
y = y(ivar,options_.drop+M_.maximum_lag+1:end)';
|
||||||
|
|
||||||
m = mean(y);
|
|
||||||
y = y - repmat(m,size(y,1),1);
|
|
||||||
s2 = mean(y.*y);
|
|
||||||
s = sqrt(s2);
|
|
||||||
oo_.mean = transpose(m);
|
|
||||||
oo_.var = y'*y/size(y,1);
|
|
||||||
|
|
||||||
labels = deblank(M_.endo_names(ivar,:));
|
m = mean(y);
|
||||||
|
y = y - repmat(m,size(y,1),1);
|
||||||
if options_.nomoments == 0
|
s2 = mean(y.*y);
|
||||||
|
s = sqrt(s2);
|
||||||
|
oo_.mean = transpose(m);
|
||||||
|
oo_.var = y'*y/size(y,1);
|
||||||
|
|
||||||
|
labels = deblank(M_.endo_names(ivar,:));
|
||||||
|
|
||||||
|
if options_.nomoments == 0
|
||||||
z = [ m' s' s2' (mean(y.^3)./s2.^1.5)' (mean(y.^4)./(s2.*s2)-3)' ];
|
z = [ m' s' s2' (mean(y.^3)./s2.^1.5)' (mean(y.^4)./(s2.*s2)-3)' ];
|
||||||
|
|
||||||
title='MOMENTS OF SIMULATED VARIABLES';
|
title='MOMENTS OF SIMULATED VARIABLES';
|
||||||
if options_.hp_filter
|
if options_.hp_filter
|
||||||
title = [title ' (HP filter, lambda = ' ...
|
title = [title ' (HP filter, lambda = ' ...
|
||||||
int2str(options_.hp_filter) ')'];
|
int2str(options_.hp_filter) ')'];
|
||||||
end
|
end
|
||||||
headers=strvcat('VARIABLE','MEAN','STD. DEV.','VARIANCE','SKEWNESS', ...
|
headers=strvcat('VARIABLE','MEAN','STD. DEV.','VARIANCE','SKEWNESS', ...
|
||||||
'KURTOSIS');
|
'KURTOSIS');
|
||||||
dyntable(title,headers,labels,z,size(labels,2)+2,16,6);
|
dyntable(title,headers,labels,z,size(labels,2)+2,16,6);
|
||||||
end
|
end
|
||||||
|
|
||||||
if options_.nocorr == 0
|
if options_.nocorr == 0
|
||||||
corr = (y'*y/size(y,1))./(s'*s);
|
corr = (y'*y/size(y,1))./(s'*s);
|
||||||
title = 'CORRELATION OF SIMULATED VARIABLES';
|
title = 'CORRELATION OF SIMULATED VARIABLES';
|
||||||
if options_.hp_filter
|
if options_.hp_filter
|
||||||
title = [title ' (HP filter, lambda = ' ...
|
title = [title ' (HP filter, lambda = ' ...
|
||||||
int2str(options_.hp_filter) ')'];
|
int2str(options_.hp_filter) ')'];
|
||||||
end
|
end
|
||||||
headers = strvcat('VARIABLE',M_.endo_names(ivar,:));
|
headers = strvcat('VARIABLE',M_.endo_names(ivar,:));
|
||||||
dyntable(title,headers,labels,corr,size(labels,2)+2,8,4);
|
dyntable(title,headers,labels,corr,size(labels,2)+2,8,4);
|
||||||
end
|
end
|
||||||
|
|
||||||
ar = options_.ar;
|
ar = options_.ar;
|
||||||
options_ = set_default_option(options_,'ar',5);
|
options_ = set_default_option(options_,'ar',5);
|
||||||
ar = options_.ar;
|
ar = options_.ar;
|
||||||
if ar > 0
|
if ar > 0
|
||||||
autocorr = [];
|
autocorr = [];
|
||||||
for i=1:ar
|
for i=1:ar
|
||||||
oo_.autocorr{i} = y(ar+1:end,:)'*y(ar+1-i:end-i,:)./((size(y,1)-ar)*s'*s);
|
oo_.autocorr{i} = y(ar+1:end,:)'*y(ar+1-i:end-i,:)./((size(y,1)-ar)*s'*s);
|
||||||
autocorr = [ autocorr diag(oo_.autocorr{i}) ];
|
autocorr = [ autocorr diag(oo_.autocorr{i}) ];
|
||||||
end
|
end
|
||||||
title = 'AUTOCORRELATION OF SIMULATED VARIABLES';
|
title = 'AUTOCORRELATION OF SIMULATED VARIABLES';
|
||||||
if options_.hp_filter
|
if options_.hp_filter
|
||||||
title = [title ' (HP filter, lambda = ' ...
|
title = [title ' (HP filter, lambda = ' ...
|
||||||
int2str(options_.hp_filter) ')'];
|
int2str(options_.hp_filter) ')'];
|
||||||
end
|
end
|
||||||
headers = strvcat('VARIABLE',int2str([1:ar]'));
|
headers = strvcat('VARIABLE',int2str([1:ar]'));
|
||||||
dyntable(title,headers,labels,autocorr,size(labels,2)+2,8,4);
|
dyntable(title,headers,labels,autocorr,size(labels,2)+2,8,4);
|
||||||
end
|
end
|
||||||
|
|
||||||
warning(warning_old_state);
|
warning(warning_old_state);
|
||||||
|
|
|
||||||
|
|
@ -1,6 +1,6 @@
|
||||||
function disp_th_moments(dr,var_list)
|
function disp_th_moments(dr,var_list)
|
||||||
% Display theoretical moments of variables
|
% Display theoretical moments of variables
|
||||||
|
|
||||||
% Copyright (C) 2001-2009 Dynare Team
|
% Copyright (C) 2001-2009 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
|
|
@ -18,62 +18,62 @@ function disp_th_moments(dr,var_list)
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
global M_ oo_ options_
|
global M_ oo_ options_
|
||||||
|
|
||||||
if size(var_list,1) == 0
|
|
||||||
var_list = M_.endo_names(1:M_.orig_endo_nbr, :);
|
|
||||||
end
|
|
||||||
nvar = size(var_list,1);
|
|
||||||
ivar=zeros(nvar,1);
|
|
||||||
for i=1:nvar
|
|
||||||
i_tmp = strmatch(var_list(i,:),M_.endo_names,'exact');
|
|
||||||
if isempty(i_tmp)
|
|
||||||
error (['One of the variable specified does not exist']) ;
|
|
||||||
else
|
|
||||||
ivar(i) = i_tmp;
|
|
||||||
end
|
|
||||||
end
|
|
||||||
|
|
||||||
[oo_.gamma_y,stationary_vars] = th_autocovariances(dr,ivar,M_,options_);
|
|
||||||
m = dr.ys(ivar);
|
|
||||||
non_stationary_vars = setdiff(1:length(ivar),stationary_vars);
|
|
||||||
ivar1 = intersect(non_stationary_vars,ivar);
|
|
||||||
m(ivar1) = NaN;
|
|
||||||
|
|
||||||
|
if size(var_list,1) == 0
|
||||||
i1 = find(abs(diag(oo_.gamma_y{1})) > 1e-12);
|
var_list = M_.endo_names(1:M_.orig_endo_nbr, :);
|
||||||
s2 = diag(oo_.gamma_y{1});
|
end
|
||||||
sd = sqrt(s2);
|
nvar = size(var_list,1);
|
||||||
if options_.order == 2
|
ivar=zeros(nvar,1);
|
||||||
|
for i=1:nvar
|
||||||
|
i_tmp = strmatch(var_list(i,:),M_.endo_names,'exact');
|
||||||
|
if isempty(i_tmp)
|
||||||
|
error (['One of the variable specified does not exist']) ;
|
||||||
|
else
|
||||||
|
ivar(i) = i_tmp;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
[oo_.gamma_y,stationary_vars] = th_autocovariances(dr,ivar,M_,options_);
|
||||||
|
m = dr.ys(ivar);
|
||||||
|
non_stationary_vars = setdiff(1:length(ivar),stationary_vars);
|
||||||
|
ivar1 = intersect(non_stationary_vars,ivar);
|
||||||
|
m(ivar1) = NaN;
|
||||||
|
|
||||||
|
|
||||||
|
i1 = find(abs(diag(oo_.gamma_y{1})) > 1e-12);
|
||||||
|
s2 = diag(oo_.gamma_y{1});
|
||||||
|
sd = sqrt(s2);
|
||||||
|
if options_.order == 2
|
||||||
m = m+oo_.gamma_y{options_.ar+3};
|
m = m+oo_.gamma_y{options_.ar+3};
|
||||||
end
|
end
|
||||||
|
|
||||||
z = [ m sd s2 ];
|
z = [ m sd s2 ];
|
||||||
oo_.mean = m;
|
oo_.mean = m;
|
||||||
oo_.var = oo_.gamma_y{1};
|
oo_.var = oo_.gamma_y{1};
|
||||||
|
|
||||||
if options_.nomoments == 0
|
if options_.nomoments == 0
|
||||||
title='THEORETICAL MOMENTS';
|
title='THEORETICAL MOMENTS';
|
||||||
if options_.hp_filter
|
if options_.hp_filter
|
||||||
title = [title ' (HP filter, lambda = ' int2str(options_.hp_filter) ')'];
|
title = [title ' (HP filter, lambda = ' int2str(options_.hp_filter) ')'];
|
||||||
end
|
end
|
||||||
headers=strvcat('VARIABLE','MEAN','STD. DEV.','VARIANCE');
|
headers=strvcat('VARIABLE','MEAN','STD. DEV.','VARIANCE');
|
||||||
labels = deblank(M_.endo_names(ivar,:));
|
labels = deblank(M_.endo_names(ivar,:));
|
||||||
lh = size(labels,2)+2;
|
lh = size(labels,2)+2;
|
||||||
dyntable(title,headers,labels,z,lh,11,4);
|
dyntable(title,headers,labels,z,lh,11,4);
|
||||||
if M_.exo_nbr > 1
|
if M_.exo_nbr > 1
|
||||||
disp(' ')
|
disp(' ')
|
||||||
title='VARIANCE DECOMPOSITION (in percent)';
|
title='VARIANCE DECOMPOSITION (in percent)';
|
||||||
if options_.hp_filter
|
if options_.hp_filter
|
||||||
title = [title ' (HP filter, lambda = ' ...
|
title = [title ' (HP filter, lambda = ' ...
|
||||||
int2str(options_.hp_filter) ')'];
|
int2str(options_.hp_filter) ')'];
|
||||||
end
|
end
|
||||||
headers = M_.exo_names;
|
headers = M_.exo_names;
|
||||||
headers(M_.exo_names_orig_ord,:) = headers;
|
headers(M_.exo_names_orig_ord,:) = headers;
|
||||||
headers = strvcat(' ',headers);
|
headers = strvcat(' ',headers);
|
||||||
lh = size(deblank(M_.endo_names(ivar(stationary_vars),:)),2)+2;
|
lh = size(deblank(M_.endo_names(ivar(stationary_vars),:)),2)+2;
|
||||||
dyntable(title,headers,deblank(M_.endo_names(ivar(stationary_vars), ...
|
dyntable(title,headers,deblank(M_.endo_names(ivar(stationary_vars), ...
|
||||||
:)),100*oo_.gamma_y{options_.ar+2}(stationary_vars,:),lh,8,2);
|
:)),100*oo_.gamma_y{options_.ar+2}(stationary_vars,:),lh,8,2);
|
||||||
end
|
end
|
||||||
|
|
||||||
conditional_variance_steps = options_.conditional_variance_decomposition;
|
conditional_variance_steps = options_.conditional_variance_decomposition;
|
||||||
|
|
@ -82,34 +82,34 @@ function disp_th_moments(dr,var_list)
|
||||||
ivar,dr,M_, ...
|
ivar,dr,M_, ...
|
||||||
options_,oo_);
|
options_,oo_);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
||||||
if options_.nocorr == 0
|
if options_.nocorr == 0
|
||||||
disp(' ')
|
disp(' ')
|
||||||
title='MATRIX OF CORRELATIONS';
|
title='MATRIX OF CORRELATIONS';
|
||||||
if options_.hp_filter
|
if options_.hp_filter
|
||||||
title = [title ' (HP filter, lambda = ' int2str(options_.hp_filter) ')'];
|
title = [title ' (HP filter, lambda = ' int2str(options_.hp_filter) ')'];
|
||||||
end
|
end
|
||||||
labels = deblank(M_.endo_names(ivar(i1),:));
|
labels = deblank(M_.endo_names(ivar(i1),:));
|
||||||
headers = strvcat('Variables',labels);
|
headers = strvcat('Variables',labels);
|
||||||
corr = oo_.gamma_y{1}(i1,i1)./(sd(i1)*sd(i1)');
|
corr = oo_.gamma_y{1}(i1,i1)./(sd(i1)*sd(i1)');
|
||||||
lh = size(labels,2)+2;
|
lh = size(labels,2)+2;
|
||||||
dyntable(title,headers,labels,corr,lh,8,4);
|
dyntable(title,headers,labels,corr,lh,8,4);
|
||||||
end
|
end
|
||||||
|
|
||||||
if options_.ar > 0
|
if options_.ar > 0
|
||||||
disp(' ')
|
disp(' ')
|
||||||
title='COEFFICIENTS OF AUTOCORRELATION';
|
title='COEFFICIENTS OF AUTOCORRELATION';
|
||||||
if options_.hp_filter
|
if options_.hp_filter
|
||||||
title = [title ' (HP filter, lambda = ' int2str(options_.hp_filter) ')'];
|
title = [title ' (HP filter, lambda = ' int2str(options_.hp_filter) ')'];
|
||||||
end
|
end
|
||||||
labels = deblank(M_.endo_names(ivar(i1),:));
|
labels = deblank(M_.endo_names(ivar(i1),:));
|
||||||
headers = strvcat('Order ',int2str([1:options_.ar]'));
|
headers = strvcat('Order ',int2str([1:options_.ar]'));
|
||||||
z=[];
|
z=[];
|
||||||
for i=1:options_.ar
|
for i=1:options_.ar
|
||||||
oo_.autocorr{i} = oo_.gamma_y{i+1};
|
oo_.autocorr{i} = oo_.gamma_y{i+1};
|
||||||
z(:,i) = diag(oo_.gamma_y{i+1}(i1,i1));
|
z(:,i) = diag(oo_.gamma_y{i+1}(i1,i1));
|
||||||
end
|
end
|
||||||
lh = size(labels,2)+2;
|
lh = size(labels,2)+2;
|
||||||
dyntable(title,headers,labels,z,lh,8,4);
|
dyntable(title,headers,labels,z,lh,8,4);
|
||||||
end
|
end
|
||||||
|
|
|
||||||
|
|
@ -32,45 +32,45 @@ function oo_ = display_conditional_variance_decomposition(Steps, SubsetOfVariabl
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
endo_nbr = M_.endo_nbr;
|
|
||||||
exo_nbr = M_.exo_nbr;
|
|
||||||
StateSpaceModel.number_of_state_equations = M_.endo_nbr;
|
|
||||||
StateSpaceModel.number_of_state_innovations = exo_nbr;
|
|
||||||
StateSpaceModel.sigma_e_is_diagonal = M_.sigma_e_is_diagonal;
|
|
||||||
|
|
||||||
iv = (1:endo_nbr)';
|
|
||||||
ic = dr.nstatic+(1:dr.npred)';
|
|
||||||
|
|
||||||
[StateSpaceModel.transition_matrix,StateSpaceModel.impulse_matrix] = kalman_transition_matrix(dr,iv,ic,[],exo_nbr);
|
|
||||||
StateSpaceModel.state_innovations_covariance_matrix = M_.Sigma_e;
|
|
||||||
|
|
||||||
conditional_decomposition_array = conditional_variance_decomposition(StateSpaceModel,Steps,dr.inv_order_var(SubsetOfVariables ));
|
endo_nbr = M_.endo_nbr;
|
||||||
|
exo_nbr = M_.exo_nbr;
|
||||||
|
StateSpaceModel.number_of_state_equations = M_.endo_nbr;
|
||||||
|
StateSpaceModel.number_of_state_innovations = exo_nbr;
|
||||||
|
StateSpaceModel.sigma_e_is_diagonal = M_.sigma_e_is_diagonal;
|
||||||
|
|
||||||
|
iv = (1:endo_nbr)';
|
||||||
|
ic = dr.nstatic+(1:dr.npred)';
|
||||||
|
|
||||||
|
[StateSpaceModel.transition_matrix,StateSpaceModel.impulse_matrix] = kalman_transition_matrix(dr,iv,ic,[],exo_nbr);
|
||||||
|
StateSpaceModel.state_innovations_covariance_matrix = M_.Sigma_e;
|
||||||
|
|
||||||
|
conditional_decomposition_array = conditional_variance_decomposition(StateSpaceModel,Steps,dr.inv_order_var(SubsetOfVariables ));
|
||||||
|
|
||||||
|
if options_.noprint == 0
|
||||||
|
disp(' ')
|
||||||
|
disp('CONDITIONAL VARIANCE DECOMPOSITION (in percent)')
|
||||||
|
end
|
||||||
|
|
||||||
|
vardec_i = zeros(length(SubsetOfVariables),exo_nbr);
|
||||||
|
|
||||||
|
for i=1:length(Steps)
|
||||||
|
disp(['Period ' int2str(Steps(i)) ':'])
|
||||||
|
|
||||||
|
for j=1:exo_nbr
|
||||||
|
vardec_i(:,j) = diag_vech(conditional_decomposition_array(:, ...
|
||||||
|
i,j));
|
||||||
|
end
|
||||||
|
vardec_i = 100*vardec_i./repmat(sum(vardec_i,2),1,exo_nbr);
|
||||||
if options_.noprint == 0
|
if options_.noprint == 0
|
||||||
disp(' ')
|
headers = M_.exo_names;
|
||||||
disp('CONDITIONAL VARIANCE DECOMPOSITION (in percent)')
|
headers(M_.exo_names_orig_ord,:) = headers;
|
||||||
|
headers = strvcat(' ',headers);
|
||||||
|
lh = size(deblank(M_.endo_names(SubsetOfVariables,:)),2)+2;
|
||||||
|
dyntable('',headers,...
|
||||||
|
deblank(M_.endo_names(SubsetOfVariables,:)),...
|
||||||
|
vardec_i,lh,8,2);
|
||||||
end
|
end
|
||||||
|
end
|
||||||
vardec_i = zeros(length(SubsetOfVariables),exo_nbr);
|
|
||||||
|
oo_.conditional_variance_decomposition = conditional_decomposition_array;
|
||||||
for i=1:length(Steps)
|
|
||||||
disp(['Period ' int2str(Steps(i)) ':'])
|
|
||||||
|
|
||||||
for j=1:exo_nbr
|
|
||||||
vardec_i(:,j) = diag_vech(conditional_decomposition_array(:, ...
|
|
||||||
i,j));
|
|
||||||
end
|
|
||||||
vardec_i = 100*vardec_i./repmat(sum(vardec_i,2),1,exo_nbr);
|
|
||||||
if options_.noprint == 0
|
|
||||||
headers = M_.exo_names;
|
|
||||||
headers(M_.exo_names_orig_ord,:) = headers;
|
|
||||||
headers = strvcat(' ',headers);
|
|
||||||
lh = size(deblank(M_.endo_names(SubsetOfVariables,:)),2)+2;
|
|
||||||
dyntable('',headers,...
|
|
||||||
deblank(M_.endo_names(SubsetOfVariables,:)),...
|
|
||||||
vardec_i,lh,8,2);
|
|
||||||
end
|
|
||||||
end
|
|
||||||
|
|
||||||
oo_.conditional_variance_decomposition = conditional_decomposition_array;
|
|
||||||
|
|
@ -38,50 +38,50 @@ function m = compute_prior_mode(hyperparameters,shape)
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
m = NaN ;
|
m = NaN ;
|
||||||
switch shape
|
switch shape
|
||||||
case 1
|
case 1
|
||||||
if (hyperparameters(1)>1 && hyperparameters(2)>1)
|
if (hyperparameters(1)>1 && hyperparameters(2)>1)
|
||||||
m = (hyperparameters(1)-1)/(hyperparameters(1)+hyperparameters(2)-2) ;
|
m = (hyperparameters(1)-1)/(hyperparameters(1)+hyperparameters(2)-2) ;
|
||||||
elseif (hyperparameters(1)<1 && hyperparameters(2)<1)
|
elseif (hyperparameters(1)<1 && hyperparameters(2)<1)
|
||||||
m = [ 0 ; 1 ] ;
|
m = [ 0 ; 1 ] ;
|
||||||
elseif ( hyperparameters(1)<1 && hyperparameters(2)>1-eps ) || ( abs(hyperparameters(1)-1)<2*eps && hyperparameters(2)>1 )
|
elseif ( hyperparameters(1)<1 && hyperparameters(2)>1-eps ) || ( abs(hyperparameters(1)-1)<2*eps && hyperparameters(2)>1 )
|
||||||
m = 0;
|
m = 0;
|
||||||
elseif ( hyperparameters(1)>1 && hyperparameters(2)<1+eps ) || ( abs(hyperparameters(1)-1)<2*eps && hyperparameters(2)<1 )
|
elseif ( hyperparameters(1)>1 && hyperparameters(2)<1+eps ) || ( abs(hyperparameters(1)-1)<2*eps && hyperparameters(2)<1 )
|
||||||
m = 1;
|
m = 1;
|
||||||
elseif ( abs(hyperparameters(1)-1)<2*eps && abs(hyperparameters(2)-1)<2*eps )% Uniform distribution!
|
elseif ( abs(hyperparameters(1)-1)<2*eps && abs(hyperparameters(2)-1)<2*eps )% Uniform distribution!
|
||||||
m = .5 ;
|
m = .5 ;
|
||||||
end
|
end
|
||||||
if length(hyperparameters)==4
|
if length(hyperparameters)==4
|
||||||
m = m*(hyperparameters(4)-hyperparameters(3)) + hyperparameters(3) ;
|
m = m*(hyperparameters(4)-hyperparameters(3)) + hyperparameters(3) ;
|
||||||
end
|
end
|
||||||
case 2
|
case 2
|
||||||
if hyperparameters(1)<1
|
if hyperparameters(1)<1
|
||||||
m = 0;
|
m = 0;
|
||||||
else
|
else
|
||||||
m = (hyperparameters(1)-1)*hyperparameters(2);
|
m = (hyperparameters(1)-1)*hyperparameters(2);
|
||||||
end
|
end
|
||||||
if length(hyperparameters)>2
|
if length(hyperparameters)>2
|
||||||
m = m + hyperparameters(3);
|
m = m + hyperparameters(3);
|
||||||
end
|
end
|
||||||
case 3
|
case 3
|
||||||
m = hyperparameters(1);
|
m = hyperparameters(1);
|
||||||
case 4
|
case 4
|
||||||
% s = hyperparameters(1)
|
% s = hyperparameters(1)
|
||||||
% nu = hyperparameters(2)
|
% nu = hyperparameters(2)
|
||||||
m = 1/sqrt((hyperparameters(2)+1)/hyperparameters(1));%sqrt((hyperparameters(2)-1)/hyperparameters(1))
|
m = 1/sqrt((hyperparameters(2)+1)/hyperparameters(1));%sqrt((hyperparameters(2)-1)/hyperparameters(1))
|
||||||
if length(hyperparameters)>2
|
if length(hyperparameters)>2
|
||||||
m = m + hyperparameters(3);
|
m = m + hyperparameters(3);
|
||||||
end
|
end
|
||||||
case 5
|
case 5
|
||||||
m = .5*(hyperparameters(2)-hyperparameters(1)) ;
|
m = .5*(hyperparameters(2)-hyperparameters(1)) ;
|
||||||
case 6
|
case 6
|
||||||
% s = hyperparameters(1)
|
% s = hyperparameters(1)
|
||||||
% nu = hyperparameters(2)
|
% nu = hyperparameters(2)
|
||||||
m = hyperparameters(1)/(hyperparameters(2)+2) ;
|
m = hyperparameters(1)/(hyperparameters(2)+2) ;
|
||||||
if length(hyperparameters)>2
|
if length(hyperparameters)>2
|
||||||
m = m + hyperparameters(3) ;
|
m = m + hyperparameters(3) ;
|
||||||
end
|
end
|
||||||
otherwise
|
otherwise
|
||||||
error('Unknown prior shape!')
|
error('Unknown prior shape!')
|
||||||
end
|
end
|
||||||
|
|
@ -38,37 +38,37 @@ sigma2 = sigma^2;
|
||||||
mu2 = mu^2;
|
mu2 = mu^2;
|
||||||
|
|
||||||
if type == 2; % Inverse Gamma 2
|
if type == 2; % Inverse Gamma 2
|
||||||
nu = 2*(2+mu2/sigma2);
|
nu = 2*(2+mu2/sigma2);
|
||||||
s = 2*mu*(1+mu2/sigma2);
|
s = 2*mu*(1+mu2/sigma2);
|
||||||
elseif type == 1; % Inverse Gamma 1
|
elseif type == 1; % Inverse Gamma 1
|
||||||
if sigma2 < Inf;
|
if sigma2 < Inf;
|
||||||
nu = sqrt(2*(2+mu2/sigma2));
|
nu = sqrt(2*(2+mu2/sigma2));
|
||||||
nu2 = 2*nu;
|
nu2 = 2*nu;
|
||||||
nu1 = 2;
|
nu1 = 2;
|
||||||
err = 2*mu2*gamma(nu/2)^2-(sigma2+mu2)*(nu-2)*gamma((nu-1)/2)^2;
|
err = 2*mu2*gamma(nu/2)^2-(sigma2+mu2)*(nu-2)*gamma((nu-1)/2)^2;
|
||||||
while abs(nu2-nu1) > 1e-12
|
while abs(nu2-nu1) > 1e-12
|
||||||
if err > 0
|
if err > 0
|
||||||
nu1 = nu;
|
nu1 = nu;
|
||||||
if nu < nu2
|
if nu < nu2
|
||||||
nu = nu2;
|
nu = nu2;
|
||||||
else
|
else
|
||||||
nu = 2*nu;
|
nu = 2*nu;
|
||||||
nu2 = nu;
|
nu2 = nu;
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
nu2 = nu;
|
nu2 = nu;
|
||||||
end
|
end
|
||||||
nu = (nu1+nu2)/2;
|
nu = (nu1+nu2)/2;
|
||||||
err = 2*mu2*gamma(nu/2)^2-(sigma2+mu2)*(nu-2)*gamma((nu-1)/2)^2;
|
err = 2*mu2*gamma(nu/2)^2-(sigma2+mu2)*(nu-2)*gamma((nu-1)/2)^2;
|
||||||
end
|
end
|
||||||
s = (sigma2+mu2)*(nu-2);
|
s = (sigma2+mu2)*(nu-2);
|
||||||
else;
|
else;
|
||||||
nu = 2;
|
nu = 2;
|
||||||
s = 2*mu2/pi;
|
s = 2*mu2/pi;
|
||||||
end;
|
end;
|
||||||
else;
|
else;
|
||||||
s = -1;
|
s = -1;
|
||||||
nu = -1;
|
nu = -1;
|
||||||
end;
|
end;
|
||||||
|
|
||||||
% 01/18/2004 MJ replaced fsolve with secant
|
% 01/18/2004 MJ replaced fsolve with secant
|
||||||
|
|
|
||||||
|
|
@ -34,156 +34,156 @@ function [mu, parameters] = mode_and_variance_to_mean(m,s2,distribution,lower_bo
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
% Check input aruments.
|
% Check input aruments.
|
||||||
if ~(nargin==3 || nargin==5 || nargin==4 )
|
if ~(nargin==3 || nargin==5 || nargin==4 )
|
||||||
error('mode_and_variance_to mean:: 3 or 5 input arguments are needed!')
|
error('mode_and_variance_to mean:: 3 or 5 input arguments are needed!')
|
||||||
|
end
|
||||||
|
|
||||||
|
% Set defaults bounds.
|
||||||
|
if nargin==3
|
||||||
|
switch distribution
|
||||||
|
case 1
|
||||||
|
lower_bound = 0;
|
||||||
|
upper_bound = Inf;
|
||||||
|
case 3
|
||||||
|
lower_bound = 0;
|
||||||
|
upper_bound = Inf;
|
||||||
|
case 2
|
||||||
|
lower_bound = 0;
|
||||||
|
upper_bound = Inf;
|
||||||
|
case 4
|
||||||
|
lower_bound = 0;
|
||||||
|
upper_bound = 1;
|
||||||
|
otherwise
|
||||||
|
error('Unknown distribution!')
|
||||||
end
|
end
|
||||||
|
end
|
||||||
% Set defaults bounds.
|
if nargin==4
|
||||||
if nargin==3
|
switch distribution
|
||||||
switch distribution
|
case 1
|
||||||
case 1
|
upper_bound = Inf;
|
||||||
lower_bound = 0;
|
case 3
|
||||||
upper_bound = Inf;
|
upper_bound = Inf;
|
||||||
case 3
|
case 2
|
||||||
lower_bound = 0;
|
upper_bound = Inf;
|
||||||
upper_bound = Inf;
|
case 4
|
||||||
case 2
|
upper_bound = 1;
|
||||||
lower_bound = 0;
|
otherwise
|
||||||
upper_bound = Inf;
|
error('Unknown distribution!')
|
||||||
case 4
|
|
||||||
lower_bound = 0;
|
|
||||||
upper_bound = 1;
|
|
||||||
otherwise
|
|
||||||
error('Unknown distribution!')
|
|
||||||
end
|
|
||||||
end
|
end
|
||||||
if nargin==4
|
end
|
||||||
switch distribution
|
|
||||||
case 1
|
|
||||||
upper_bound = Inf;
|
if (distribution==1)% Gamma distribution
|
||||||
case 3
|
if m<lower_bound
|
||||||
upper_bound = Inf;
|
error('The mode has to be greater than the lower bound!')
|
||||||
case 2
|
|
||||||
upper_bound = Inf;
|
|
||||||
case 4
|
|
||||||
upper_bound = 1;
|
|
||||||
otherwise
|
|
||||||
error('Unknown distribution!')
|
|
||||||
end
|
|
||||||
end
|
end
|
||||||
|
if (m-lower_bound)<1e-12
|
||||||
|
error('The gamma distribution should be specified with the mean and variance.')
|
||||||
if (distribution==1)% Gamma distribution
|
end
|
||||||
if m<lower_bound
|
m = m - lower_bound ;
|
||||||
error('The mode has to be greater than the lower bound!')
|
beta = -.5*m*(1-sqrt(1+4*s2/(m*m))) ;
|
||||||
end
|
alpha = (m+beta)/beta ;
|
||||||
if (m-lower_bound)<1e-12
|
parameters(1) = alpha;
|
||||||
error('The gamma distribution should be specified with the mean and variance.')
|
parameters(2) = beta;
|
||||||
end
|
mu = alpha*beta + lower_bound ;
|
||||||
m = m - lower_bound ;
|
return
|
||||||
beta = -.5*m*(1-sqrt(1+4*s2/(m*m))) ;
|
end
|
||||||
alpha = (m+beta)/beta ;
|
|
||||||
parameters(1) = alpha;
|
if (distribution==2)% Inverse Gamma - 2 distribution
|
||||||
parameters(2) = beta;
|
if m<lower_bound+2*eps
|
||||||
mu = alpha*beta + lower_bound ;
|
error('The mode has to be greater than the lower bound!')
|
||||||
return
|
|
||||||
end
|
end
|
||||||
|
m = m - lower_bound ;
|
||||||
if (distribution==2)% Inverse Gamma - 2 distribution
|
if isinf(s2)
|
||||||
if m<lower_bound+2*eps
|
nu = 4;
|
||||||
error('The mode has to be greater than the lower bound!')
|
s = 2*m;
|
||||||
end
|
else
|
||||||
m = m - lower_bound ;
|
delta = 2*(m*m/s2);
|
||||||
if isinf(s2)
|
poly = [ 1 , -(8+delta) , 20-4*delta , -(16+4*delta) ];
|
||||||
nu = 4;
|
all_roots = roots(poly);
|
||||||
s = 2*m;
|
real_roots = all_roots(find(abs(imag(all_roots))<2*eps));
|
||||||
else
|
nu = real_roots(find(real_roots>2));
|
||||||
delta = 2*(m*m/s2);
|
s = m*(nu+2);
|
||||||
poly = [ 1 , -(8+delta) , 20-4*delta , -(16+4*delta) ];
|
|
||||||
all_roots = roots(poly);
|
|
||||||
real_roots = all_roots(find(abs(imag(all_roots))<2*eps));
|
|
||||||
nu = real_roots(find(real_roots>2));
|
|
||||||
s = m*(nu+2);
|
|
||||||
end
|
|
||||||
parameters(1) = nu;
|
|
||||||
parameters(2) = s;
|
|
||||||
mu = s/(nu-2) + lower_bound;
|
|
||||||
return
|
|
||||||
end
|
end
|
||||||
|
parameters(1) = nu;
|
||||||
if (distribution==3)% Inverted Gamma 1 distribution
|
parameters(2) = s;
|
||||||
if m<lower_bound+2*eps
|
mu = s/(nu-2) + lower_bound;
|
||||||
error('The mode has to be greater than the lower bound!')
|
return
|
||||||
end
|
end
|
||||||
m = m - lower_bound ;
|
|
||||||
if isinf(s2)
|
if (distribution==3)% Inverted Gamma 1 distribution
|
||||||
nu = 2;
|
if m<lower_bound+2*eps
|
||||||
s = 3*(m*m);
|
error('The mode has to be greater than the lower bound!')
|
||||||
else
|
end
|
||||||
[mu, parameters] = mode_and_variance_to_mean(m,s2,2);
|
m = m - lower_bound ;
|
||||||
nu = sqrt(parameters(1));
|
if isinf(s2)
|
||||||
nu2 = 2*nu;
|
nu = 2;
|
||||||
nu1 = 2;
|
s = 3*(m*m);
|
||||||
err = s2/(m*m) - (nu+1)/(nu-2) + .5*(nu+1)*(gamma((nu-1)/2)/gamma(nu/2))^2;
|
else
|
||||||
while abs(nu2-nu1) > 1e-12
|
[mu, parameters] = mode_and_variance_to_mean(m,s2,2);
|
||||||
if err < 0
|
nu = sqrt(parameters(1));
|
||||||
nu1 = nu;
|
nu2 = 2*nu;
|
||||||
if nu < nu2
|
nu1 = 2;
|
||||||
nu = nu2;
|
err = s2/(m*m) - (nu+1)/(nu-2) + .5*(nu+1)*(gamma((nu-1)/2)/gamma(nu/2))^2;
|
||||||
else
|
while abs(nu2-nu1) > 1e-12
|
||||||
nu = 2*nu;
|
if err < 0
|
||||||
nu2 = nu;
|
nu1 = nu;
|
||||||
end
|
if nu < nu2
|
||||||
|
nu = nu2;
|
||||||
else
|
else
|
||||||
|
nu = 2*nu;
|
||||||
nu2 = nu;
|
nu2 = nu;
|
||||||
end
|
end
|
||||||
nu = (nu1+nu2)/2;
|
else
|
||||||
err = s2/(m*m) - (nu+1)/(nu-2) + .5*(nu+1)*(gamma((nu-1)/2)/gamma(nu/2))^2;
|
nu2 = nu;
|
||||||
end
|
end
|
||||||
s = (nu+1)*(m*m) ;
|
nu = (nu1+nu2)/2;
|
||||||
|
err = s2/(m*m) - (nu+1)/(nu-2) + .5*(nu+1)*(gamma((nu-1)/2)/gamma(nu/2))^2;
|
||||||
end
|
end
|
||||||
parameters(1) = nu;
|
s = (nu+1)*(m*m) ;
|
||||||
parameters(2) = s;
|
|
||||||
mu = sqrt(.5*s)*gamma(.5*(nu-1))/gamma(.5*nu) + lower_bound ;
|
|
||||||
return
|
|
||||||
end
|
end
|
||||||
|
parameters(1) = nu;
|
||||||
if (distribution==4)% Beta distribution
|
parameters(2) = s;
|
||||||
if m<lower_bound
|
mu = sqrt(.5*s)*gamma(.5*(nu-1))/gamma(.5*nu) + lower_bound ;
|
||||||
error('The mode has to be greater than the lower bound!')
|
return
|
||||||
end
|
end
|
||||||
if m>upper_bound
|
|
||||||
error('The mode has to be less than the upper bound!')
|
if (distribution==4)% Beta distribution
|
||||||
end
|
if m<lower_bound
|
||||||
if (m-lower_bound)<1e-12
|
error('The mode has to be greater than the lower bound!')
|
||||||
error('The beta distribution should be specified with the mean and variance.')
|
end
|
||||||
end
|
if m>upper_bound
|
||||||
if (upper_bound-m)<1e-12
|
error('The mode has to be less than the upper bound!')
|
||||||
error('The beta distribution should be specified with the mean and variance.')
|
end
|
||||||
end
|
if (m-lower_bound)<1e-12
|
||||||
ll = upper_bound-lower_bound;
|
error('The beta distribution should be specified with the mean and variance.')
|
||||||
m = (m-lower_bound)/ll;
|
end
|
||||||
s2 = s2/(ll*ll);
|
if (upper_bound-m)<1e-12
|
||||||
delta = m^2/s2;
|
error('The beta distribution should be specified with the mean and variance.')
|
||||||
poly = NaN(1,4);
|
end
|
||||||
poly(1) = 1;
|
ll = upper_bound-lower_bound;
|
||||||
poly(2) = 7*m-(1-m)*delta-3;
|
m = (m-lower_bound)/ll;
|
||||||
poly(3) = 16*m^2-14*m+3-2*m*delta+delta;
|
s2 = s2/(ll*ll);
|
||||||
poly(4) = 12*m^3-16*m^2+7*m-1;
|
delta = m^2/s2;
|
||||||
all_roots = roots(poly);
|
poly = NaN(1,4);
|
||||||
real_roots = all_roots(find(abs(imag(all_roots))<2*eps));
|
poly(1) = 1;
|
||||||
idx = find(real_roots>1);
|
poly(2) = 7*m-(1-m)*delta-3;
|
||||||
if length(idx)>1
|
poly(3) = 16*m^2-14*m+3-2*m*delta+delta;
|
||||||
error('Multiplicity of solutions for the beta distribution specification.')
|
poly(4) = 12*m^3-16*m^2+7*m-1;
|
||||||
elseif isempty(idx)
|
all_roots = roots(poly);
|
||||||
disp('No solution for the beta distribution specification. You should reduce the variance.')
|
real_roots = all_roots(find(abs(imag(all_roots))<2*eps));
|
||||||
error();
|
idx = find(real_roots>1);
|
||||||
end
|
if length(idx)>1
|
||||||
alpha = real_roots(idx);
|
error('Multiplicity of solutions for the beta distribution specification.')
|
||||||
beta = ((1-m)*alpha+2*m-1)/m;
|
elseif isempty(idx)
|
||||||
parameters(1) = alpha;
|
disp('No solution for the beta distribution specification. You should reduce the variance.')
|
||||||
parameters(2) = beta;
|
error();
|
||||||
mu = alpha/(alpha+beta)*(upper_bound-lower_bound)+lower_bound;
|
end
|
||||||
return
|
alpha = real_roots(idx);
|
||||||
end
|
beta = ((1-m)*alpha+2*m-1)/m;
|
||||||
|
parameters(1) = alpha;
|
||||||
|
parameters(2) = beta;
|
||||||
|
mu = alpha/(alpha+beta)*(upper_bound-lower_bound)+lower_bound;
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
@ -31,6 +31,6 @@ function density = multivariate_normal_pdf(X,Mean,Sigma_upper_chol,n);
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
density = (2*pi)^(-.5*n) * ...
|
density = (2*pi)^(-.5*n) * ...
|
||||||
prod(diag(Sigma_upper_chol))^(-1) * ...
|
prod(diag(Sigma_upper_chol))^(-1) * ...
|
||||||
exp(-.5*(X-Mean)*(Sigma_upper_chol\(transpose(Sigma_upper_chol)\transpose(X-Mean))));
|
exp(-.5*(X-Mean)*(Sigma_upper_chol\(transpose(Sigma_upper_chol)\transpose(X-Mean))));
|
||||||
|
|
@ -30,7 +30,7 @@ function density = multivariate_student_pdf(X,Mean,Sigma_upper_chol,df);
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
nn = length(X);
|
nn = length(X);
|
||||||
t1 = gamma( .5*(nn+df) ) / ( gamma( .5*nn ) * (df*pi)^(.5*nn) ) ;
|
t1 = gamma( .5*(nn+df) ) / ( gamma( .5*nn ) * (df*pi)^(.5*nn) ) ;
|
||||||
t2 = t1 / prod(diag(Sigma_upper_chol)) ;
|
t2 = t1 / prod(diag(Sigma_upper_chol)) ;
|
||||||
density = t2 / ( 1 + (X-Mean)*(Sigma_upper_chol\(transpose(Sigma_upper_chol)\transpose(X-Mean)))/df )^(.5*(nn+df));
|
density = t2 / ( 1 + (X-Mean)*(Sigma_upper_chol\(transpose(Sigma_upper_chol)\transpose(X-Mean)))/df )^(.5*(nn+df));
|
||||||
|
|
@ -41,13 +41,13 @@ function G = rand_inverse_wishart(m, v, H_inv_upper_chol)
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
X = randn(v, m) * H_inv_upper_chol;
|
X = randn(v, m) * H_inv_upper_chol;
|
||||||
|
|
||||||
|
|
||||||
% At this point, X'*X is Wishart distributed
|
|
||||||
% G = inv(X'*X);
|
|
||||||
|
|
||||||
% Rather compute inv(X'*X) using the SVD
|
|
||||||
[U,S,V] = svd(X, 0);
|
% At this point, X'*X is Wishart distributed
|
||||||
SSi = 1 ./ (diag(S) .^ 2);
|
% G = inv(X'*X);
|
||||||
G = (V .* repmat(SSi', m, 1)) * V';
|
|
||||||
|
% Rather compute inv(X'*X) using the SVD
|
||||||
|
[U,S,V] = svd(X, 0);
|
||||||
|
SSi = 1 ./ (diag(S) .^ 2);
|
||||||
|
G = (V .* repmat(SSi', m, 1)) * V';
|
||||||
|
|
@ -37,7 +37,7 @@ function B = rand_matrix_normal(n, p, M, Omega_lower_chol, Sigma_lower_chol)
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
B1 = randn(n * p, 1);
|
B1 = randn(n * p, 1);
|
||||||
B2 = kron(Omega_lower_chol, Sigma_lower_chol) * B1;
|
B2 = kron(Omega_lower_chol, Sigma_lower_chol) * B1;
|
||||||
B3 = reshape(B2, n, p);
|
B3 = reshape(B2, n, p);
|
||||||
B = B3 + M;
|
B = B3 + M;
|
||||||
|
|
|
||||||
|
|
@ -31,4 +31,4 @@ function draw = rand_multivariate_normal(Mean,Sigma_upper_chol,n)
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
draw = Mean + randn(1,n) * Sigma_upper_chol;
|
draw = Mean + randn(1,n) * Sigma_upper_chol;
|
||||||
|
|
|
||||||
|
|
@ -35,5 +35,5 @@ function draw = rand_multivariate_student(Mean,Sigma_upper_chol,df)
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
n = length(Mean);
|
n = length(Mean);
|
||||||
draw = Mean + randn(1,n) * Sigma_upper_chol * sqrt(df/sum(randn(df,1).^2));
|
draw = Mean + randn(1,n) * Sigma_upper_chol * sqrt(df/sum(randn(df,1).^2));
|
||||||
|
|
|
||||||
1174
matlab/dr1.m
1174
matlab/dr1.m
File diff suppressed because it is too large
Load Diff
|
|
@ -1,489 +1,489 @@
|
||||||
function [dr,info,M_,options_,oo_] = dr11_sparse(dr,task,M_,options_,oo_, jacobia_, hessian)
|
function [dr,info,M_,options_,oo_] = dr11_sparse(dr,task,M_,options_,oo_, jacobia_, hessian)
|
||||||
%function [dr,info,M_,options_,oo_] = dr11_sparse(dr,task,M_,options_,oo_, jacobia_, hessian)
|
%function [dr,info,M_,options_,oo_] = dr11_sparse(dr,task,M_,options_,oo_, jacobia_, hessian)
|
||||||
|
|
||||||
% Copyright (C) 2008 Dynare Team
|
% Copyright (C) 2008 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
% Dynare is free software: you can redistribute it and/or modify
|
||||||
% it under the terms of the GNU General Public License as published by
|
% it under the terms of the GNU General Public License as published by
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
% the Free Software Foundation, either version 3 of the License, or
|
||||||
% (at your option) any later version.
|
% (at your option) any later version.
|
||||||
%
|
%
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
% Dynare is distributed in the hope that it will be useful,
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
%task
|
%task
|
||||||
info = 0;
|
info = 0;
|
||||||
klen = M_.maximum_endo_lag + M_.maximum_endo_lead + 1;
|
klen = M_.maximum_endo_lag + M_.maximum_endo_lead + 1;
|
||||||
kstate = dr.kstate;
|
kstate = dr.kstate;
|
||||||
kad = dr.kad;
|
kad = dr.kad;
|
||||||
kae = dr.kae;
|
kae = dr.kae;
|
||||||
nstatic = dr.nstatic;
|
nstatic = dr.nstatic;
|
||||||
nfwrd = dr.nfwrd;
|
nfwrd = dr.nfwrd;
|
||||||
npred = dr.npred;
|
npred = dr.npred;
|
||||||
nboth = dr.nboth;
|
nboth = dr.nboth;
|
||||||
order_var = dr.order_var;
|
order_var = dr.order_var;
|
||||||
nd = size(kstate,1);
|
nd = size(kstate,1);
|
||||||
nz = nnz(M_.lead_lag_incidence);
|
nz = nnz(M_.lead_lag_incidence);
|
||||||
|
|
||||||
sdyn = M_.endo_nbr - nstatic;
|
sdyn = M_.endo_nbr - nstatic;
|
||||||
k0 = M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var);
|
k0 = M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var);
|
||||||
k1 = M_.lead_lag_incidence(find([1:klen] ~= M_.maximum_endo_lag+1),:);
|
k1 = M_.lead_lag_incidence(find([1:klen] ~= M_.maximum_endo_lag+1),:);
|
||||||
b = jacobia_(:,k0);
|
b = jacobia_(:,k0);
|
||||||
|
|
||||||
if M_.maximum_endo_lead == 0; % backward models
|
if M_.maximum_endo_lead == 0; % backward models
|
||||||
a = jacobia_(:,nonzeros(k1'));
|
a = jacobia_(:,nonzeros(k1'));
|
||||||
dr.ghx = zeros(size(a));
|
dr.ghx = zeros(size(a));
|
||||||
m = 0;
|
m = 0;
|
||||||
for i=M_.maximum_endo_lag:-1:1
|
for i=M_.maximum_endo_lag:-1:1
|
||||||
k = nonzeros(M_.lead_lag_incidence(i,order_var));
|
k = nonzeros(M_.lead_lag_incidence(i,order_var));
|
||||||
dr.ghx(:,m+[1:length(k)]) = -b\a(:,k);
|
dr.ghx(:,m+[1:length(k)]) = -b\a(:,k);
|
||||||
m = m+length(k);
|
m = m+length(k);
|
||||||
end
|
end
|
||||||
if M_.exo_nbr & task~=1
|
if M_.exo_nbr & task~=1
|
||||||
jacobia_
|
jacobia_
|
||||||
jacobia_(:,nz+1:end)
|
jacobia_(:,nz+1:end)
|
||||||
b
|
b
|
||||||
dr.ghu = -b\jacobia_(:,nz+1:end);
|
dr.ghu = -b\jacobia_(:,nz+1:end);
|
||||||
disp(['nz=' int2str(nz) ]);
|
disp(['nz=' int2str(nz) ]);
|
||||||
dr.ghu
|
dr.ghu
|
||||||
end
|
end
|
||||||
dr.eigval = eig(transition_matrix(dr,M_));
|
dr.eigval = eig(transition_matrix(dr,M_));
|
||||||
dr.rank = 0;
|
dr.rank = 0;
|
||||||
if any(abs(dr.eigval) > options_.qz_criterium)
|
if any(abs(dr.eigval) > options_.qz_criterium)
|
||||||
temp = sort(abs(dr.eigval));
|
temp = sort(abs(dr.eigval));
|
||||||
nba = nnz(abs(dr.eigval) > options_.qz_criterium);
|
nba = nnz(abs(dr.eigval) > options_.qz_criterium);
|
||||||
temp = temp(nd-nba+1:nd)-1-options_.qz_criterium;
|
temp = temp(nd-nba+1:nd)-1-options_.qz_criterium;
|
||||||
info(1) = 3;
|
info(1) = 3;
|
||||||
info(2) = temp'*temp;
|
info(2) = temp'*temp;
|
||||||
end
|
end
|
||||||
return;
|
return;
|
||||||
end
|
end
|
||||||
%forward--looking models
|
%forward--looking models
|
||||||
if nstatic > 0
|
if nstatic > 0
|
||||||
[Q,R] = qr(b(:,1:nstatic));
|
[Q,R] = qr(b(:,1:nstatic));
|
||||||
aa = Q'*jacobia_;
|
aa = Q'*jacobia_;
|
||||||
else
|
else
|
||||||
aa = jacobia_;
|
aa = jacobia_;
|
||||||
end
|
end
|
||||||
a = aa(:,nonzeros(k1'));
|
a = aa(:,nonzeros(k1'));
|
||||||
b = aa(:,k0);
|
b = aa(:,k0);
|
||||||
b10 = b(1:nstatic,1:nstatic);
|
b10 = b(1:nstatic,1:nstatic);
|
||||||
b11 = b(1:nstatic,nstatic+1:end);
|
b11 = b(1:nstatic,nstatic+1:end);
|
||||||
b2 = b(nstatic+1:end,nstatic+1:end);
|
b2 = b(nstatic+1:end,nstatic+1:end);
|
||||||
if any(isinf(a(:)))
|
if any(isinf(a(:)))
|
||||||
info = 1;
|
info = 1;
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
|
|
||||||
% buildind D and E
|
% buildind D and E
|
||||||
%nd
|
%nd
|
||||||
d = zeros(nd,nd) ;
|
d = zeros(nd,nd) ;
|
||||||
e = d ;
|
e = d ;
|
||||||
k = find(kstate(:,2) >= M_.maximum_endo_lag+2 & kstate(:,3));
|
k = find(kstate(:,2) >= M_.maximum_endo_lag+2 & kstate(:,3));
|
||||||
d(1:sdyn,k) = a(nstatic+1:end,kstate(k,3)) ;
|
d(1:sdyn,k) = a(nstatic+1:end,kstate(k,3)) ;
|
||||||
k1 = find(kstate(:,2) == M_.maximum_endo_lag+2);
|
k1 = find(kstate(:,2) == M_.maximum_endo_lag+2);
|
||||||
e(1:sdyn,k1) = -b2(:,kstate(k1,1)-nstatic);
|
e(1:sdyn,k1) = -b2(:,kstate(k1,1)-nstatic);
|
||||||
k = find(kstate(:,2) <= M_.maximum_endo_lag+1 & kstate(:,4));
|
k = find(kstate(:,2) <= M_.maximum_endo_lag+1 & kstate(:,4));
|
||||||
e(1:sdyn,k) = -a(nstatic+1:end,kstate(k,4)) ;
|
e(1:sdyn,k) = -a(nstatic+1:end,kstate(k,4)) ;
|
||||||
k2 = find(kstate(:,2) == M_.maximum_endo_lag+1);
|
k2 = find(kstate(:,2) == M_.maximum_endo_lag+1);
|
||||||
k2 = k2(~ismember(kstate(k2,1),kstate(k1,1)));
|
k2 = k2(~ismember(kstate(k2,1),kstate(k1,1)));
|
||||||
d(1:sdyn,k2) = b2(:,kstate(k2,1)-nstatic);
|
d(1:sdyn,k2) = b2(:,kstate(k2,1)-nstatic);
|
||||||
|
|
||||||
if ~isempty(kad)
|
if ~isempty(kad)
|
||||||
for j = 1:size(kad,1)
|
for j = 1:size(kad,1)
|
||||||
d(sdyn+j,kad(j)) = 1 ;
|
d(sdyn+j,kad(j)) = 1 ;
|
||||||
e(sdyn+j,kae(j)) = 1 ;
|
e(sdyn+j,kae(j)) = 1 ;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
%e
|
%e
|
||||||
%d
|
%d
|
||||||
|
|
||||||
[ss,tt,w,sdim,dr.eigval,info1] = mjdgges(e,d,options_.qz_criterium);
|
[ss,tt,w,sdim,dr.eigval,info1] = mjdgges(e,d,options_.qz_criterium);
|
||||||
|
|
||||||
|
|
||||||
if info1
|
if info1
|
||||||
info(1) = 2;
|
info(1) = 2;
|
||||||
info(2) = info1;
|
info(2) = info1;
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
|
|
||||||
nba = nd-sdim;
|
nba = nd-sdim;
|
||||||
|
|
||||||
nyf = sum(kstate(:,2) > M_.maximum_endo_lag+1);
|
nyf = sum(kstate(:,2) > M_.maximum_endo_lag+1);
|
||||||
|
|
||||||
if task == 1
|
if task == 1
|
||||||
dr.rank = rank(w(1:nyf,nd-nyf+1:end));
|
dr.rank = rank(w(1:nyf,nd-nyf+1:end));
|
||||||
% Under Octave, eig(A,B) doesn't exist, and
|
% Under Octave, eig(A,B) doesn't exist, and
|
||||||
% lambda = qz(A,B) won't return infinite eigenvalues
|
% lambda = qz(A,B) won't return infinite eigenvalues
|
||||||
if ~exist('OCTAVE_VERSION')
|
if ~exist('OCTAVE_VERSION')
|
||||||
dr.eigval = eig(e,d);
|
dr.eigval = eig(e,d);
|
||||||
end
|
end
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
|
|
||||||
if nba ~= nyf
|
if nba ~= nyf
|
||||||
temp = sort(abs(dr.eigval));
|
temp = sort(abs(dr.eigval));
|
||||||
if nba > nyf
|
if nba > nyf
|
||||||
temp = temp(nd-nba+1:nd-nyf)-1-options_.qz_criterium;
|
temp = temp(nd-nba+1:nd-nyf)-1-options_.qz_criterium;
|
||||||
info(1) = 3;
|
info(1) = 3;
|
||||||
elseif nba < nyf;
|
elseif nba < nyf;
|
||||||
temp = temp(nd-nyf+1:nd-nba)-1-options_.qz_criterium;
|
temp = temp(nd-nyf+1:nd-nba)-1-options_.qz_criterium;
|
||||||
info(1) = 4;
|
info(1) = 4;
|
||||||
end
|
end
|
||||||
info(2) = temp'*temp;
|
info(2) = temp'*temp;
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
|
|
||||||
np = nd - nyf;
|
np = nd - nyf;
|
||||||
n2 = np + 1;
|
n2 = np + 1;
|
||||||
n3 = nyf;
|
n3 = nyf;
|
||||||
n4 = n3 + 1;
|
n4 = n3 + 1;
|
||||||
% derivatives with respect to dynamic state variables
|
% derivatives with respect to dynamic state variables
|
||||||
% forward variables
|
% forward variables
|
||||||
w1 =w(1:n3,n2:nd);
|
w1 =w(1:n3,n2:nd);
|
||||||
if condest(w1) > 1e9;
|
if condest(w1) > 1e9;
|
||||||
info(1) = 5;
|
info(1) = 5;
|
||||||
info(2) = condest(w1);
|
info(2) = condest(w1);
|
||||||
return;
|
return;
|
||||||
else
|
else
|
||||||
gx = -w1'\w(n4:nd,n2:nd)';
|
gx = -w1'\w(n4:nd,n2:nd)';
|
||||||
end
|
end
|
||||||
|
|
||||||
% predetermined variables
|
% predetermined variables
|
||||||
hx = w(1:n3,1:np)'*gx+w(n4:nd,1:np)';
|
hx = w(1:n3,1:np)'*gx+w(n4:nd,1:np)';
|
||||||
hx = (tt(1:np,1:np)*hx)\(ss(1:np,1:np)*hx);
|
hx = (tt(1:np,1:np)*hx)\(ss(1:np,1:np)*hx);
|
||||||
|
|
||||||
k1 = find(kstate(n4:nd,2) == M_.maximum_endo_lag+1);
|
k1 = find(kstate(n4:nd,2) == M_.maximum_endo_lag+1);
|
||||||
k2 = find(kstate(1:n3,2) == M_.maximum_endo_lag+2);
|
k2 = find(kstate(1:n3,2) == M_.maximum_endo_lag+2);
|
||||||
hx(k1,:)
|
hx(k1,:)
|
||||||
gx(k2(nboth+1:end),:)
|
gx(k2(nboth+1:end),:)
|
||||||
dr.ghx = [hx(k1,:); gx(k2(nboth+1:end),:)];
|
dr.ghx = [hx(k1,:); gx(k2(nboth+1:end),:)];
|
||||||
dr.ghx
|
dr.ghx
|
||||||
%lead variables actually present in the model
|
%lead variables actually present in the model
|
||||||
j3 = nonzeros(kstate(:,3));
|
j3 = nonzeros(kstate(:,3));
|
||||||
j4 = find(kstate(:,3));
|
j4 = find(kstate(:,3));
|
||||||
% derivatives with respect to exogenous variables
|
% derivatives with respect to exogenous variables
|
||||||
disp(['M_.exo_nbr=' int2str(M_.exo_nbr)]);
|
disp(['M_.exo_nbr=' int2str(M_.exo_nbr)]);
|
||||||
if M_.exo_nbr
|
if M_.exo_nbr
|
||||||
fu = aa(:,nz+(1:M_.exo_nbr));
|
fu = aa(:,nz+(1:M_.exo_nbr));
|
||||||
a1 = b;
|
a1 = b;
|
||||||
aa1 = [];
|
aa1 = [];
|
||||||
if nstatic > 0
|
if nstatic > 0
|
||||||
aa1 = a1(:,1:nstatic);
|
aa1 = a1(:,1:nstatic);
|
||||||
end
|
end
|
||||||
dr.ghu = -[aa1 a(:,j3)*gx(j4,1:npred)+a1(:,nstatic+1:nstatic+ ...
|
dr.ghu = -[aa1 a(:,j3)*gx(j4,1:npred)+a1(:,nstatic+1:nstatic+ ...
|
||||||
npred) a1(:,nstatic+npred+1:end)]\fu;
|
npred) a1(:,nstatic+npred+1:end)]\fu;
|
||||||
else
|
else
|
||||||
dr.ghu = [];
|
dr.ghu = [];
|
||||||
end
|
end
|
||||||
|
|
||||||
% static variables
|
% static variables
|
||||||
if nstatic > 0
|
if nstatic > 0
|
||||||
temp = -a(1:nstatic,j3)*gx(j4,:)*hx;
|
temp = -a(1:nstatic,j3)*gx(j4,:)*hx;
|
||||||
j5 = find(kstate(n4:nd,4));
|
j5 = find(kstate(n4:nd,4));
|
||||||
temp(:,j5) = temp(:,j5)-a(1:nstatic,nonzeros(kstate(:,4)));
|
temp(:,j5) = temp(:,j5)-a(1:nstatic,nonzeros(kstate(:,4)));
|
||||||
temp = b10\(temp-b11*dr.ghx);
|
temp = b10\(temp-b11*dr.ghx);
|
||||||
dr.ghx = [temp; dr.ghx];
|
dr.ghx = [temp; dr.ghx];
|
||||||
temp = [];
|
temp = [];
|
||||||
end
|
end
|
||||||
|
|
||||||
if options_.loglinear == 1
|
if options_.loglinear == 1
|
||||||
k = find(dr.kstate(:,2) <= M_.maximum_endo_lag+1);
|
k = find(dr.kstate(:,2) <= M_.maximum_endo_lag+1);
|
||||||
klag = dr.kstate(k,[1 2]);
|
klag = dr.kstate(k,[1 2]);
|
||||||
k1 = dr.order_var;
|
k1 = dr.order_var;
|
||||||
|
|
||||||
dr.ghx = repmat(1./dr.ys(k1),1,size(dr.ghx,2)).*dr.ghx.* ...
|
dr.ghx = repmat(1./dr.ys(k1),1,size(dr.ghx,2)).*dr.ghx.* ...
|
||||||
repmat(dr.ys(k1(klag(:,1)))',size(dr.ghx,1),1);
|
repmat(dr.ys(k1(klag(:,1)))',size(dr.ghx,1),1);
|
||||||
dr.ghu = repmat(1./dr.ys(k1),1,size(dr.ghu,2)).*dr.ghu;
|
dr.ghu = repmat(1./dr.ys(k1),1,size(dr.ghu,2)).*dr.ghu;
|
||||||
end
|
end
|
||||||
|
|
||||||
%% Necessary when using Sims' routines for QZ
|
%% Necessary when using Sims' routines for QZ
|
||||||
if options_.use_qzdiv
|
if options_.use_qzdiv
|
||||||
gx = real(gx);
|
gx = real(gx);
|
||||||
hx = real(hx);
|
hx = real(hx);
|
||||||
dr.ghx = real(dr.ghx);
|
dr.ghx = real(dr.ghx);
|
||||||
dr.ghu = real(dr.ghu);
|
dr.ghu = real(dr.ghu);
|
||||||
end
|
end
|
||||||
|
|
||||||
%exogenous deterministic variables
|
%exogenous deterministic variables
|
||||||
if M_.exo_det_nbr > 0
|
if M_.exo_det_nbr > 0
|
||||||
f1 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+2:end,order_var))));
|
f1 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+2:end,order_var))));
|
||||||
f0 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var))));
|
f0 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var))));
|
||||||
fudet = sparse(jacobia_(:,nz+M_.exo_nbr+1:end));
|
fudet = sparse(jacobia_(:,nz+M_.exo_nbr+1:end));
|
||||||
M1 = inv(f0+[zeros(M_.endo_nbr,nstatic) f1*gx zeros(M_.endo_nbr,nyf-nboth)]);
|
M1 = inv(f0+[zeros(M_.endo_nbr,nstatic) f1*gx zeros(M_.endo_nbr,nyf-nboth)]);
|
||||||
M2 = M1*f1;
|
M2 = M1*f1;
|
||||||
dr.ghud = cell(M_.exo_det_length,1);
|
dr.ghud = cell(M_.exo_det_length,1);
|
||||||
dr.ghud{1} = -M1*fudet;
|
dr.ghud{1} = -M1*fudet;
|
||||||
for i = 2:M_.exo_det_length
|
for i = 2:M_.exo_det_length
|
||||||
dr.ghud{i} = -M2*dr.ghud{i-1}(end-nyf+1:end,:);
|
dr.ghud{i} = -M2*dr.ghud{i-1}(end-nyf+1:end,:);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if options_.order == 1
|
if options_.order == 1
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
|
|
||||||
% Second order
|
% Second order
|
||||||
%tempex = oo_.exo_simul ;
|
%tempex = oo_.exo_simul ;
|
||||||
|
|
||||||
%hessian = real(hessext('ff1_',[z; oo_.exo_steady_state]))' ;
|
%hessian = real(hessext('ff1_',[z; oo_.exo_steady_state]))' ;
|
||||||
kk = flipud(cumsum(flipud(M_.lead_lag_incidence(M_.maximum_endo_lag+1:end,order_var)),1));
|
kk = flipud(cumsum(flipud(M_.lead_lag_incidence(M_.maximum_endo_lag+1:end,order_var)),1));
|
||||||
if M_.maximum_endo_lag > 0
|
if M_.maximum_endo_lag > 0
|
||||||
kk = [cumsum(M_.lead_lag_incidence(1:M_.maximum_endo_lag,order_var),1); kk];
|
kk = [cumsum(M_.lead_lag_incidence(1:M_.maximum_endo_lag,order_var),1); kk];
|
||||||
end
|
end
|
||||||
kk = kk';
|
kk = kk';
|
||||||
kk = find(kk(:));
|
kk = find(kk(:));
|
||||||
nk = size(kk,1) + M_.exo_nbr + M_.exo_det_nbr;
|
nk = size(kk,1) + M_.exo_nbr + M_.exo_det_nbr;
|
||||||
k1 = M_.lead_lag_incidence(:,order_var);
|
k1 = M_.lead_lag_incidence(:,order_var);
|
||||||
k1 = k1';
|
k1 = k1';
|
||||||
k1 = k1(:);
|
k1 = k1(:);
|
||||||
k1 = k1(kk);
|
k1 = k1(kk);
|
||||||
k2 = find(k1);
|
k2 = find(k1);
|
||||||
kk1(k1(k2)) = k2;
|
kk1(k1(k2)) = k2;
|
||||||
kk1 = [kk1 length(k1)+1:length(k1)+M_.exo_nbr+M_.exo_det_nbr];
|
kk1 = [kk1 length(k1)+1:length(k1)+M_.exo_nbr+M_.exo_det_nbr];
|
||||||
kk = reshape([1:nk^2],nk,nk);
|
kk = reshape([1:nk^2],nk,nk);
|
||||||
kk1 = kk(kk1,kk1);
|
kk1 = kk(kk1,kk1);
|
||||||
%[junk,junk,hessian] = feval([M_.fname '_dynamic'],z, oo_.exo_steady_state);
|
%[junk,junk,hessian] = feval([M_.fname '_dynamic'],z, oo_.exo_steady_state);
|
||||||
hessian(:,kk1(:)) = hessian;
|
hessian(:,kk1(:)) = hessian;
|
||||||
|
|
||||||
%oo_.exo_simul = tempex ;
|
%oo_.exo_simul = tempex ;
|
||||||
%clear tempex
|
%clear tempex
|
||||||
|
|
||||||
n1 = 0;
|
n1 = 0;
|
||||||
n2 = np;
|
n2 = np;
|
||||||
zx = zeros(np,np);
|
zx = zeros(np,np);
|
||||||
zu=zeros(np,M_.exo_nbr);
|
zu=zeros(np,M_.exo_nbr);
|
||||||
for i=2:M_.maximum_endo_lag+1
|
for i=2:M_.maximum_endo_lag+1
|
||||||
k1 = sum(kstate(:,2) == i);
|
k1 = sum(kstate(:,2) == i);
|
||||||
zx(n1+1:n1+k1,n2-k1+1:n2)=eye(k1);
|
zx(n1+1:n1+k1,n2-k1+1:n2)=eye(k1);
|
||||||
n1 = n1+k1;
|
n1 = n1+k1;
|
||||||
n2 = n2-k1;
|
n2 = n2-k1;
|
||||||
end
|
end
|
||||||
kk = flipud(cumsum(flipud(M_.lead_lag_incidence(M_.maximum_endo_lag+1:end,order_var)),1));
|
kk = flipud(cumsum(flipud(M_.lead_lag_incidence(M_.maximum_endo_lag+1:end,order_var)),1));
|
||||||
k0 = [1:M_.endo_nbr];
|
k0 = [1:M_.endo_nbr];
|
||||||
gx1 = dr.ghx;
|
gx1 = dr.ghx;
|
||||||
hu = dr.ghu(nstatic+[1:npred],:);
|
hu = dr.ghu(nstatic+[1:npred],:);
|
||||||
zx = [zx; gx1];
|
zx = [zx; gx1];
|
||||||
zu = [zu; dr.ghu];
|
zu = [zu; dr.ghu];
|
||||||
for i=1:M_.maximum_endo_lead
|
for i=1:M_.maximum_endo_lead
|
||||||
k1 = find(kk(i+1,k0) > 0);
|
k1 = find(kk(i+1,k0) > 0);
|
||||||
zu = [zu; gx1(k1,1:npred)*hu];
|
zu = [zu; gx1(k1,1:npred)*hu];
|
||||||
gx1 = gx1(k1,:)*hx;
|
gx1 = gx1(k1,:)*hx;
|
||||||
zx = [zx; gx1];
|
zx = [zx; gx1];
|
||||||
kk = kk(:,k0);
|
kk = kk(:,k0);
|
||||||
k0 = k1;
|
k0 = k1;
|
||||||
end
|
end
|
||||||
zx=[zx; zeros(M_.exo_nbr,np);zeros(M_.exo_det_nbr,np)];
|
zx=[zx; zeros(M_.exo_nbr,np);zeros(M_.exo_det_nbr,np)];
|
||||||
zu=[zu; eye(M_.exo_nbr);zeros(M_.exo_det_nbr,M_.exo_nbr)];
|
zu=[zu; eye(M_.exo_nbr);zeros(M_.exo_det_nbr,M_.exo_nbr)];
|
||||||
[nrzx,nczx] = size(zx);
|
[nrzx,nczx] = size(zx);
|
||||||
|
|
||||||
rhs = -sparse_hessian_times_B_kronecker_C(hessian,zx);
|
rhs = -sparse_hessian_times_B_kronecker_C(hessian,zx);
|
||||||
|
|
||||||
%lhs
|
%lhs
|
||||||
n = M_.endo_nbr+sum(kstate(:,2) > M_.maximum_endo_lag+1 & kstate(:,2) < M_.maximum_endo_lag+M_.maximum_endo_lead+1);
|
n = M_.endo_nbr+sum(kstate(:,2) > M_.maximum_endo_lag+1 & kstate(:,2) < M_.maximum_endo_lag+M_.maximum_endo_lead+1);
|
||||||
A = zeros(n,n);
|
A = zeros(n,n);
|
||||||
B = zeros(n,n);
|
B = zeros(n,n);
|
||||||
A(1:M_.endo_nbr,1:M_.endo_nbr) = jacobia_(:,M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var));
|
A(1:M_.endo_nbr,1:M_.endo_nbr) = jacobia_(:,M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var));
|
||||||
% variables with the highest lead
|
% variables with the highest lead
|
||||||
k1 = find(kstate(:,2) == M_.maximum_endo_lag+M_.maximum_endo_lead+1);
|
k1 = find(kstate(:,2) == M_.maximum_endo_lag+M_.maximum_endo_lead+1);
|
||||||
if M_.maximum_endo_lead > 1
|
if M_.maximum_endo_lead > 1
|
||||||
k2 = find(kstate(:,2) == M_.maximum_endo_lag+M_.maximum_endo_lead);
|
k2 = find(kstate(:,2) == M_.maximum_endo_lag+M_.maximum_endo_lead);
|
||||||
[junk,junk,k3] = intersect(kstate(k1,1),kstate(k2,1));
|
[junk,junk,k3] = intersect(kstate(k1,1),kstate(k2,1));
|
||||||
else
|
else
|
||||||
k2 = [1:M_.endo_nbr];
|
k2 = [1:M_.endo_nbr];
|
||||||
k3 = kstate(k1,1);
|
k3 = kstate(k1,1);
|
||||||
end
|
end
|
||||||
% Jacobian with respect to the variables with the highest lead
|
% Jacobian with respect to the variables with the highest lead
|
||||||
B(1:M_.endo_nbr,end-length(k2)+k3) = jacobia_(:,kstate(k1,3)+M_.endo_nbr);
|
B(1:M_.endo_nbr,end-length(k2)+k3) = jacobia_(:,kstate(k1,3)+M_.endo_nbr);
|
||||||
offset = M_.endo_nbr;
|
offset = M_.endo_nbr;
|
||||||
k0 = [1:M_.endo_nbr];
|
k0 = [1:M_.endo_nbr];
|
||||||
gx1 = dr.ghx;
|
gx1 = dr.ghx;
|
||||||
for i=1:M_.maximum_endo_lead-1
|
for i=1:M_.maximum_endo_lead-1
|
||||||
k1 = find(kstate(:,2) == M_.maximum_endo_lag+i+1);
|
k1 = find(kstate(:,2) == M_.maximum_endo_lag+i+1);
|
||||||
[k2,junk,k3] = find(kstate(k1,3));
|
[k2,junk,k3] = find(kstate(k1,3));
|
||||||
A(1:M_.endo_nbr,offset+k2) = jacobia_(:,k3+M_.endo_nbr);
|
A(1:M_.endo_nbr,offset+k2) = jacobia_(:,k3+M_.endo_nbr);
|
||||||
n1 = length(k1);
|
n1 = length(k1);
|
||||||
A(offset+[1:n1],nstatic+[1:npred]) = -gx1(kstate(k1,1),1:npred);
|
A(offset+[1:n1],nstatic+[1:npred]) = -gx1(kstate(k1,1),1:npred);
|
||||||
gx1 = gx1*hx;
|
gx1 = gx1*hx;
|
||||||
A(offset+[1:n1],offset+[1:n1]) = eye(n1);
|
A(offset+[1:n1],offset+[1:n1]) = eye(n1);
|
||||||
n0 = length(k0);
|
n0 = length(k0);
|
||||||
E = eye(n0);
|
E = eye(n0);
|
||||||
if i == 1
|
if i == 1
|
||||||
[junk,junk,k4]=intersect(kstate(k1,1),[1:M_.endo_nbr]);
|
[junk,junk,k4]=intersect(kstate(k1,1),[1:M_.endo_nbr]);
|
||||||
else
|
else
|
||||||
[junk,junk,k4]=intersect(kstate(k1,1),kstate(k0,1));
|
[junk,junk,k4]=intersect(kstate(k1,1),kstate(k0,1));
|
||||||
end
|
end
|
||||||
i1 = offset-n0+n1;
|
i1 = offset-n0+n1;
|
||||||
B(offset+[1:n1],offset-n0+[1:n0]) = -E(k4,:);
|
B(offset+[1:n1],offset-n0+[1:n0]) = -E(k4,:);
|
||||||
k0 = k1;
|
k0 = k1;
|
||||||
offset = offset + n1;
|
offset = offset + n1;
|
||||||
end
|
end
|
||||||
[junk,k1,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+M_.maximum_endo_lead+1,order_var));
|
[junk,k1,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+M_.maximum_endo_lead+1,order_var));
|
||||||
A(1:M_.endo_nbr,nstatic+1:nstatic+npred)=...
|
A(1:M_.endo_nbr,nstatic+1:nstatic+npred)=...
|
||||||
A(1:M_.endo_nbr,nstatic+[1:npred])+jacobia_(:,k2)*gx1(k1,1:npred);
|
A(1:M_.endo_nbr,nstatic+[1:npred])+jacobia_(:,k2)*gx1(k1,1:npred);
|
||||||
C = hx;
|
C = hx;
|
||||||
D = [rhs; zeros(n-M_.endo_nbr,size(rhs,2))];
|
D = [rhs; zeros(n-M_.endo_nbr,size(rhs,2))];
|
||||||
|
|
||||||
|
|
||||||
dr.ghxx = gensylv(2,A,B,C,D);
|
dr.ghxx = gensylv(2,A,B,C,D);
|
||||||
|
|
||||||
%ghxu
|
%ghxu
|
||||||
%rhs
|
%rhs
|
||||||
hu = dr.ghu(nstatic+1:nstatic+npred,:);
|
hu = dr.ghu(nstatic+1:nstatic+npred,:);
|
||||||
%kk = reshape([1:np*np],np,np);
|
%kk = reshape([1:np*np],np,np);
|
||||||
%kk = kk(1:npred,1:npred);
|
%kk = kk(1:npred,1:npred);
|
||||||
%rhs = -hessian*kron(zx,zu)-f1*dr.ghxx(end-nyf+1:end,kk(:))*kron(hx(1:npred,:),hu(1:npred,:));
|
%rhs = -hessian*kron(zx,zu)-f1*dr.ghxx(end-nyf+1:end,kk(:))*kron(hx(1:npred,:),hu(1:npred,:));
|
||||||
|
|
||||||
rhs = sparse_hessian_times_B_kronecker_C(hessian,zx,zu);
|
rhs = sparse_hessian_times_B_kronecker_C(hessian,zx,zu);
|
||||||
|
|
||||||
nyf1 = sum(kstate(:,2) == M_.maximum_endo_lag+2);
|
nyf1 = sum(kstate(:,2) == M_.maximum_endo_lag+2);
|
||||||
hu1 = [hu;zeros(np-npred,M_.exo_nbr)];
|
hu1 = [hu;zeros(np-npred,M_.exo_nbr)];
|
||||||
%B1 = [B(1:M_.endo_nbr,:);zeros(size(A,1)-M_.endo_nbr,size(B,2))];
|
%B1 = [B(1:M_.endo_nbr,:);zeros(size(A,1)-M_.endo_nbr,size(B,2))];
|
||||||
[nrhx,nchx] = size(hx);
|
[nrhx,nchx] = size(hx);
|
||||||
[nrhu1,nchu1] = size(hu1);
|
[nrhu1,nchu1] = size(hu1);
|
||||||
|
|
||||||
B1 = B*A_times_B_kronecker_C(dr.ghxx,hx,hu1);
|
B1 = B*A_times_B_kronecker_C(dr.ghxx,hx,hu1);
|
||||||
rhs = -[rhs; zeros(n-M_.endo_nbr,size(rhs,2))]-B1;
|
rhs = -[rhs; zeros(n-M_.endo_nbr,size(rhs,2))]-B1;
|
||||||
|
|
||||||
|
|
||||||
%lhs
|
%lhs
|
||||||
dr.ghxu = A\rhs;
|
dr.ghxu = A\rhs;
|
||||||
|
|
||||||
%ghuu
|
%ghuu
|
||||||
%rhs
|
%rhs
|
||||||
kk = reshape([1:np*np],np,np);
|
kk = reshape([1:np*np],np,np);
|
||||||
kk = kk(1:npred,1:npred);
|
kk = kk(1:npred,1:npred);
|
||||||
|
|
||||||
rhs = sparse_hessian_times_B_kronecker_C(hessian,zu);
|
rhs = sparse_hessian_times_B_kronecker_C(hessian,zu);
|
||||||
|
|
||||||
|
|
||||||
B1 = A_times_B_kronecker_C(B*dr.ghxx,hu1);
|
B1 = A_times_B_kronecker_C(B*dr.ghxx,hu1);
|
||||||
rhs = -[rhs; zeros(n-M_.endo_nbr,size(rhs,2))]-B1;
|
rhs = -[rhs; zeros(n-M_.endo_nbr,size(rhs,2))]-B1;
|
||||||
|
|
||||||
%lhs
|
%lhs
|
||||||
dr.ghuu = A\rhs;
|
dr.ghuu = A\rhs;
|
||||||
|
|
||||||
dr.ghxx = dr.ghxx(1:M_.endo_nbr,:);
|
dr.ghxx = dr.ghxx(1:M_.endo_nbr,:);
|
||||||
dr.ghxu = dr.ghxu(1:M_.endo_nbr,:);
|
dr.ghxu = dr.ghxu(1:M_.endo_nbr,:);
|
||||||
dr.ghuu = dr.ghuu(1:M_.endo_nbr,:);
|
dr.ghuu = dr.ghuu(1:M_.endo_nbr,:);
|
||||||
|
|
||||||
|
|
||||||
% dr.ghs2
|
% dr.ghs2
|
||||||
% derivatives of F with respect to forward variables
|
% derivatives of F with respect to forward variables
|
||||||
% reordering predetermined variables in diminishing lag order
|
% reordering predetermined variables in diminishing lag order
|
||||||
O1 = zeros(M_.endo_nbr,nstatic);
|
O1 = zeros(M_.endo_nbr,nstatic);
|
||||||
O2 = zeros(M_.endo_nbr,M_.endo_nbr-nstatic-npred);
|
O2 = zeros(M_.endo_nbr,M_.endo_nbr-nstatic-npred);
|
||||||
LHS = jacobia_(:,M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var));
|
LHS = jacobia_(:,M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var));
|
||||||
RHS = zeros(M_.endo_nbr,M_.exo_nbr^2);
|
RHS = zeros(M_.endo_nbr,M_.exo_nbr^2);
|
||||||
kk = find(kstate(:,2) == M_.maximum_endo_lag+2);
|
kk = find(kstate(:,2) == M_.maximum_endo_lag+2);
|
||||||
gu = dr.ghu;
|
gu = dr.ghu;
|
||||||
guu = dr.ghuu;
|
guu = dr.ghuu;
|
||||||
Gu = [dr.ghu(nstatic+[1:npred],:); zeros(np-npred,M_.exo_nbr)];
|
Gu = [dr.ghu(nstatic+[1:npred],:); zeros(np-npred,M_.exo_nbr)];
|
||||||
Guu = [dr.ghuu(nstatic+[1:npred],:); zeros(np-npred,M_.exo_nbr*M_.exo_nbr)];
|
Guu = [dr.ghuu(nstatic+[1:npred],:); zeros(np-npred,M_.exo_nbr*M_.exo_nbr)];
|
||||||
E = eye(M_.endo_nbr);
|
E = eye(M_.endo_nbr);
|
||||||
M_.lead_lag_incidenceordered = flipud(cumsum(flipud(M_.lead_lag_incidence(M_.maximum_endo_lag+1:end,order_var)),1));
|
M_.lead_lag_incidenceordered = flipud(cumsum(flipud(M_.lead_lag_incidence(M_.maximum_endo_lag+1:end,order_var)),1));
|
||||||
if M_.maximum_endo_lag > 0
|
if M_.maximum_endo_lag > 0
|
||||||
M_.lead_lag_incidenceordered = [cumsum(M_.lead_lag_incidence(1:M_.maximum_endo_lag,order_var),1); M_.lead_lag_incidenceordered];
|
M_.lead_lag_incidenceordered = [cumsum(M_.lead_lag_incidence(1:M_.maximum_endo_lag,order_var),1); M_.lead_lag_incidenceordered];
|
||||||
end
|
end
|
||||||
M_.lead_lag_incidenceordered = M_.lead_lag_incidenceordered';
|
M_.lead_lag_incidenceordered = M_.lead_lag_incidenceordered';
|
||||||
M_.lead_lag_incidenceordered = M_.lead_lag_incidenceordered(:);
|
M_.lead_lag_incidenceordered = M_.lead_lag_incidenceordered(:);
|
||||||
k1 = find(M_.lead_lag_incidenceordered);
|
k1 = find(M_.lead_lag_incidenceordered);
|
||||||
M_.lead_lag_incidenceordered(k1) = [1:length(k1)]';
|
M_.lead_lag_incidenceordered(k1) = [1:length(k1)]';
|
||||||
M_.lead_lag_incidenceordered =reshape(M_.lead_lag_incidenceordered,M_.endo_nbr,M_.maximum_endo_lag+M_.maximum_endo_lead+1)';
|
M_.lead_lag_incidenceordered =reshape(M_.lead_lag_incidenceordered,M_.endo_nbr,M_.maximum_endo_lag+M_.maximum_endo_lead+1)';
|
||||||
kh = reshape([1:nk^2],nk,nk);
|
kh = reshape([1:nk^2],nk,nk);
|
||||||
kp = sum(kstate(:,2) <= M_.maximum_endo_lag+1);
|
kp = sum(kstate(:,2) <= M_.maximum_endo_lag+1);
|
||||||
E1 = [eye(npred); zeros(kp-npred,npred)];
|
E1 = [eye(npred); zeros(kp-npred,npred)];
|
||||||
H = E1;
|
H = E1;
|
||||||
hxx = dr.ghxx(nstatic+[1:npred],:);
|
hxx = dr.ghxx(nstatic+[1:npred],:);
|
||||||
for i=1:M_.maximum_endo_lead
|
for i=1:M_.maximum_endo_lead
|
||||||
for j=i:M_.maximum_endo_lead
|
for j=i:M_.maximum_endo_lead
|
||||||
[junk,k2a,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+j+1,order_var));
|
[junk,k2a,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+j+1,order_var));
|
||||||
[junk,k3a,k3] = ...
|
[junk,k3a,k3] = ...
|
||||||
find(M_.lead_lag_incidenceordered(M_.maximum_endo_lag+j+1,:));
|
find(M_.lead_lag_incidenceordered(M_.maximum_endo_lag+j+1,:));
|
||||||
nk3a = length(k3a);
|
nk3a = length(k3a);
|
||||||
B1 = sparse_hessian_times_B_kronecker_C(hessian(:,kh(k3,k3)),gu(k3a,:));
|
B1 = sparse_hessian_times_B_kronecker_C(hessian(:,kh(k3,k3)),gu(k3a,:));
|
||||||
RHS = RHS + jacobia_(:,k2)*guu(k2a,:)+B1;
|
RHS = RHS + jacobia_(:,k2)*guu(k2a,:)+B1;
|
||||||
end
|
end
|
||||||
% LHS
|
% LHS
|
||||||
[junk,k2a,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+i+1,order_var));
|
[junk,k2a,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+i+1,order_var));
|
||||||
LHS = LHS + jacobia_(:,k2)*(E(k2a,:)+[O1(k2a,:) dr.ghx(k2a,:)*H O2(k2a,:)]);
|
LHS = LHS + jacobia_(:,k2)*(E(k2a,:)+[O1(k2a,:) dr.ghx(k2a,:)*H O2(k2a,:)]);
|
||||||
|
|
||||||
if i == M_.maximum_endo_lead
|
if i == M_.maximum_endo_lead
|
||||||
break
|
break
|
||||||
end
|
end
|
||||||
|
|
||||||
kk = find(kstate(:,2) == M_.maximum_endo_lag+i+1);
|
kk = find(kstate(:,2) == M_.maximum_endo_lag+i+1);
|
||||||
gu = dr.ghx*Gu;
|
gu = dr.ghx*Gu;
|
||||||
[nrGu,ncGu] = size(Gu);
|
[nrGu,ncGu] = size(Gu);
|
||||||
G1 = A_times_B_kronecker_C(dr.ghxx,Gu);
|
G1 = A_times_B_kronecker_C(dr.ghxx,Gu);
|
||||||
G2 = A_times_B_kronecker_C(hxx,Gu);
|
G2 = A_times_B_kronecker_C(hxx,Gu);
|
||||||
guu = dr.ghx*Guu+G1;
|
guu = dr.ghx*Guu+G1;
|
||||||
Gu = hx*Gu;
|
Gu = hx*Gu;
|
||||||
Guu = hx*Guu;
|
Guu = hx*Guu;
|
||||||
Guu(end-npred+1:end,:) = Guu(end-npred+1:end,:) + G2;
|
Guu(end-npred+1:end,:) = Guu(end-npred+1:end,:) + G2;
|
||||||
H = E1 + hx*H;
|
H = E1 + hx*H;
|
||||||
end
|
end
|
||||||
RHS = RHS*M_.Sigma_e(:);
|
RHS = RHS*M_.Sigma_e(:);
|
||||||
dr.fuu = RHS;
|
dr.fuu = RHS;
|
||||||
%RHS = -RHS-dr.fbias;
|
%RHS = -RHS-dr.fbias;
|
||||||
RHS = -RHS;
|
RHS = -RHS;
|
||||||
dr.ghs2 = LHS\RHS;
|
dr.ghs2 = LHS\RHS;
|
||||||
|
|
||||||
% deterministic exogenous variables
|
% deterministic exogenous variables
|
||||||
if M_.exo_det_nbr > 0
|
if M_.exo_det_nbr > 0
|
||||||
hud = dr.ghud{1}(nstatic+1:nstatic+npred,:);
|
hud = dr.ghud{1}(nstatic+1:nstatic+npred,:);
|
||||||
zud=[zeros(np,M_.exo_det_nbr);dr.ghud{1};gx(:,1:npred)*hud;zeros(M_.exo_nbr,M_.exo_det_nbr);eye(M_.exo_det_nbr)];
|
zud=[zeros(np,M_.exo_det_nbr);dr.ghud{1};gx(:,1:npred)*hud;zeros(M_.exo_nbr,M_.exo_det_nbr);eye(M_.exo_det_nbr)];
|
||||||
R1 = hessian*kron(zx,zud);
|
R1 = hessian*kron(zx,zud);
|
||||||
dr.ghxud = cell(M_.exo_det_length,1);
|
dr.ghxud = cell(M_.exo_det_length,1);
|
||||||
kf = [M_.endo_nbr-nyf+1:M_.endo_nbr];
|
kf = [M_.endo_nbr-nyf+1:M_.endo_nbr];
|
||||||
kp = nstatic+[1:npred];
|
kp = nstatic+[1:npred];
|
||||||
dr.ghxud{1} = -M1*(R1+f1*dr.ghxx(kf,:)*kron(dr.ghx(kp,:),dr.ghud{1}(kp,:)));
|
dr.ghxud{1} = -M1*(R1+f1*dr.ghxx(kf,:)*kron(dr.ghx(kp,:),dr.ghud{1}(kp,:)));
|
||||||
Eud = eye(M_.exo_det_nbr);
|
Eud = eye(M_.exo_det_nbr);
|
||||||
for i = 2:M_.exo_det_length
|
for i = 2:M_.exo_det_length
|
||||||
hudi = dr.ghud{i}(kp,:);
|
hudi = dr.ghud{i}(kp,:);
|
||||||
zudi=[zeros(np,M_.exo_det_nbr);dr.ghud{i};gx(:,1:npred)*hudi;zeros(M_.exo_nbr+M_.exo_det_nbr,M_.exo_det_nbr)];
|
zudi=[zeros(np,M_.exo_det_nbr);dr.ghud{i};gx(:,1:npred)*hudi;zeros(M_.exo_nbr+M_.exo_det_nbr,M_.exo_det_nbr)];
|
||||||
R2 = hessian*kron(zx,zudi);
|
R2 = hessian*kron(zx,zudi);
|
||||||
dr.ghxud{i} = -M2*(dr.ghxud{i-1}(kf,:)*kron(hx,Eud)+dr.ghxx(kf,:)*kron(dr.ghx(kp,:),dr.ghud{i}(kp,:)))-M1*R2;
|
dr.ghxud{i} = -M2*(dr.ghxud{i-1}(kf,:)*kron(hx,Eud)+dr.ghxx(kf,:)*kron(dr.ghx(kp,:),dr.ghud{i}(kp,:)))-M1*R2;
|
||||||
end
|
end
|
||||||
R1 = hessian*kron(zu,zud);
|
R1 = hessian*kron(zu,zud);
|
||||||
dr.ghudud = cell(M_.exo_det_length,1);
|
dr.ghudud = cell(M_.exo_det_length,1);
|
||||||
kf = [M_.endo_nbr-nyf+1:M_.endo_nbr];
|
kf = [M_.endo_nbr-nyf+1:M_.endo_nbr];
|
||||||
|
|
||||||
dr.ghuud{1} = -M1*(R1+f1*dr.ghxx(kf,:)*kron(dr.ghu(kp,:),dr.ghud{1}(kp,:)));
|
dr.ghuud{1} = -M1*(R1+f1*dr.ghxx(kf,:)*kron(dr.ghu(kp,:),dr.ghud{1}(kp,:)));
|
||||||
Eud = eye(M_.exo_det_nbr);
|
Eud = eye(M_.exo_det_nbr);
|
||||||
for i = 2:M_.exo_det_length
|
for i = 2:M_.exo_det_length
|
||||||
hudi = dr.ghud{i}(kp,:);
|
hudi = dr.ghud{i}(kp,:);
|
||||||
zudi=[zeros(np,M_.exo_det_nbr);dr.ghud{i};gx(:,1:npred)*hudi;zeros(M_.exo_nbr+M_.exo_det_nbr,M_.exo_det_nbr)];
|
zudi=[zeros(np,M_.exo_det_nbr);dr.ghud{i};gx(:,1:npred)*hudi;zeros(M_.exo_nbr+M_.exo_det_nbr,M_.exo_det_nbr)];
|
||||||
R2 = hessian*kron(zu,zudi);
|
R2 = hessian*kron(zu,zudi);
|
||||||
dr.ghuud{i} = -M2*dr.ghxud{i-1}(kf,:)*kron(hu,Eud)-M1*R2;
|
dr.ghuud{i} = -M2*dr.ghxud{i-1}(kf,:)*kron(hu,Eud)-M1*R2;
|
||||||
end
|
end
|
||||||
R1 = hessian*kron(zud,zud);
|
R1 = hessian*kron(zud,zud);
|
||||||
dr.ghudud = cell(M_.exo_det_length,M_.exo_det_length);
|
dr.ghudud = cell(M_.exo_det_length,M_.exo_det_length);
|
||||||
dr.ghudud{1,1} = -M1*R1-M2*dr.ghxx(kf,:)*kron(hud,hud);
|
dr.ghudud{1,1} = -M1*R1-M2*dr.ghxx(kf,:)*kron(hud,hud);
|
||||||
for i = 2:M_.exo_det_length
|
for i = 2:M_.exo_det_length
|
||||||
hudi = dr.ghud{i}(nstatic+1:nstatic+npred,:);
|
hudi = dr.ghud{i}(nstatic+1:nstatic+npred,:);
|
||||||
zudi=[zeros(np,M_.exo_det_nbr);dr.ghud{i};gx(:,1:npred)*hudi+dr.ghud{i-1}(kf,:);zeros(M_.exo_nbr+M_.exo_det_nbr,M_.exo_det_nbr)];
|
zudi=[zeros(np,M_.exo_det_nbr);dr.ghud{i};gx(:,1:npred)*hudi+dr.ghud{i-1}(kf,:);zeros(M_.exo_nbr+M_.exo_det_nbr,M_.exo_det_nbr)];
|
||||||
R2 = hessian*kron(zudi,zudi);
|
R2 = hessian*kron(zudi,zudi);
|
||||||
dr.ghudud{i,i} = -M2*(dr.ghudud{i-1,i-1}(kf,:)+...
|
dr.ghudud{i,i} = -M2*(dr.ghudud{i-1,i-1}(kf,:)+...
|
||||||
2*dr.ghxud{i-1}(kf,:)*kron(hudi,Eud) ...
|
2*dr.ghxud{i-1}(kf,:)*kron(hudi,Eud) ...
|
||||||
+dr.ghxx(kf,:)*kron(hudi,hudi))-M1*R2;
|
+dr.ghxx(kf,:)*kron(hudi,hudi))-M1*R2;
|
||||||
R2 = hessian*kron(zud,zudi);
|
R2 = hessian*kron(zud,zudi);
|
||||||
dr.ghudud{1,i} = -M2*(dr.ghxud{i-1}(kf,:)*kron(hud,Eud)+...
|
dr.ghudud{1,i} = -M2*(dr.ghxud{i-1}(kf,:)*kron(hud,Eud)+...
|
||||||
dr.ghxx(kf,:)*kron(hud,hudi))...
|
dr.ghxx(kf,:)*kron(hud,hudi))...
|
||||||
-M1*R2;
|
-M1*R2;
|
||||||
for j=2:i-1
|
for j=2:i-1
|
||||||
hudj = dr.ghud{j}(kp,:);
|
hudj = dr.ghud{j}(kp,:);
|
||||||
zudj=[zeros(np,M_.exo_det_nbr);dr.ghud{j};gx(:,1:npred)*hudj;zeros(M_.exo_nbr+M_.exo_det_nbr,M_.exo_det_nbr)];
|
zudj=[zeros(np,M_.exo_det_nbr);dr.ghud{j};gx(:,1:npred)*hudj;zeros(M_.exo_nbr+M_.exo_det_nbr,M_.exo_det_nbr)];
|
||||||
R2 = hessian*kron(zudj,zudi);
|
R2 = hessian*kron(zudj,zudi);
|
||||||
dr.ghudud{j,i} = -M2*(dr.ghudud{j-1,i-1}(kf,:)+dr.ghxud{j-1}(kf,:)* ...
|
dr.ghudud{j,i} = -M2*(dr.ghudud{j-1,i-1}(kf,:)+dr.ghxud{j-1}(kf,:)* ...
|
||||||
kron(hudi,Eud)+dr.ghxud{i-1}(kf,:)* ...
|
kron(hudi,Eud)+dr.ghxud{i-1}(kf,:)* ...
|
||||||
kron(hudj,Eud)+dr.ghxx(kf,:)*kron(hudj,hudi))-M1*R2;
|
kron(hudj,Eud)+dr.ghxx(kf,:)*kron(hudj,hudi))-M1*R2;
|
||||||
end
|
end
|
||||||
|
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
@ -47,47 +47,47 @@ function [dr,info,M_,options_,oo_] = dr1_sparse(dr,task,M_,options_,oo_)
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
info = 0;
|
info = 0;
|
||||||
|
|
||||||
options_ = set_default_option(options_,'loglinear',0);
|
options_ = set_default_option(options_,'loglinear',0);
|
||||||
options_ = set_default_option(options_,'noprint',0);
|
options_ = set_default_option(options_,'noprint',0);
|
||||||
options_ = set_default_option(options_,'olr',0);
|
options_ = set_default_option(options_,'olr',0);
|
||||||
options_ = set_default_option(options_,'olr_beta',1);
|
options_ = set_default_option(options_,'olr_beta',1);
|
||||||
options_ = set_default_option(options_,'qz_criterium',1.000001);
|
options_ = set_default_option(options_,'qz_criterium',1.000001);
|
||||||
|
|
||||||
xlen = M_.maximum_endo_lead + M_.maximum_endo_lag + 1;
|
xlen = M_.maximum_endo_lead + M_.maximum_endo_lag + 1;
|
||||||
klen = M_.maximum_endo_lag + M_.maximum_endo_lead + 1;
|
klen = M_.maximum_endo_lag + M_.maximum_endo_lead + 1;
|
||||||
iyv = M_.lead_lag_incidence';
|
iyv = M_.lead_lag_incidence';
|
||||||
iyv = iyv(:);
|
iyv = iyv(:);
|
||||||
iyr0 = find(iyv) ;
|
iyr0 = find(iyv) ;
|
||||||
it_ = M_.maximum_lag + 1 ;
|
it_ = M_.maximum_lag + 1 ;
|
||||||
|
|
||||||
if M_.exo_nbr == 0
|
if M_.exo_nbr == 0
|
||||||
oo_.exo_steady_state = [] ;
|
oo_.exo_steady_state = [] ;
|
||||||
|
end
|
||||||
|
|
||||||
|
% expanding system for Optimal Linear Regulator
|
||||||
|
if options_.ramsey_policy
|
||||||
|
if isfield(M_,'orig_model')
|
||||||
|
orig_model = M_.orig_model;
|
||||||
|
M_.endo_nbr = orig_model.endo_nbr;
|
||||||
|
M_.orig_endo_nbr = orig_model.orig_endo_nbr;
|
||||||
|
M_.aux_vars = orig_model.aux_vars;
|
||||||
|
M_.endo_names = orig_model.endo_names;
|
||||||
|
M_.lead_lag_incidence = orig_model.lead_lag_incidence;
|
||||||
|
M_.maximum_lead = orig_model.maximum_lead;
|
||||||
|
M_.maximum_endo_lead = orig_model.maximum_endo_lead;
|
||||||
|
M_.maximum_lag = orig_model.maximum_lag;
|
||||||
|
M_.maximum_endo_lag = orig_model.maximum_endo_lag;
|
||||||
end
|
end
|
||||||
|
old_solve_algo = options_.solve_algo;
|
||||||
% expanding system for Optimal Linear Regulator
|
% options_.solve_algo = 1;
|
||||||
if options_.ramsey_policy
|
oo_.steady_state = dynare_solve('dyn_ramsey_static_',oo_.steady_state,0,M_,options_,oo_,it_);
|
||||||
if isfield(M_,'orig_model')
|
options_.solve_algo = old_solve_algo;
|
||||||
orig_model = M_.orig_model;
|
[junk,junk,multbar] = dyn_ramsey_static_(oo_.steady_state,M_,options_,oo_,it_);
|
||||||
M_.endo_nbr = orig_model.endo_nbr;
|
[jacobia_,M_] = dyn_ramsey_dynamic_(oo_.steady_state,multbar,M_,options_,oo_,it_);
|
||||||
M_.orig_endo_nbr = orig_model.orig_endo_nbr;
|
klen = M_.maximum_lag + M_.maximum_lead + 1;
|
||||||
M_.aux_vars = orig_model.aux_vars;
|
dr.ys = [oo_.steady_state;zeros(M_.exo_nbr,1);multbar];
|
||||||
M_.endo_names = orig_model.endo_names;
|
|
||||||
M_.lead_lag_incidence = orig_model.lead_lag_incidence;
|
|
||||||
M_.maximum_lead = orig_model.maximum_lead;
|
|
||||||
M_.maximum_endo_lead = orig_model.maximum_endo_lead;
|
|
||||||
M_.maximum_lag = orig_model.maximum_lag;
|
|
||||||
M_.maximum_endo_lag = orig_model.maximum_endo_lag;
|
|
||||||
end
|
|
||||||
old_solve_algo = options_.solve_algo;
|
|
||||||
% options_.solve_algo = 1;
|
|
||||||
oo_.steady_state = dynare_solve('dyn_ramsey_static_',oo_.steady_state,0,M_,options_,oo_,it_);
|
|
||||||
options_.solve_algo = old_solve_algo;
|
|
||||||
[junk,junk,multbar] = dyn_ramsey_static_(oo_.steady_state,M_,options_,oo_,it_);
|
|
||||||
[jacobia_,M_] = dyn_ramsey_dynamic_(oo_.steady_state,multbar,M_,options_,oo_,it_);
|
|
||||||
klen = M_.maximum_lag + M_.maximum_lead + 1;
|
|
||||||
dr.ys = [oo_.steady_state;zeros(M_.exo_nbr,1);multbar];
|
|
||||||
% $$$ if options_.ramsey_policy == 2
|
% $$$ if options_.ramsey_policy == 2
|
||||||
% $$$ mask = M_.orig_model.lead_lag_incidence ~= 0;
|
% $$$ mask = M_.orig_model.lead_lag_incidence ~= 0;
|
||||||
% $$$ incidence_submatrix = M_.lead_lag_incidence(M_.orig_model.maximum_lead+(1:size(mask,1)),1:M_.orig_model.endo_nbr);
|
% $$$ incidence_submatrix = M_.lead_lag_incidence(M_.orig_model.maximum_lead+(1:size(mask,1)),1:M_.orig_model.endo_nbr);
|
||||||
|
|
@ -178,91 +178,91 @@ function [dr,info,M_,options_,oo_] = dr1_sparse(dr,task,M_,options_,oo_)
|
||||||
% $$$ M_.endo_names = strvcat(M_.orig_model.endo_names, M_.endo_names(endo_nbr1+k,:));
|
% $$$ M_.endo_names = strvcat(M_.orig_model.endo_names, M_.endo_names(endo_nbr1+k,:));
|
||||||
% $$$ M_.endo_nbr = endo_nbr1+length(k);
|
% $$$ M_.endo_nbr = endo_nbr1+length(k);
|
||||||
% $$$ end
|
% $$$ end
|
||||||
else
|
else
|
||||||
klen = M_.maximum_lag + M_.maximum_lead + 1;
|
klen = M_.maximum_lag + M_.maximum_lead + 1;
|
||||||
iyv = M_.lead_lag_incidence';
|
iyv = M_.lead_lag_incidence';
|
||||||
iyv = iyv(:);
|
iyv = iyv(:);
|
||||||
iyr0 = find(iyv) ;
|
iyr0 = find(iyv) ;
|
||||||
it_ = M_.maximum_lag + 1 ;
|
it_ = M_.maximum_lag + 1 ;
|
||||||
|
|
||||||
if M_.exo_nbr == 0
|
if M_.exo_nbr == 0
|
||||||
oo_.exo_steady_state = [] ;
|
oo_.exo_steady_state = [] ;
|
||||||
end
|
|
||||||
|
|
||||||
it_ = M_.maximum_lag + 1;
|
|
||||||
z = repmat(dr.ys,1,klen);
|
|
||||||
z = z(iyr0) ;
|
|
||||||
if options_.model_mode==0 || options_.model_mode == 2
|
|
||||||
if options_.order == 1
|
|
||||||
[junk,jacobia_] = feval([M_.fname '_dynamic'],z,[oo_.exo_simul ...
|
|
||||||
oo_.exo_det_simul], M_.params, it_);
|
|
||||||
hessian = 0;
|
|
||||||
elseif options_.order == 2
|
|
||||||
[junk,jacobia_,hessian] = feval([M_.fname '_dynamic'],z,...
|
|
||||||
[oo_.exo_simul ...
|
|
||||||
oo_.exo_det_simul], M_.params, it_);
|
|
||||||
end
|
|
||||||
dr=set_state_space(dr,M_);
|
|
||||||
if options_.debug
|
|
||||||
save([M_.fname '_debug.mat'],'jacobia_')
|
|
||||||
end
|
|
||||||
[dr,info,M_,options_,oo_] = dr11_sparse(dr,task,M_,options_,oo_, jacobia_, hessian);
|
|
||||||
dr.nyf = nnz(dr.kstate(:,2)>M_.maximum_lag+1);
|
|
||||||
elseif options_.model_mode==1
|
|
||||||
if options_.order == 1
|
|
||||||
|
|
||||||
[junk,derivate] = feval([M_.fname '_dynamic'],ones(M_.maximum_lag+M_.maximum_lead+1,1)*dr.ys',[oo_.exo_simul ...
|
|
||||||
oo_.exo_det_simul], M_.params, it_);
|
|
||||||
%full(jacobia_)
|
|
||||||
dr.eigval = [];
|
|
||||||
dr.nyf = 0;
|
|
||||||
dr.rank = 0;
|
|
||||||
first_col_exo = M_.endo_nbr * (M_.maximum_endo_lag + M_.maximum_endo_lead + 1);
|
|
||||||
for i=1:length(M_.block_structure.block)
|
|
||||||
%disp(['block = ' int2str(i)]);
|
|
||||||
M_.block_structure.block(i).dr.Null=0;
|
|
||||||
M_.block_structure.block(i).dr=set_state_space(M_.block_structure.block(i).dr,M_.block_structure.block(i));
|
|
||||||
col_selector=repmat(M_.block_structure.block(i).variable,1,M_.block_structure.block(i).maximum_endo_lag+M_.block_structure.block(i).maximum_endo_lead+1)+kron([M_.maximum_endo_lag-M_.block_structure.block(i).maximum_endo_lag:M_.maximum_endo_lag+M_.block_structure.block(i).maximum_endo_lead],M_.endo_nbr*ones(1,M_.block_structure.block(i).endo_nbr));
|
|
||||||
row_selector = M_.block_structure.block(i).equation;
|
|
||||||
%jcb_=jacobia_(row_selector,col_selector);
|
|
||||||
jcb_=derivate(i).g1;
|
|
||||||
%disp('jcb_');
|
|
||||||
%full(jcb_)
|
|
||||||
%M_.block_structure.block(i).lead_lag_incidence'
|
|
||||||
jcb_ = jcb_(:,find(M_.block_structure.block(i).lead_lag_incidence')) ;
|
|
||||||
if M_.block_structure.block(i).exo_nbr>0
|
|
||||||
col_selector = [ first_col_exo + ...
|
|
||||||
repmat(M_.block_structure.block(i).exogenous,1,M_.block_structure.block(i).maximum_exo_lag+M_.block_structure.block(i).maximum_exo_lead+1)+kron([M_.maximum_exo_lag-M_.block_structure.block(i).maximum_exo_lag:M_.maximum_exo_lag+M_.block_structure.block(i).maximum_exo_lead],M_.exo_nbr*ones(1,M_.block_structure.block(i).exo_nbr))];
|
|
||||||
end
|
|
||||||
%derivate(i).g1
|
|
||||||
%derivate(i).g1_x
|
|
||||||
%col_selector
|
|
||||||
%jcb_ = [ jcb_ jacobia_(row_selector,col_selector)];
|
|
||||||
jcb_ = [ jcb_ derivate(i).g1_x];
|
|
||||||
%full(jcb_)
|
|
||||||
|
|
||||||
hss_=0; %hessian(M_.block_structure.block(i).equation,M_.block_structure.block(i).variable);
|
|
||||||
dra = M_.block_structure.block(i).dr;
|
|
||||||
%M_.block_structure.block(i).exo_nbr=M_.exo_nbr;
|
|
||||||
[dra ,info,M_.block_structure.block(i),options_,oo_] = dr11_sparse(dra ,task,M_.block_structure.block(i),options_,oo_, jcb_, hss_);
|
|
||||||
M_.block_structure.block(i).dr = dra;
|
|
||||||
dr.eigval = [dr.eigval; dra.eigval];
|
|
||||||
nyf = nnz(dra.kstate(:,2)>M_.block_structure.block(i).maximum_endo_lag+1);
|
|
||||||
n_explod = nnz(abs(dra.eigval) > options_.qz_criterium);
|
|
||||||
if nyf ~= n_explod
|
|
||||||
disp(['EIGENVALUES in block ' int2str(i) ':']);
|
|
||||||
[m_lambda,ii]=sort(abs(dra.eigval));
|
|
||||||
disp(sprintf('%16s %16s %16s\n','Modulus','Real','Imaginary'))
|
|
||||||
z=[m_lambda real(dra.eigval(ii)) imag(dra.eigval(ii))]';
|
|
||||||
disp(sprintf('%16.4g %16.4g %16.4g\n',z))
|
|
||||||
disp(['The rank condition is not satisfy in block ' int2str(i) ' :']);
|
|
||||||
disp([' ' int2str(nyf) ' forward-looking variable(s) for ' ...
|
|
||||||
int2str(n_explod) ' eigenvalue(s) larger than 1 in modulus']);
|
|
||||||
end
|
|
||||||
dr.nyf = dr.nyf + nyf;
|
|
||||||
dr.rank = dr.rank + dra.rank;
|
|
||||||
end;
|
|
||||||
end
|
|
||||||
end
|
|
||||||
end
|
end
|
||||||
|
|
||||||
|
it_ = M_.maximum_lag + 1;
|
||||||
|
z = repmat(dr.ys,1,klen);
|
||||||
|
z = z(iyr0) ;
|
||||||
|
if options_.model_mode==0 || options_.model_mode == 2
|
||||||
|
if options_.order == 1
|
||||||
|
[junk,jacobia_] = feval([M_.fname '_dynamic'],z,[oo_.exo_simul ...
|
||||||
|
oo_.exo_det_simul], M_.params, it_);
|
||||||
|
hessian = 0;
|
||||||
|
elseif options_.order == 2
|
||||||
|
[junk,jacobia_,hessian] = feval([M_.fname '_dynamic'],z,...
|
||||||
|
[oo_.exo_simul ...
|
||||||
|
oo_.exo_det_simul], M_.params, it_);
|
||||||
|
end
|
||||||
|
dr=set_state_space(dr,M_);
|
||||||
|
if options_.debug
|
||||||
|
save([M_.fname '_debug.mat'],'jacobia_')
|
||||||
|
end
|
||||||
|
[dr,info,M_,options_,oo_] = dr11_sparse(dr,task,M_,options_,oo_, jacobia_, hessian);
|
||||||
|
dr.nyf = nnz(dr.kstate(:,2)>M_.maximum_lag+1);
|
||||||
|
elseif options_.model_mode==1
|
||||||
|
if options_.order == 1
|
||||||
|
|
||||||
|
[junk,derivate] = feval([M_.fname '_dynamic'],ones(M_.maximum_lag+M_.maximum_lead+1,1)*dr.ys',[oo_.exo_simul ...
|
||||||
|
oo_.exo_det_simul], M_.params, it_);
|
||||||
|
%full(jacobia_)
|
||||||
|
dr.eigval = [];
|
||||||
|
dr.nyf = 0;
|
||||||
|
dr.rank = 0;
|
||||||
|
first_col_exo = M_.endo_nbr * (M_.maximum_endo_lag + M_.maximum_endo_lead + 1);
|
||||||
|
for i=1:length(M_.block_structure.block)
|
||||||
|
%disp(['block = ' int2str(i)]);
|
||||||
|
M_.block_structure.block(i).dr.Null=0;
|
||||||
|
M_.block_structure.block(i).dr=set_state_space(M_.block_structure.block(i).dr,M_.block_structure.block(i));
|
||||||
|
col_selector=repmat(M_.block_structure.block(i).variable,1,M_.block_structure.block(i).maximum_endo_lag+M_.block_structure.block(i).maximum_endo_lead+1)+kron([M_.maximum_endo_lag-M_.block_structure.block(i).maximum_endo_lag:M_.maximum_endo_lag+M_.block_structure.block(i).maximum_endo_lead],M_.endo_nbr*ones(1,M_.block_structure.block(i).endo_nbr));
|
||||||
|
row_selector = M_.block_structure.block(i).equation;
|
||||||
|
%jcb_=jacobia_(row_selector,col_selector);
|
||||||
|
jcb_=derivate(i).g1;
|
||||||
|
%disp('jcb_');
|
||||||
|
%full(jcb_)
|
||||||
|
%M_.block_structure.block(i).lead_lag_incidence'
|
||||||
|
jcb_ = jcb_(:,find(M_.block_structure.block(i).lead_lag_incidence')) ;
|
||||||
|
if M_.block_structure.block(i).exo_nbr>0
|
||||||
|
col_selector = [ first_col_exo + ...
|
||||||
|
repmat(M_.block_structure.block(i).exogenous,1,M_.block_structure.block(i).maximum_exo_lag+M_.block_structure.block(i).maximum_exo_lead+1)+kron([M_.maximum_exo_lag-M_.block_structure.block(i).maximum_exo_lag:M_.maximum_exo_lag+M_.block_structure.block(i).maximum_exo_lead],M_.exo_nbr*ones(1,M_.block_structure.block(i).exo_nbr))];
|
||||||
|
end
|
||||||
|
%derivate(i).g1
|
||||||
|
%derivate(i).g1_x
|
||||||
|
%col_selector
|
||||||
|
%jcb_ = [ jcb_ jacobia_(row_selector,col_selector)];
|
||||||
|
jcb_ = [ jcb_ derivate(i).g1_x];
|
||||||
|
%full(jcb_)
|
||||||
|
|
||||||
|
hss_=0; %hessian(M_.block_structure.block(i).equation,M_.block_structure.block(i).variable);
|
||||||
|
dra = M_.block_structure.block(i).dr;
|
||||||
|
%M_.block_structure.block(i).exo_nbr=M_.exo_nbr;
|
||||||
|
[dra ,info,M_.block_structure.block(i),options_,oo_] = dr11_sparse(dra ,task,M_.block_structure.block(i),options_,oo_, jcb_, hss_);
|
||||||
|
M_.block_structure.block(i).dr = dra;
|
||||||
|
dr.eigval = [dr.eigval; dra.eigval];
|
||||||
|
nyf = nnz(dra.kstate(:,2)>M_.block_structure.block(i).maximum_endo_lag+1);
|
||||||
|
n_explod = nnz(abs(dra.eigval) > options_.qz_criterium);
|
||||||
|
if nyf ~= n_explod
|
||||||
|
disp(['EIGENVALUES in block ' int2str(i) ':']);
|
||||||
|
[m_lambda,ii]=sort(abs(dra.eigval));
|
||||||
|
disp(sprintf('%16s %16s %16s\n','Modulus','Real','Imaginary'))
|
||||||
|
z=[m_lambda real(dra.eigval(ii)) imag(dra.eigval(ii))]';
|
||||||
|
disp(sprintf('%16.4g %16.4g %16.4g\n',z))
|
||||||
|
disp(['The rank condition is not satisfy in block ' int2str(i) ' :']);
|
||||||
|
disp([' ' int2str(nyf) ' forward-looking variable(s) for ' ...
|
||||||
|
int2str(n_explod) ' eigenvalue(s) larger than 1 in modulus']);
|
||||||
|
end
|
||||||
|
dr.nyf = dr.nyf + nyf;
|
||||||
|
dr.rank = dr.rank + dra.rank;
|
||||||
|
end;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -37,20 +37,20 @@ global options_
|
||||||
options_.smpl = zeros(2,1) ;
|
options_.smpl = zeros(2,1) ;
|
||||||
|
|
||||||
if nargin == 0
|
if nargin == 0
|
||||||
options_.smpl(1) = 1 ;
|
options_.smpl(1) = 1 ;
|
||||||
options_.smpl(2) = options_.periods ;
|
options_.smpl(2) = options_.periods ;
|
||||||
elseif nargin == 1
|
elseif nargin == 1
|
||||||
if s1 > options_.periods
|
if s1 > options_.periods
|
||||||
error('DSAMPLE: argument greater than number of periods');
|
error('DSAMPLE: argument greater than number of periods');
|
||||||
end
|
end
|
||||||
options_.smpl(1) = 1 ;
|
options_.smpl(1) = 1 ;
|
||||||
options_.smpl(2) = s1 ;
|
options_.smpl(2) = s1 ;
|
||||||
else
|
else
|
||||||
if s1 > options_.periods || s2 > options_.periods
|
if s1 > options_.periods || s2 > options_.periods
|
||||||
error('DSAMPLE: one of the arguments is greater than number of periods');
|
error('DSAMPLE: one of the arguments is greater than number of periods');
|
||||||
end
|
end
|
||||||
options_.smpl(1) = s1 ;
|
options_.smpl(1) = s1 ;
|
||||||
options_.smpl(2) = s2 ;
|
options_.smpl(2) = s2 ;
|
||||||
end
|
end
|
||||||
|
|
||||||
% 02/23/01 MJ added error checking
|
% 02/23/01 MJ added error checking
|
||||||
|
|
@ -38,274 +38,274 @@ function [fval,cost_flag,ys,trend_coeff,info] = dsge_posterior_kernel(xparam1,ge
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
global bayestopt_ estim_params_ options_ trend_coeff_ M_ oo_
|
global bayestopt_ estim_params_ options_ trend_coeff_ M_ oo_
|
||||||
fval = [];
|
fval = [];
|
||||||
ys = [];
|
ys = [];
|
||||||
trend_coeff = [];
|
trend_coeff = [];
|
||||||
cost_flag = 1;
|
cost_flag = 1;
|
||||||
nobs = size(options_.varobs,1);
|
nobs = size(options_.varobs,1);
|
||||||
%------------------------------------------------------------------------------
|
%------------------------------------------------------------------------------
|
||||||
% 1. Get the structural parameters & define penalties
|
% 1. Get the structural parameters & define penalties
|
||||||
%------------------------------------------------------------------------------
|
%------------------------------------------------------------------------------
|
||||||
if options_.mode_compute ~= 1 & any(xparam1 < bayestopt_.lb)
|
if options_.mode_compute ~= 1 & any(xparam1 < bayestopt_.lb)
|
||||||
k = find(xparam1 < bayestopt_.lb);
|
k = find(xparam1 < bayestopt_.lb);
|
||||||
fval = bayestopt_.penalty+sum((bayestopt_.lb(k)-xparam1(k)).^2);
|
fval = bayestopt_.penalty+sum((bayestopt_.lb(k)-xparam1(k)).^2);
|
||||||
cost_flag = 0;
|
cost_flag = 0;
|
||||||
info = 41;
|
info = 41;
|
||||||
return;
|
return;
|
||||||
end
|
end
|
||||||
if options_.mode_compute ~= 1 & any(xparam1 > bayestopt_.ub)
|
if options_.mode_compute ~= 1 & any(xparam1 > bayestopt_.ub)
|
||||||
k = find(xparam1 > bayestopt_.ub);
|
k = find(xparam1 > bayestopt_.ub);
|
||||||
fval = bayestopt_.penalty+sum((xparam1(k)-bayestopt_.ub(k)).^2);
|
fval = bayestopt_.penalty+sum((xparam1(k)-bayestopt_.ub(k)).^2);
|
||||||
cost_flag = 0;
|
cost_flag = 0;
|
||||||
info = 42;
|
info = 42;
|
||||||
return;
|
return;
|
||||||
end
|
end
|
||||||
Q = M_.Sigma_e;
|
Q = M_.Sigma_e;
|
||||||
H = M_.H;
|
H = M_.H;
|
||||||
for i=1:estim_params_.nvx
|
for i=1:estim_params_.nvx
|
||||||
k =estim_params_.var_exo(i,1);
|
k =estim_params_.var_exo(i,1);
|
||||||
Q(k,k) = xparam1(i)*xparam1(i);
|
Q(k,k) = xparam1(i)*xparam1(i);
|
||||||
end
|
end
|
||||||
offset = estim_params_.nvx;
|
offset = estim_params_.nvx;
|
||||||
if estim_params_.nvn
|
if estim_params_.nvn
|
||||||
for i=1:estim_params_.nvn
|
for i=1:estim_params_.nvn
|
||||||
k = estim_params_.var_endo(i,1);
|
k = estim_params_.var_endo(i,1);
|
||||||
H(k,k) = xparam1(i+offset)*xparam1(i+offset);
|
H(k,k) = xparam1(i+offset)*xparam1(i+offset);
|
||||||
end
|
end
|
||||||
offset = offset+estim_params_.nvn;
|
offset = offset+estim_params_.nvn;
|
||||||
end
|
end
|
||||||
if estim_params_.ncx
|
if estim_params_.ncx
|
||||||
for i=1:estim_params_.ncx
|
for i=1:estim_params_.ncx
|
||||||
k1 =estim_params_.corrx(i,1);
|
k1 =estim_params_.corrx(i,1);
|
||||||
k2 =estim_params_.corrx(i,2);
|
k2 =estim_params_.corrx(i,2);
|
||||||
Q(k1,k2) = xparam1(i+offset)*sqrt(Q(k1,k1)*Q(k2,k2));
|
Q(k1,k2) = xparam1(i+offset)*sqrt(Q(k1,k1)*Q(k2,k2));
|
||||||
Q(k2,k1) = Q(k1,k2);
|
Q(k2,k1) = Q(k1,k2);
|
||||||
end
|
end
|
||||||
[CholQ,testQ] = chol(Q);
|
[CholQ,testQ] = chol(Q);
|
||||||
if testQ %% The variance-covariance matrix of the structural innovations is not definite positive.
|
if testQ %% The variance-covariance matrix of the structural innovations is not definite positive.
|
||||||
%% We have to compute the eigenvalues of this matrix in order to build the penalty.
|
%% We have to compute the eigenvalues of this matrix in order to build the penalty.
|
||||||
a = diag(eig(Q));
|
a = diag(eig(Q));
|
||||||
k = find(a < 0);
|
k = find(a < 0);
|
||||||
if k > 0
|
if k > 0
|
||||||
fval = bayestopt_.penalty+sum(-a(k));
|
fval = bayestopt_.penalty+sum(-a(k));
|
||||||
cost_flag = 0;
|
cost_flag = 0;
|
||||||
info = 43;
|
info = 43;
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
offset = offset+estim_params_.ncx;
|
offset = offset+estim_params_.ncx;
|
||||||
end
|
end
|
||||||
if estim_params_.ncn
|
if estim_params_.ncn
|
||||||
for i=1:estim_params_.ncn
|
for i=1:estim_params_.ncn
|
||||||
k1 = options_.lgyidx2varobs(estim_params_.corrn(i,1));
|
k1 = options_.lgyidx2varobs(estim_params_.corrn(i,1));
|
||||||
k2 = options_.lgyidx2varobs(estim_params_.corrn(i,2));
|
k2 = options_.lgyidx2varobs(estim_params_.corrn(i,2));
|
||||||
H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2));
|
H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2));
|
||||||
H(k2,k1) = H(k1,k2);
|
H(k2,k1) = H(k1,k2);
|
||||||
end
|
end
|
||||||
[CholH,testH] = chol(H);
|
[CholH,testH] = chol(H);
|
||||||
if testH
|
if testH
|
||||||
a = diag(eig(H));
|
a = diag(eig(H));
|
||||||
k = find(a < 0);
|
k = find(a < 0);
|
||||||
if k > 0
|
if k > 0
|
||||||
fval = bayestopt_.penalty+sum(-a(k));
|
fval = bayestopt_.penalty+sum(-a(k));
|
||||||
cost_flag = 0;
|
cost_flag = 0;
|
||||||
info = 44;
|
info = 44;
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
offset = offset+estim_params_.ncn;
|
offset = offset+estim_params_.ncn;
|
||||||
end
|
end
|
||||||
if estim_params_.np > 0
|
if estim_params_.np > 0
|
||||||
M_.params(estim_params_.param_vals(:,1)) = xparam1(offset+1:end);
|
M_.params(estim_params_.param_vals(:,1)) = xparam1(offset+1:end);
|
||||||
end
|
end
|
||||||
M_.Sigma_e = Q;
|
M_.Sigma_e = Q;
|
||||||
M_.H = H;
|
M_.H = H;
|
||||||
%------------------------------------------------------------------------------
|
%------------------------------------------------------------------------------
|
||||||
% 2. call model setup & reduction program
|
% 2. call model setup & reduction program
|
||||||
%------------------------------------------------------------------------------
|
%------------------------------------------------------------------------------
|
||||||
[T,R,SteadyState,info] = dynare_resolve(bayestopt_.restrict_var_list,...
|
[T,R,SteadyState,info] = dynare_resolve(bayestopt_.restrict_var_list,...
|
||||||
bayestopt_.restrict_columns,...
|
bayestopt_.restrict_columns,...
|
||||||
bayestopt_.restrict_aux);
|
bayestopt_.restrict_aux);
|
||||||
if info(1) == 1 | info(1) == 2 | info(1) == 5
|
if info(1) == 1 | info(1) == 2 | info(1) == 5
|
||||||
fval = bayestopt_.penalty+1;
|
fval = bayestopt_.penalty+1;
|
||||||
cost_flag = 0;
|
cost_flag = 0;
|
||||||
return
|
return
|
||||||
elseif info(1) == 3 | info(1) == 4 | info(1) == 20
|
elseif info(1) == 3 | info(1) == 4 | info(1) == 20
|
||||||
fval = bayestopt_.penalty+info(2);%^2; % penalty power raised in DR1.m and resol already. GP July'08
|
fval = bayestopt_.penalty+info(2);%^2; % penalty power raised in DR1.m and resol already. GP July'08
|
||||||
cost_flag = 0;
|
cost_flag = 0;
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
bayestopt_.mf = bayestopt_.mf1;
|
bayestopt_.mf = bayestopt_.mf1;
|
||||||
if ~options_.noconstant
|
if ~options_.noconstant
|
||||||
if options_.loglinear == 1
|
if options_.loglinear == 1
|
||||||
constant = log(SteadyState(bayestopt_.mfys));
|
constant = log(SteadyState(bayestopt_.mfys));
|
||||||
else
|
else
|
||||||
constant = SteadyState(bayestopt_.mfys);
|
constant = SteadyState(bayestopt_.mfys);
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
constant = zeros(nobs,1);
|
constant = zeros(nobs,1);
|
||||||
end
|
end
|
||||||
if bayestopt_.with_trend == 1
|
if bayestopt_.with_trend == 1
|
||||||
trend_coeff = zeros(nobs,1);
|
trend_coeff = zeros(nobs,1);
|
||||||
t = options_.trend_coeffs;
|
t = options_.trend_coeffs;
|
||||||
for i=1:length(t)
|
for i=1:length(t)
|
||||||
if ~isempty(t{i})
|
if ~isempty(t{i})
|
||||||
trend_coeff(i) = evalin('base',t{i});
|
trend_coeff(i) = evalin('base',t{i});
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
trend = repmat(constant,1,gend)+trend_coeff*[1:gend];
|
trend = repmat(constant,1,gend)+trend_coeff*[1:gend];
|
||||||
else
|
else
|
||||||
trend = repmat(constant,1,gend);
|
trend = repmat(constant,1,gend);
|
||||||
end
|
end
|
||||||
start = options_.presample+1;
|
start = options_.presample+1;
|
||||||
np = size(T,1);
|
np = size(T,1);
|
||||||
mf = bayestopt_.mf;
|
mf = bayestopt_.mf;
|
||||||
no_missing_data_flag = (number_of_observations==gend*nobs);
|
no_missing_data_flag = (number_of_observations==gend*nobs);
|
||||||
%------------------------------------------------------------------------------
|
%------------------------------------------------------------------------------
|
||||||
% 3. Initial condition of the Kalman filter
|
% 3. Initial condition of the Kalman filter
|
||||||
%------------------------------------------------------------------------------
|
%------------------------------------------------------------------------------
|
||||||
kalman_algo = options_.kalman_algo;
|
kalman_algo = options_.kalman_algo;
|
||||||
if options_.lik_init == 1 % Kalman filter
|
if options_.lik_init == 1 % Kalman filter
|
||||||
if kalman_algo ~= 2
|
if kalman_algo ~= 2
|
||||||
kalman_algo = 1;
|
kalman_algo = 1;
|
||||||
end
|
end
|
||||||
Pstar = lyapunov_symm(T,R*Q*R',options_.qz_criterium,options_.lyapunov_complex_threshold);
|
Pstar = lyapunov_symm(T,R*Q*R',options_.qz_criterium,options_.lyapunov_complex_threshold);
|
||||||
Pinf = [];
|
Pinf = [];
|
||||||
elseif options_.lik_init == 2 % Old Diffuse Kalman filter
|
elseif options_.lik_init == 2 % Old Diffuse Kalman filter
|
||||||
if kalman_algo ~= 2
|
if kalman_algo ~= 2
|
||||||
kalman_algo = 1;
|
kalman_algo = 1;
|
||||||
end
|
end
|
||||||
Pstar = 10*eye(np);
|
Pstar = 10*eye(np);
|
||||||
Pinf = [];
|
Pinf = [];
|
||||||
elseif options_.lik_init == 3 % Diffuse Kalman filter
|
elseif options_.lik_init == 3 % Diffuse Kalman filter
|
||||||
if kalman_algo ~= 4
|
if kalman_algo ~= 4
|
||||||
kalman_algo = 3;
|
kalman_algo = 3;
|
||||||
end
|
end
|
||||||
[QT,ST] = schur(T);
|
[QT,ST] = schur(T);
|
||||||
e1 = abs(ordeig(ST)) > 2-options_.qz_criterium;
|
e1 = abs(ordeig(ST)) > 2-options_.qz_criterium;
|
||||||
[QT,ST] = ordschur(QT,ST,e1);
|
[QT,ST] = ordschur(QT,ST,e1);
|
||||||
k = find(abs(ordeig(ST)) > 2-options_.qz_criterium);
|
k = find(abs(ordeig(ST)) > 2-options_.qz_criterium);
|
||||||
nk = length(k);
|
nk = length(k);
|
||||||
nk1 = nk+1;
|
nk1 = nk+1;
|
||||||
Pinf = zeros(np,np);
|
Pinf = zeros(np,np);
|
||||||
Pinf(1:nk,1:nk) = eye(nk);
|
Pinf(1:nk,1:nk) = eye(nk);
|
||||||
Pstar = zeros(np,np);
|
Pstar = zeros(np,np);
|
||||||
B = QT'*R*Q*R'*QT;
|
B = QT'*R*Q*R'*QT;
|
||||||
for i=np:-1:nk+2
|
for i=np:-1:nk+2
|
||||||
if ST(i,i-1) == 0
|
if ST(i,i-1) == 0
|
||||||
if i == np
|
if i == np
|
||||||
c = zeros(np-nk,1);
|
c = zeros(np-nk,1);
|
||||||
else
|
else
|
||||||
c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
|
c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
|
||||||
ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
|
ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
|
||||||
end
|
end
|
||||||
q = eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i);
|
q = eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i);
|
||||||
Pstar(nk1:i,i) = q\(B(nk1:i,i)+c);
|
Pstar(nk1:i,i) = q\(B(nk1:i,i)+c);
|
||||||
Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
|
Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
|
||||||
else
|
else
|
||||||
if i == np
|
if i == np
|
||||||
c = zeros(np-nk,1);
|
c = zeros(np-nk,1);
|
||||||
c1 = zeros(np-nk,1);
|
c1 = zeros(np-nk,1);
|
||||||
else
|
else
|
||||||
c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
|
c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
|
||||||
ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i)+...
|
ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i)+...
|
||||||
ST(i,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1);
|
ST(i,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1);
|
||||||
c1 = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i-1,i+1:end)')+...
|
c1 = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i-1,i+1:end)')+...
|
||||||
ST(i-1,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1)+...
|
ST(i-1,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1)+...
|
||||||
ST(i-1,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
|
ST(i-1,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
|
||||||
end
|
end
|
||||||
q = [eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i) -ST(nk1:i,nk1:i)*ST(i,i-1);...
|
q = [eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i) -ST(nk1:i,nk1:i)*ST(i,i-1);...
|
||||||
-ST(nk1:i,nk1:i)*ST(i-1,i) eye(i-nk)-ST(nk1:i,nk1:i)*ST(i-1,i-1)];
|
-ST(nk1:i,nk1:i)*ST(i-1,i) eye(i-nk)-ST(nk1:i,nk1:i)*ST(i-1,i-1)];
|
||||||
z = q\[B(nk1:i,i)+c;B(nk1:i,i-1)+c1];
|
z = q\[B(nk1:i,i)+c;B(nk1:i,i-1)+c1];
|
||||||
Pstar(nk1:i,i) = z(1:(i-nk));
|
Pstar(nk1:i,i) = z(1:(i-nk));
|
||||||
Pstar(nk1:i,i-1) = z(i-nk+1:end);
|
Pstar(nk1:i,i-1) = z(i-nk+1:end);
|
||||||
Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
|
Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
|
||||||
Pstar(i-1,nk1:i-2) = Pstar(nk1:i-2,i-1)';
|
Pstar(i-1,nk1:i-2) = Pstar(nk1:i-2,i-1)';
|
||||||
i = i - 1;
|
i = i - 1;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if i == nk+2
|
if i == nk+2
|
||||||
c = ST(nk+1,:)*(Pstar(:,nk+2:end)*ST(nk1,nk+2:end)')+ST(nk1,nk1)*ST(nk1,nk+2:end)*Pstar(nk+2:end,nk1);
|
c = ST(nk+1,:)*(Pstar(:,nk+2:end)*ST(nk1,nk+2:end)')+ST(nk1,nk1)*ST(nk1,nk+2:end)*Pstar(nk+2:end,nk1);
|
||||||
Pstar(nk1,nk1)=(B(nk1,nk1)+c)/(1-ST(nk1,nk1)*ST(nk1,nk1));
|
Pstar(nk1,nk1)=(B(nk1,nk1)+c)/(1-ST(nk1,nk1)*ST(nk1,nk1));
|
||||||
end
|
end
|
||||||
Z = QT(mf,:);
|
Z = QT(mf,:);
|
||||||
R1 = QT'*R;
|
R1 = QT'*R;
|
||||||
[QQ,RR,EE] = qr(Z*ST(:,1:nk),0);
|
[QQ,RR,EE] = qr(Z*ST(:,1:nk),0);
|
||||||
k = find(abs(diag(RR)) < 1e-8);
|
k = find(abs(diag(RR)) < 1e-8);
|
||||||
if length(k) > 0
|
if length(k) > 0
|
||||||
k1 = EE(:,k);
|
k1 = EE(:,k);
|
||||||
dd =ones(nk,1);
|
dd =ones(nk,1);
|
||||||
dd(k1) = zeros(length(k1),1);
|
dd(k1) = zeros(length(k1),1);
|
||||||
Pinf(1:nk,1:nk) = diag(dd);
|
Pinf(1:nk,1:nk) = diag(dd);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if kalman_algo == 2
|
if kalman_algo == 2
|
||||||
no_correlation_flag = 1;
|
no_correlation_flag = 1;
|
||||||
if length(H)==1
|
if length(H)==1
|
||||||
H = zeros(nobs,1);
|
H = zeros(nobs,1);
|
||||||
else
|
else
|
||||||
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
|
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
|
||||||
H = diag(H);
|
H = diag(H);
|
||||||
else
|
else
|
||||||
no_correlation_flag = 0;
|
no_correlation_flag = 0;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
kalman_tol = options_.kalman_tol;
|
kalman_tol = options_.kalman_tol;
|
||||||
riccati_tol = options_.riccati_tol;
|
riccati_tol = options_.riccati_tol;
|
||||||
mf = bayestopt_.mf1;
|
mf = bayestopt_.mf1;
|
||||||
Y = data-trend;
|
Y = data-trend;
|
||||||
%------------------------------------------------------------------------------
|
%------------------------------------------------------------------------------
|
||||||
% 4. Likelihood evaluation
|
% 4. Likelihood evaluation
|
||||||
%------------------------------------------------------------------------------
|
%------------------------------------------------------------------------------
|
||||||
if (kalman_algo==1)% Multivariate Kalman Filter
|
if (kalman_algo==1)% Multivariate Kalman Filter
|
||||||
if no_missing_data_flag
|
if no_missing_data_flag
|
||||||
LIK = kalman_filter(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol);
|
LIK = kalman_filter(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol);
|
||||||
else
|
else
|
||||||
LIK = ...
|
LIK = ...
|
||||||
missing_observations_kalman_filter(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol, ...
|
missing_observations_kalman_filter(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol, ...
|
||||||
data_index,number_of_observations,no_more_missing_observations);
|
data_index,number_of_observations,no_more_missing_observations);
|
||||||
end
|
end
|
||||||
if isinf(LIK)
|
if isinf(LIK)
|
||||||
kalman_algo = 2;
|
kalman_algo = 2;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if (kalman_algo==2)% Univariate Kalman Filter
|
if (kalman_algo==2)% Univariate Kalman Filter
|
||||||
if no_correlation_flag
|
if no_correlation_flag
|
||||||
LIK = univariate_kalman_filter(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol,data_index,number_of_observations,no_more_missing_observations);
|
LIK = univariate_kalman_filter(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol,data_index,number_of_observations,no_more_missing_observations);
|
||||||
else
|
else
|
||||||
LIK = univariate_kalman_filter_corr(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol,data_index,number_of_observations,no_more_missing_observations);
|
LIK = univariate_kalman_filter_corr(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol,data_index,number_of_observations,no_more_missing_observations);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if (kalman_algo==3)% Multivariate Diffuse Kalman Filter
|
if (kalman_algo==3)% Multivariate Diffuse Kalman Filter
|
||||||
if no_missing_data_flag
|
if no_missing_data_flag
|
||||||
LIK = diffuse_kalman_filter(ST,R1,Q,H,Pinf,Pstar,Y,start,Z,kalman_tol,riccati_tol);
|
LIK = diffuse_kalman_filter(ST,R1,Q,H,Pinf,Pstar,Y,start,Z,kalman_tol,riccati_tol);
|
||||||
else
|
else
|
||||||
LIK = missing_observations_diffuse_kalman_filter(ST,R1,Q,H,Pinf,Pstar,Y,start,Z,kalman_tol,riccati_tol,...
|
LIK = missing_observations_diffuse_kalman_filter(ST,R1,Q,H,Pinf,Pstar,Y,start,Z,kalman_tol,riccati_tol,...
|
||||||
data_index,number_of_observations,no_more_missing_observations);
|
data_index,number_of_observations,no_more_missing_observations);
|
||||||
end
|
end
|
||||||
if isinf(LIK)
|
if isinf(LIK)
|
||||||
kalman_algo = 4;
|
kalman_algo = 4;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if (kalman_algo==4)% Univariate Diffuse Kalman Filter
|
if (kalman_algo==4)% Univariate Diffuse Kalman Filter
|
||||||
if no_correlation_flag
|
if no_correlation_flag
|
||||||
LIK = univariate_diffuse_kalman_filter(ST,R1,Q,H,Pinf,Pstar,Y,start,Z,kalman_tol,riccati_tol,...
|
LIK = univariate_diffuse_kalman_filter(ST,R1,Q,H,Pinf,Pstar,Y,start,Z,kalman_tol,riccati_tol,...
|
||||||
data_index,number_of_observations,no_more_missing_observations);
|
data_index,number_of_observations,no_more_missing_observations);
|
||||||
else
|
else
|
||||||
LIK = univariate_diffuse_kalman_filter_corr(ST,R1,Q,H,Pinf,Pstar,Y,start,Z,kalman_tol,riccati_tol,...
|
LIK = univariate_diffuse_kalman_filter_corr(ST,R1,Q,H,Pinf,Pstar,Y,start,Z,kalman_tol,riccati_tol,...
|
||||||
data_index,number_of_observations,no_more_missing_observations);
|
data_index,number_of_observations,no_more_missing_observations);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
if imag(LIK) ~= 0
|
if imag(LIK) ~= 0
|
||||||
likelihood = bayestopt_.penalty;
|
likelihood = bayestopt_.penalty;
|
||||||
else
|
else
|
||||||
likelihood = LIK;
|
likelihood = LIK;
|
||||||
end
|
end
|
||||||
% ------------------------------------------------------------------------------
|
% ------------------------------------------------------------------------------
|
||||||
% Adds prior if necessary
|
% Adds prior if necessary
|
||||||
% ------------------------------------------------------------------------------
|
% ------------------------------------------------------------------------------
|
||||||
lnprior = priordens(xparam1,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
|
lnprior = priordens(xparam1,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
|
||||||
fval = (likelihood-lnprior);
|
fval = (likelihood-lnprior);
|
||||||
options_.kalman_algo = kalman_algo;
|
options_.kalman_algo = kalman_algo;
|
||||||
|
|
@ -1,5 +1,5 @@
|
||||||
function [nvar,vartan,NumberOfConditionalDecompFiles] = ...
|
function [nvar,vartan,NumberOfConditionalDecompFiles] = ...
|
||||||
dsge_simulated_theoretical_conditional_variance_decomposition(SampleSize,Steps,M_,options_,oo_,type)
|
dsge_simulated_theoretical_conditional_variance_decomposition(SampleSize,Steps,M_,options_,oo_,type)
|
||||||
% This function computes the posterior or prior distribution of the conditional variance
|
% This function computes the posterior or prior distribution of the conditional variance
|
||||||
% decomposition of the endogenous variables (or a subset of the endogenous variables).
|
% decomposition of the endogenous variables (or a subset of the endogenous variables).
|
||||||
%
|
%
|
||||||
|
|
|
||||||
|
|
@ -47,7 +47,7 @@ else
|
||||||
error()
|
error()
|
||||||
end
|
end
|
||||||
NumberOfDrawsFiles = length(DrawsFiles);
|
NumberOfDrawsFiles = length(DrawsFiles);
|
||||||
|
|
||||||
% Set varlist (vartan)
|
% Set varlist (vartan)
|
||||||
if ~posterior
|
if ~posterior
|
||||||
if isfield(options_,'varlist')
|
if isfield(options_,'varlist')
|
||||||
|
|
|
||||||
|
|
@ -1,5 +1,5 @@
|
||||||
function [nvar,vartan,NumberOfDecompFiles] = ...
|
function [nvar,vartan,NumberOfDecompFiles] = ...
|
||||||
dsge_simulated_theoretical_variance_decomposition(SampleSize,M_,options_,oo_,type)
|
dsge_simulated_theoretical_variance_decomposition(SampleSize,M_,options_,oo_,type)
|
||||||
% This function computes the posterior or prior distribution of the variance
|
% This function computes the posterior or prior distribution of the variance
|
||||||
% decomposition of the observed endogenous variables.
|
% decomposition of the observed endogenous variables.
|
||||||
%
|
%
|
||||||
|
|
|
||||||
|
|
@ -17,8 +17,7 @@ function y=dy_date(year,period)
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
global M_
|
global M_
|
||||||
|
|
||||||
y = M_.freq*(year-M_.start_date(1))+period-M_.start_date(2)+1;
|
y = M_.freq*(year-M_.start_date(1))+period-M_.start_date(2)+1;
|
||||||
|
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -31,54 +31,54 @@ function [z,zss]=dyn2vec(s1,s2)
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
global M_ oo_ options_
|
global M_ oo_ options_
|
||||||
|
|
||||||
if options_.smpl == 0
|
if options_.smpl == 0
|
||||||
k = [1:size(oo_.endo_simul,2)];
|
k = [1:size(oo_.endo_simul,2)];
|
||||||
else
|
else
|
||||||
k = [M_.maximum_lag+options_.smpl(1):M_.maximum_lag+options_.smpl(2)];
|
k = [M_.maximum_lag+options_.smpl(1):M_.maximum_lag+options_.smpl(2)];
|
||||||
end
|
end
|
||||||
|
|
||||||
if nargin == 0
|
if nargin == 0
|
||||||
if nargout > 0
|
if nargout > 0
|
||||||
t = ['DYNARE dyn2vec error: the function doesn''t return values when' ...
|
t = ['DYNARE dyn2vec error: the function doesn''t return values when' ...
|
||||||
' used without input argument'];
|
' used without input argument'];
|
||||||
error(t);
|
error(t);
|
||||||
end
|
end
|
||||||
for i=1:size(oo_.endo_simul,1)
|
for i=1:size(oo_.endo_simul,1)
|
||||||
assignin('base',deblank(M_.endo_names(i,:)),oo_.endo_simul(i,k)');
|
assignin('base',deblank(M_.endo_names(i,:)),oo_.endo_simul(i,k)');
|
||||||
end
|
end
|
||||||
return
|
return
|
||||||
else
|
else
|
||||||
j = strmatch(s1,M_.endo_names,'exact');
|
j = strmatch(s1,M_.endo_names,'exact');
|
||||||
if ~ isempty(j)
|
if ~ isempty(j)
|
||||||
z = oo_.endo_simul(j,k)';
|
z = oo_.endo_simul(j,k)';
|
||||||
else
|
else
|
||||||
j = strmatch(s1,M_.exo_names,'exact');
|
j = strmatch(s1,M_.exo_names,'exact');
|
||||||
if ~ isempty(j)
|
if ~ isempty(j)
|
||||||
if options_.smpl == 0
|
if options_.smpl == 0
|
||||||
z = oo_.exo_simul(:,j);
|
z = oo_.exo_simul(:,j);
|
||||||
else
|
else
|
||||||
z = oo_.exo_simul(M_.maximum_lag+options_.smpl(1):M_.maximum_lag+options_.smpl(2));
|
z = oo_.exo_simul(M_.maximum_lag+options_.smpl(1):M_.maximum_lag+options_.smpl(2));
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
t = ['DYNARE dyn2vec error: variable ' deblank(s1(i,:)) ' doesn''t' ...
|
t = ['DYNARE dyn2vec error: variable ' deblank(s1(i,:)) ' doesn''t' ...
|
||||||
' exist.'] ;
|
' exist.'] ;
|
||||||
error (t) ;
|
error (t) ;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
||||||
if nargout == 0
|
if nargout == 0
|
||||||
if nargin == 1
|
if nargin == 1
|
||||||
assignin('base',s1,z);
|
assignin('base',s1,z);
|
||||||
elseif nargin == 2
|
elseif nargin == 2
|
||||||
assignin('base',s2,z);
|
assignin('base',s2,z);
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
zss=oo_.steady_state(j);
|
zss=oo_.steady_state(j);
|
||||||
end
|
end
|
||||||
|
|
||||||
% 02/23/01 MJ redone, incorporating FC's improvements
|
% 02/23/01 MJ redone, incorporating FC's improvements
|
||||||
% 08/24/01 MJ replaced globlize by internal assignin
|
% 08/24/01 MJ replaced globlize by internal assignin
|
||||||
% 08/24/01 MJ added 'exact' to strmatch (thanks to David Vavra)
|
% 08/24/01 MJ added 'exact' to strmatch (thanks to David Vavra)
|
||||||
|
|
|
||||||
|
|
@ -1,239 +1,239 @@
|
||||||
function [J,M_] = dyn_ramsey_dynamic_(ys,lbar,M_,options_,oo_,it_)
|
function [J,M_] = dyn_ramsey_dynamic_(ys,lbar,M_,options_,oo_,it_)
|
||||||
% function J = dyn_ramsey_dynamic_(ys,lbar)
|
% function J = dyn_ramsey_dynamic_(ys,lbar)
|
||||||
% dyn_ramsey_dynamic_ sets up the Jacobian of the expanded model for optimal
|
% dyn_ramsey_dynamic_ sets up the Jacobian of the expanded model for optimal
|
||||||
% policies. It modifies several fields of M_
|
% policies. It modifies several fields of M_
|
||||||
%
|
%
|
||||||
% INPUTS:
|
% INPUTS:
|
||||||
% ys: steady state of original endogenous variables
|
% ys: steady state of original endogenous variables
|
||||||
% lbar: steady state of Lagrange multipliers
|
% lbar: steady state of Lagrange multipliers
|
||||||
%
|
%
|
||||||
% OUPTUTS:
|
% OUPTUTS:
|
||||||
% J: jaocobian of expanded model
|
% J: jaocobian of expanded model
|
||||||
%
|
%
|
||||||
% SPECIAL REQUIREMENTS
|
% SPECIAL REQUIREMENTS
|
||||||
% none
|
% none
|
||||||
|
|
||||||
% Copyright (C) 2003-2009 Dynare Team
|
% Copyright (C) 2003-2009 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
% Dynare is free software: you can redistribute it and/or modify
|
||||||
% it under the terms of the GNU General Public License as published by
|
% it under the terms of the GNU General Public License as published by
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
% the Free Software Foundation, either version 3 of the License, or
|
||||||
% (at your option) any later version.
|
% (at your option) any later version.
|
||||||
%
|
%
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
% Dynare is distributed in the hope that it will be useful,
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
% retrieving model parameters
|
% retrieving model parameters
|
||||||
endo_nbr = M_.endo_nbr;
|
endo_nbr = M_.endo_nbr;
|
||||||
i_endo_nbr = 1:endo_nbr;
|
i_endo_nbr = 1:endo_nbr;
|
||||||
endo_names = M_.endo_names;
|
endo_names = M_.endo_names;
|
||||||
% exo_nbr = M_.exo_nbr+M_.exo_det_nbr;
|
% exo_nbr = M_.exo_nbr+M_.exo_det_nbr;
|
||||||
% exo_names = vertcat(M_.exo_names,M_.exo_det_names);
|
% exo_names = vertcat(M_.exo_names,M_.exo_det_names);
|
||||||
exo_nbr = M_.exo_nbr;
|
exo_nbr = M_.exo_nbr;
|
||||||
exo_names = M_.exo_names;
|
exo_names = M_.exo_names;
|
||||||
i_leadlag = M_.lead_lag_incidence;
|
i_leadlag = M_.lead_lag_incidence;
|
||||||
max_lead = M_.maximum_lead;
|
max_lead = M_.maximum_lead;
|
||||||
max_endo_lead = M_.maximum_endo_lead;
|
max_endo_lead = M_.maximum_endo_lead;
|
||||||
max_lag = M_.maximum_lag;
|
max_lag = M_.maximum_lag;
|
||||||
max_endo_lag = M_.maximum_endo_lag;
|
max_endo_lag = M_.maximum_endo_lag;
|
||||||
leadlag_nbr = max_lead+max_lag+1;
|
leadlag_nbr = max_lead+max_lag+1;
|
||||||
fname = M_.fname;
|
fname = M_.fname;
|
||||||
% instr_names = options_.olr_inst;
|
% instr_names = options_.olr_inst;
|
||||||
% instr_nbr = size(options_.olr_inst,1);
|
% instr_nbr = size(options_.olr_inst,1);
|
||||||
|
|
||||||
% discount factor
|
% discount factor
|
||||||
beta = options_.planner_discount;
|
beta = options_.planner_discount;
|
||||||
|
|
||||||
% storing original values
|
% storing original values
|
||||||
orig_model.endo_nbr = endo_nbr;
|
orig_model.endo_nbr = endo_nbr;
|
||||||
orig_model.orig_endo_nbr = M_.orig_endo_nbr;
|
orig_model.orig_endo_nbr = M_.orig_endo_nbr;
|
||||||
orig_model.aux_vars = M_.aux_vars;
|
orig_model.aux_vars = M_.aux_vars;
|
||||||
orig_model.endo_names = endo_names;
|
orig_model.endo_names = endo_names;
|
||||||
orig_model.lead_lag_incidence = i_leadlag;
|
orig_model.lead_lag_incidence = i_leadlag;
|
||||||
orig_model.maximum_lead = max_lead;
|
orig_model.maximum_lead = max_lead;
|
||||||
orig_model.maximum_endo_lead = max_endo_lead;
|
orig_model.maximum_endo_lead = max_endo_lead;
|
||||||
orig_model.maximum_lag = max_lag;
|
orig_model.maximum_lag = max_lag;
|
||||||
orig_model.maximum_endo_lag = max_endo_lag;
|
orig_model.maximum_endo_lag = max_endo_lag;
|
||||||
|
|
||||||
y = repmat(ys,1,max_lag+max_lead+1);
|
y = repmat(ys,1,max_lag+max_lead+1);
|
||||||
k = find(i_leadlag');
|
k = find(i_leadlag');
|
||||||
|
|
||||||
% retrieving derivatives of the objective function
|
% retrieving derivatives of the objective function
|
||||||
[U,Uy,Uyy] = feval([fname '_objective_static'],ys,zeros(1,exo_nbr), M_.params);
|
[U,Uy,Uyy] = feval([fname '_objective_static'],ys,zeros(1,exo_nbr), M_.params);
|
||||||
Uy = Uy';
|
Uy = Uy';
|
||||||
Uyy = reshape(Uyy,endo_nbr,endo_nbr);
|
Uyy = reshape(Uyy,endo_nbr,endo_nbr);
|
||||||
|
|
||||||
% retrieving derivatives of original model
|
% retrieving derivatives of original model
|
||||||
[f,fJ,fH] = feval([fname '_dynamic'],y(k),[oo_.exo_simul oo_.exo_det_simul], M_.params, it_);
|
[f,fJ,fH] = feval([fname '_dynamic'],y(k),[oo_.exo_simul oo_.exo_det_simul], M_.params, it_);
|
||||||
instr_nbr = endo_nbr - size(f,1);
|
instr_nbr = endo_nbr - size(f,1);
|
||||||
mult_nbr = endo_nbr-instr_nbr;
|
mult_nbr = endo_nbr-instr_nbr;
|
||||||
|
|
||||||
% parameters for expanded model
|
% parameters for expanded model
|
||||||
endo_nbr1 = 2*endo_nbr-instr_nbr+exo_nbr;
|
endo_nbr1 = 2*endo_nbr-instr_nbr+exo_nbr;
|
||||||
max_lead1 = max_lead + max_lag;
|
max_lead1 = max_lead + max_lag;
|
||||||
max_lag1 = max_lead1;
|
max_lag1 = max_lead1;
|
||||||
max_leadlag1 = max_lead1;
|
max_leadlag1 = max_lead1;
|
||||||
|
|
||||||
% adding new variables names
|
% adding new variables names
|
||||||
endo_names1 = endo_names;
|
endo_names1 = endo_names;
|
||||||
% adding shocks to endogenous variables
|
% adding shocks to endogenous variables
|
||||||
endo_names1 = strvcat(endo_names1, exo_names);
|
endo_names1 = strvcat(endo_names1, exo_names);
|
||||||
% adding multipliers names
|
% adding multipliers names
|
||||||
for i=1:mult_nbr;
|
for i=1:mult_nbr;
|
||||||
endo_names1 = strvcat(endo_names1,['mult_' int2str(i)]);
|
endo_names1 = strvcat(endo_names1,['mult_' int2str(i)]);
|
||||||
end
|
end
|
||||||
|
|
||||||
% expanding matrix of lead/lag incidence
|
% expanding matrix of lead/lag incidence
|
||||||
%
|
%
|
||||||
% multipliers lead/lag incidence
|
% multipliers lead/lag incidence
|
||||||
i_mult = [];
|
i_mult = [];
|
||||||
for i=1:leadlag_nbr
|
for i=1:leadlag_nbr
|
||||||
i_mult = [any(fJ(:,nonzeros(i_leadlag(i,:))) ~= 0,2)' ; i_mult];
|
i_mult = [any(fJ(:,nonzeros(i_leadlag(i,:))) ~= 0,2)' ; i_mult];
|
||||||
end
|
end
|
||||||
% putting it all together:
|
% putting it all together:
|
||||||
% original variables, exogenous variables made endogenous, multipliers
|
% original variables, exogenous variables made endogenous, multipliers
|
||||||
%
|
%
|
||||||
% number of original dynamic variables
|
% number of original dynamic variables
|
||||||
n_dyn = nnz(i_leadlag);
|
n_dyn = nnz(i_leadlag);
|
||||||
% numbering columns of dynamic multipliers to be put in the last columns
|
% numbering columns of dynamic multipliers to be put in the last columns
|
||||||
% of the new Jacobian
|
% of the new Jacobian
|
||||||
i_leadlag1 = [cumsum(i_leadlag(1:max_lag,:),1); ...
|
i_leadlag1 = [cumsum(i_leadlag(1:max_lag,:),1); ...
|
||||||
repmat(i_leadlag(max_lag+1,:),leadlag_nbr,1); ...
|
repmat(i_leadlag(max_lag+1,:),leadlag_nbr,1); ...
|
||||||
flipud(cumsum(flipud(i_leadlag(max_lag+2:end,:)),1))];
|
flipud(cumsum(flipud(i_leadlag(max_lag+2:end,:)),1))];
|
||||||
i_leadlag1 = i_leadlag1';
|
i_leadlag1 = i_leadlag1';
|
||||||
k = find(i_leadlag1 > 0);
|
k = find(i_leadlag1 > 0);
|
||||||
n = length(k);
|
n = length(k);
|
||||||
i_leadlag1(k) = 1:n;
|
i_leadlag1(k) = 1:n;
|
||||||
i_leadlag1 = i_leadlag1';
|
i_leadlag1 = i_leadlag1';
|
||||||
i_mult = i_mult';
|
i_mult = i_mult';
|
||||||
k = find(i_mult > 0);
|
k = find(i_mult > 0);
|
||||||
i_mult(k) = n+leadlag_nbr*exo_nbr+(1:length(k));
|
i_mult(k) = n+leadlag_nbr*exo_nbr+(1:length(k));
|
||||||
i_mult = i_mult';
|
i_mult = i_mult';
|
||||||
i_leadlag1 = [ i_leadlag1 ...
|
i_leadlag1 = [ i_leadlag1 ...
|
||||||
[zeros(max_lag,exo_nbr);...
|
[zeros(max_lag,exo_nbr);...
|
||||||
reshape(n+(1:leadlag_nbr*exo_nbr),exo_nbr,leadlag_nbr)'; ...
|
reshape(n+(1:leadlag_nbr*exo_nbr),exo_nbr,leadlag_nbr)'; ...
|
||||||
zeros(max_lead,exo_nbr)] ...
|
zeros(max_lead,exo_nbr)] ...
|
||||||
[zeros(max_lag,mult_nbr);...
|
[zeros(max_lag,mult_nbr);...
|
||||||
i_mult;...
|
i_mult;...
|
||||||
zeros(max_lead,mult_nbr)]];
|
zeros(max_lead,mult_nbr)]];
|
||||||
i_leadlag1 = i_leadlag1';
|
i_leadlag1 = i_leadlag1';
|
||||||
k = find(i_leadlag1 > 0);
|
k = find(i_leadlag1 > 0);
|
||||||
n = length(k);
|
n = length(k);
|
||||||
i_leadlag1(k) = 1:n;
|
i_leadlag1(k) = 1:n;
|
||||||
i_leadlag1 = i_leadlag1';
|
i_leadlag1 = i_leadlag1';
|
||||||
|
|
||||||
% building Jacobian of expanded model
|
% building Jacobian of expanded model
|
||||||
jacobian = zeros(endo_nbr+mult_nbr,nnz(i_leadlag1)+exo_nbr);
|
jacobian = zeros(endo_nbr+mult_nbr,nnz(i_leadlag1)+exo_nbr);
|
||||||
% derivatives of f.o.c. w.r. to endogenous variables
|
% derivatives of f.o.c. w.r. to endogenous variables
|
||||||
% to be rearranged further down
|
% to be rearranged further down
|
||||||
lbarfH = lbar'*fH;
|
lbarfH = lbar'*fH;
|
||||||
% indices of Hessian columns
|
% indices of Hessian columns
|
||||||
n1 = nnz(i_leadlag)+exo_nbr;
|
n1 = nnz(i_leadlag)+exo_nbr;
|
||||||
iH = reshape(1:n1^2,n1,n1);
|
iH = reshape(1:n1^2,n1,n1);
|
||||||
J = zeros(endo_nbr1,nnz(i_leadlag1)+exo_nbr);
|
J = zeros(endo_nbr1,nnz(i_leadlag1)+exo_nbr);
|
||||||
% second order derivatives of objective function
|
% second order derivatives of objective function
|
||||||
J(1:endo_nbr,i_leadlag1(max_leadlag1+1,1:endo_nbr)) = Uyy;
|
J(1:endo_nbr,i_leadlag1(max_leadlag1+1,1:endo_nbr)) = Uyy;
|
||||||
% loop on lead/lags in expanded model
|
% loop on lead/lags in expanded model
|
||||||
for i=1:2*max_leadlag1 + 1
|
for i=1:2*max_leadlag1 + 1
|
||||||
% index of variables at the current lag in expanded model
|
% index of variables at the current lag in expanded model
|
||||||
kc = find(i_leadlag1(i,i_endo_nbr) > 0);
|
kc = find(i_leadlag1(i,i_endo_nbr) > 0);
|
||||||
t1 = max(1,i-max_leadlag1);
|
t1 = max(1,i-max_leadlag1);
|
||||||
t2 = min(i,max_leadlag1+1);
|
t2 = min(i,max_leadlag1+1);
|
||||||
% loop on lead/lag blocks of relevant 1st order derivatives
|
% loop on lead/lag blocks of relevant 1st order derivatives
|
||||||
for j = t1:t2
|
for j = t1:t2
|
||||||
% derivatives w.r. endogenous variables
|
% derivatives w.r. endogenous variables
|
||||||
ic = find(i_leadlag(i-j+1,:) > 0 );
|
ic = find(i_leadlag(i-j+1,:) > 0 );
|
||||||
kc1 = i_leadlag(i-j+1,ic);
|
kc1 = i_leadlag(i-j+1,ic);
|
||||||
[junk,ic1,ic2] = intersect(ic,kc);
|
[junk,ic1,ic2] = intersect(ic,kc);
|
||||||
kc2 = i_leadlag1(i,kc(ic2));
|
kc2 = i_leadlag1(i,kc(ic2));
|
||||||
ir = find(i_leadlag(max_leadlag1+2-j,:) > 0 );
|
ir = find(i_leadlag(max_leadlag1+2-j,:) > 0 );
|
||||||
kr1 = i_leadlag(max_leadlag1+2-j,ir);
|
kr1 = i_leadlag(max_leadlag1+2-j,ir);
|
||||||
J(ir,kc2) = J(ir,kc2) + beta^(j-max_lead-1)...
|
J(ir,kc2) = J(ir,kc2) + beta^(j-max_lead-1)...
|
||||||
*reshape(lbarfH(iH(kr1,kc1)),length(kr1),length(kc1));
|
*reshape(lbarfH(iH(kr1,kc1)),length(kr1),length(kc1));
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
% derivatives w.r. aux. variables for lead/lag exogenous shocks
|
% derivatives w.r. aux. variables for lead/lag exogenous shocks
|
||||||
for i=1:leadlag_nbr
|
for i=1:leadlag_nbr
|
||||||
kc = i_leadlag1(max_lag+i,endo_nbr+(1:exo_nbr));
|
kc = i_leadlag1(max_lag+i,endo_nbr+(1:exo_nbr));
|
||||||
ir = find(i_leadlag(leadlag_nbr+1-i,:) > 0);
|
ir = find(i_leadlag(leadlag_nbr+1-i,:) > 0);
|
||||||
kr1 = i_leadlag(leadlag_nbr+1-i,ir);
|
kr1 = i_leadlag(leadlag_nbr+1-i,ir);
|
||||||
J(ir,kc) = beta^(i-max_lead-1)...
|
J(ir,kc) = beta^(i-max_lead-1)...
|
||||||
*reshape(lbarfH(iH(kr1,n_dyn+(1:exo_nbr))),length(kr1), ...
|
*reshape(lbarfH(iH(kr1,n_dyn+(1:exo_nbr))),length(kr1), ...
|
||||||
exo_nbr);
|
exo_nbr);
|
||||||
end
|
end
|
||||||
% derivatives w.r. Lagrange multipliers
|
% derivatives w.r. Lagrange multipliers
|
||||||
for i=1:leadlag_nbr
|
for i=1:leadlag_nbr
|
||||||
ic1 = find(i_leadlag(leadlag_nbr+1-i,:) > 0);
|
ic1 = find(i_leadlag(leadlag_nbr+1-i,:) > 0);
|
||||||
kc1 = i_leadlag(leadlag_nbr+1-i,ic1);
|
kc1 = i_leadlag(leadlag_nbr+1-i,ic1);
|
||||||
ic2 = find(i_leadlag1(max_lag+i,endo_nbr+exo_nbr+(1:mult_nbr)) > 0);
|
ic2 = find(i_leadlag1(max_lag+i,endo_nbr+exo_nbr+(1:mult_nbr)) > 0);
|
||||||
kc2 = i_leadlag1(max_lag+i,endo_nbr+exo_nbr+ic2);
|
kc2 = i_leadlag1(max_lag+i,endo_nbr+exo_nbr+ic2);
|
||||||
J(ic1,kc2) = beta^(i-max_lead-1)*fJ(ic2,kc1)';
|
J(ic1,kc2) = beta^(i-max_lead-1)*fJ(ic2,kc1)';
|
||||||
end
|
end
|
||||||
|
|
||||||
% Jacobian of original equations
|
% Jacobian of original equations
|
||||||
%
|
%
|
||||||
% w.r. endogenous variables
|
% w.r. endogenous variables
|
||||||
ir = endo_nbr+(1:endo_nbr-instr_nbr);
|
ir = endo_nbr+(1:endo_nbr-instr_nbr);
|
||||||
for i=1:leadlag_nbr
|
for i=1:leadlag_nbr
|
||||||
ic1 = find(i_leadlag(i,:) > 0);
|
ic1 = find(i_leadlag(i,:) > 0);
|
||||||
kc1 = i_leadlag(i,ic1);
|
kc1 = i_leadlag(i,ic1);
|
||||||
ic2 = find(i_leadlag1(max_lead+i,:) > 0);
|
ic2 = find(i_leadlag1(max_lead+i,:) > 0);
|
||||||
kc2 = i_leadlag1(max_lead+i,ic2);
|
kc2 = i_leadlag1(max_lead+i,ic2);
|
||||||
[junk,junk,ic3] = intersect(ic1,ic2);
|
[junk,junk,ic3] = intersect(ic1,ic2);
|
||||||
J(ir,kc2(ic3)) = fJ(:,kc1);
|
J(ir,kc2(ic3)) = fJ(:,kc1);
|
||||||
end
|
end
|
||||||
% w.r. exogenous variables
|
% w.r. exogenous variables
|
||||||
J(ir,nnz(i_leadlag1)+(1:exo_nbr)) = fJ(:,nnz(i_leadlag)+(1:exo_nbr));
|
J(ir,nnz(i_leadlag1)+(1:exo_nbr)) = fJ(:,nnz(i_leadlag)+(1:exo_nbr));
|
||||||
|
|
||||||
% auxiliary variable for exogenous shocks
|
% auxiliary variable for exogenous shocks
|
||||||
ir = 2*endo_nbr-instr_nbr+(1:exo_nbr);
|
ir = 2*endo_nbr-instr_nbr+(1:exo_nbr);
|
||||||
kc = i_leadlag1(leadlag_nbr,endo_nbr+(1:exo_nbr));
|
kc = i_leadlag1(leadlag_nbr,endo_nbr+(1:exo_nbr));
|
||||||
J(ir,kc) = eye(exo_nbr);
|
J(ir,kc) = eye(exo_nbr);
|
||||||
J(ir,nnz(i_leadlag1)+(1:exo_nbr)) = -eye(exo_nbr);
|
J(ir,nnz(i_leadlag1)+(1:exo_nbr)) = -eye(exo_nbr);
|
||||||
|
|
||||||
% eliminating empty columns
|
% eliminating empty columns
|
||||||
|
|
||||||
% getting indices of nonzero entries
|
% getting indices of nonzero entries
|
||||||
m = find(i_leadlag1');
|
m = find(i_leadlag1');
|
||||||
n1 = max_lag1*endo_nbr1+1;
|
n1 = max_lag1*endo_nbr1+1;
|
||||||
n2 = n1+endo_nbr-1;
|
n2 = n1+endo_nbr-1;
|
||||||
|
|
||||||
|
|
||||||
n = length(m);
|
n = length(m);
|
||||||
k = 1:size(J,2);
|
k = 1:size(J,2);
|
||||||
|
|
||||||
for i=1:n
|
for i=1:n
|
||||||
if sum(abs(J(:,i))) < 1e-8
|
if sum(abs(J(:,i))) < 1e-8
|
||||||
if m(i) < n1 | m(i) > n2
|
if m(i) < n1 | m(i) > n2
|
||||||
k(i) = 0;
|
k(i) = 0;
|
||||||
m(i) = 0;
|
m(i) = 0;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
||||||
J = J(:,nonzeros(k));
|
J = J(:,nonzeros(k));
|
||||||
i_leadlag1 = zeros(size(i_leadlag1))';
|
i_leadlag1 = zeros(size(i_leadlag1))';
|
||||||
i_leadlag1(nonzeros(m)) = 1:nnz(m);
|
i_leadlag1(nonzeros(m)) = 1:nnz(m);
|
||||||
i_leadlag1 = i_leadlag1';
|
i_leadlag1 = i_leadlag1';
|
||||||
|
|
||||||
% setting expanded model parameters
|
% setting expanded model parameters
|
||||||
% storing original values
|
% storing original values
|
||||||
M_.endo_nbr = endo_nbr1;
|
M_.endo_nbr = endo_nbr1;
|
||||||
% Consider that there is no auxiliary variable, because otherwise it
|
% Consider that there is no auxiliary variable, because otherwise it
|
||||||
% interacts badly with the auxiliary variables from the preprocessor.
|
% interacts badly with the auxiliary variables from the preprocessor.
|
||||||
M_.orig_endo_nbr = endo_nbr1;
|
M_.orig_endo_nbr = endo_nbr1;
|
||||||
M_.aux_vars = [];
|
M_.aux_vars = [];
|
||||||
M_.endo_names = endo_names1;
|
M_.endo_names = endo_names1;
|
||||||
M_.lead_lag_incidence = i_leadlag1;
|
M_.lead_lag_incidence = i_leadlag1;
|
||||||
M_.maximum_lead = max_lead1;
|
M_.maximum_lead = max_lead1;
|
||||||
M_.maximum_endo_lead = max_lead1;
|
M_.maximum_endo_lead = max_lead1;
|
||||||
M_.maximum_lag = max_lag1;
|
M_.maximum_lag = max_lag1;
|
||||||
M_.maximum_endo_lag = max_lag1;
|
M_.maximum_endo_lag = max_lag1;
|
||||||
M_.orig_model = orig_model;
|
M_.orig_model = orig_model;
|
||||||
|
|
|
||||||
|
|
@ -1,138 +1,137 @@
|
||||||
function [resids, rJ,mult] = dyn_ramsey_static_(x,M_,options_,oo_,it_)
|
function [resids, rJ,mult] = dyn_ramsey_static_(x,M_,options_,oo_,it_)
|
||||||
|
|
||||||
% function [resids, rJ,mult] = dyn_ramsey_static_(x)
|
% function [resids, rJ,mult] = dyn_ramsey_static_(x)
|
||||||
% Computes the static first order conditions for optimal policy
|
% Computes the static first order conditions for optimal policy
|
||||||
%
|
%
|
||||||
% INPUTS
|
% INPUTS
|
||||||
% x: vector of endogenous variables
|
% x: vector of endogenous variables
|
||||||
%
|
%
|
||||||
% OUTPUTS
|
% OUTPUTS
|
||||||
% resids: residuals of non linear equations
|
% resids: residuals of non linear equations
|
||||||
% rJ: Jacobian
|
% rJ: Jacobian
|
||||||
% mult: Lagrangian multipliers
|
% mult: Lagrangian multipliers
|
||||||
%
|
%
|
||||||
% SPECIAL REQUIREMENTS
|
% SPECIAL REQUIREMENTS
|
||||||
% none
|
% none
|
||||||
|
|
||||||
% Copyright (C) 2003-2007 Dynare Team
|
% Copyright (C) 2003-2007 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
% Dynare is free software: you can redistribute it and/or modify
|
||||||
% it under the terms of the GNU General Public License as published by
|
% it under the terms of the GNU General Public License as published by
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
% the Free Software Foundation, either version 3 of the License, or
|
||||||
% (at your option) any later version.
|
% (at your option) any later version.
|
||||||
%
|
%
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
% Dynare is distributed in the hope that it will be useful,
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
% GNU General Public License for more details.
|
% GNU General Public License for more details.
|
||||||
%
|
%
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
|
|
||||||
% recovering usefull fields
|
% recovering usefull fields
|
||||||
endo_nbr = M_.endo_nbr;
|
endo_nbr = M_.endo_nbr;
|
||||||
exo_nbr = M_.exo_nbr;
|
exo_nbr = M_.exo_nbr;
|
||||||
fname = M_.fname;
|
fname = M_.fname;
|
||||||
% inst_nbr = M_.inst_nbr;
|
% inst_nbr = M_.inst_nbr;
|
||||||
% i_endo_no_inst = M_.endogenous_variables_without_instruments;
|
% i_endo_no_inst = M_.endogenous_variables_without_instruments;
|
||||||
max_lead = M_.maximum_lead;
|
max_lead = M_.maximum_lead;
|
||||||
max_lag = M_.maximum_lag;
|
max_lag = M_.maximum_lag;
|
||||||
beta = options_.planner_discount;
|
beta = options_.planner_discount;
|
||||||
|
|
||||||
% indices of all endogenous variables
|
% indices of all endogenous variables
|
||||||
i_endo = [1:endo_nbr]';
|
i_endo = [1:endo_nbr]';
|
||||||
% indices of endogenous variable except instruments
|
% indices of endogenous variable except instruments
|
||||||
% i_inst = M_.instruments;
|
% i_inst = M_.instruments;
|
||||||
% lead_lag incidence matrix for endogenous variables
|
% lead_lag incidence matrix for endogenous variables
|
||||||
i_lag = M_.lead_lag_incidence;
|
i_lag = M_.lead_lag_incidence;
|
||||||
|
|
||||||
if options_.steadystate_flag
|
if options_.steadystate_flag
|
||||||
k_inst = [];
|
k_inst = [];
|
||||||
instruments = options_.instruments;
|
instruments = options_.instruments;
|
||||||
for i = 1:size(instruments,1)
|
for i = 1:size(instruments,1)
|
||||||
k_inst = [k_inst; strmatch(options_.instruments(i,:), ...
|
k_inst = [k_inst; strmatch(options_.instruments(i,:), ...
|
||||||
M_.endo_names,'exact')];
|
M_.endo_names,'exact')];
|
||||||
end
|
end
|
||||||
oo_.steady_state(k_inst) = x;
|
oo_.steady_state(k_inst) = x;
|
||||||
[x,check] = feval([M_.fname '_steadystate'],...
|
[x,check] = feval([M_.fname '_steadystate'],...
|
||||||
oo_.steady_state,...
|
oo_.steady_state,...
|
||||||
[oo_.exo_steady_state; ...
|
[oo_.exo_steady_state; ...
|
||||||
oo_.exo_det_steady_state]);
|
oo_.exo_det_steady_state]);
|
||||||
if size(x,1) < M_.endo_nbr
|
if size(x,1) < M_.endo_nbr
|
||||||
if length(M_.aux_vars) > 0
|
if length(M_.aux_vars) > 0
|
||||||
x = add_auxiliary_variables_to_steadystate(x,M_.aux_vars,...
|
x = add_auxiliary_variables_to_steadystate(x,M_.aux_vars,...
|
||||||
M_.fname,...
|
M_.fname,...
|
||||||
oo_.exo_steady_state,...
|
oo_.exo_steady_state,...
|
||||||
oo_.exo_det_steady_state,...
|
oo_.exo_det_steady_state,...
|
||||||
M_.params);
|
M_.params);
|
||||||
else
|
else
|
||||||
error([M_.fname '_steadystate.m doesn''t match the model']);
|
error([M_.fname '_steadystate.m doesn''t match the model']);
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
||||||
% value and Jacobian of objective function
|
% value and Jacobian of objective function
|
||||||
ex = zeros(1,M_.exo_nbr);
|
ex = zeros(1,M_.exo_nbr);
|
||||||
[U,Uy,Uyy] = feval([fname '_objective_static'],x(i_endo),ex, M_.params);
|
[U,Uy,Uyy] = feval([fname '_objective_static'],x(i_endo),ex, M_.params);
|
||||||
Uy = Uy';
|
Uy = Uy';
|
||||||
Uyy = reshape(Uyy,endo_nbr,endo_nbr);
|
Uyy = reshape(Uyy,endo_nbr,endo_nbr);
|
||||||
|
|
||||||
y = repmat(x(i_endo),1,max_lag+max_lead+1);
|
y = repmat(x(i_endo),1,max_lag+max_lead+1);
|
||||||
% value and Jacobian of dynamic function
|
% value and Jacobian of dynamic function
|
||||||
k = find(i_lag');
|
k = find(i_lag');
|
||||||
it_ = 1;
|
it_ = 1;
|
||||||
% [f,fJ,fH] = feval([fname '_dynamic'],y(k),ex);
|
% [f,fJ,fH] = feval([fname '_dynamic'],y(k),ex);
|
||||||
[f,fJ] = feval([fname '_dynamic'],y(k),[oo_.exo_simul oo_.exo_det_simul], M_.params, it_);
|
[f,fJ] = feval([fname '_dynamic'],y(k),[oo_.exo_simul oo_.exo_det_simul], M_.params, it_);
|
||||||
% indices of Lagrange multipliers
|
% indices of Lagrange multipliers
|
||||||
inst_nbr = endo_nbr - size(f,1);
|
inst_nbr = endo_nbr - size(f,1);
|
||||||
i_mult = [endo_nbr+1:2*endo_nbr-inst_nbr]';
|
i_mult = [endo_nbr+1:2*endo_nbr-inst_nbr]';
|
||||||
|
|
||||||
% derivatives of Lagrangian with respect to endogenous variables
|
% derivatives of Lagrangian with respect to endogenous variables
|
||||||
% res1 = Uy;
|
% res1 = Uy;
|
||||||
A = zeros(endo_nbr,endo_nbr-inst_nbr);
|
A = zeros(endo_nbr,endo_nbr-inst_nbr);
|
||||||
for i=1:max_lag+max_lead+1
|
for i=1:max_lag+max_lead+1
|
||||||
% select variables present in the model at a given lag
|
% select variables present in the model at a given lag
|
||||||
[junk,k1,k2] = find(i_lag(i,:));
|
[junk,k1,k2] = find(i_lag(i,:));
|
||||||
% res1(k1) = res1(k1) + beta^(max_lag-i+1)*fJ(:,k2)'*x(i_mult);
|
% res1(k1) = res1(k1) + beta^(max_lag-i+1)*fJ(:,k2)'*x(i_mult);
|
||||||
A(k1,:) = A(k1,:) + beta^(max_lag-i+1)*fJ(:,k2)';
|
A(k1,:) = A(k1,:) + beta^(max_lag-i+1)*fJ(:,k2)';
|
||||||
end
|
end
|
||||||
|
|
||||||
% i_inst = var_index(options_.olr_inst);
|
% i_inst = var_index(options_.olr_inst);
|
||||||
% k = setdiff(1:size(A,1),i_inst);
|
% k = setdiff(1:size(A,1),i_inst);
|
||||||
% mult = -A(k,:)\Uy(k);
|
% mult = -A(k,:)\Uy(k);
|
||||||
mult = -A\Uy;
|
mult = -A\Uy;
|
||||||
% resids = [f; Uy(i_inst)+A(i_inst,:)*mult];
|
% resids = [f; Uy(i_inst)+A(i_inst,:)*mult];
|
||||||
resids1 = Uy+A*mult;
|
resids1 = Uy+A*mult;
|
||||||
% resids = [f; sqrt(resids1'*resids1/endo_nbr)];
|
% resids = [f; sqrt(resids1'*resids1/endo_nbr)];
|
||||||
[q,r,e] = qr([A Uy]');
|
[q,r,e] = qr([A Uy]');
|
||||||
if options_.steadystate_flag
|
if options_.steadystate_flag
|
||||||
resids = [r(end,(endo_nbr-inst_nbr+1:end))'];
|
resids = [r(end,(endo_nbr-inst_nbr+1:end))'];
|
||||||
else
|
else
|
||||||
resids = [f; r(end,(endo_nbr-inst_nbr+1:end))'];
|
resids = [f; r(end,(endo_nbr-inst_nbr+1:end))'];
|
||||||
end
|
end
|
||||||
rJ = [];
|
rJ = [];
|
||||||
return;
|
return;
|
||||||
|
|
||||||
% Jacobian of first order conditions
|
% Jacobian of first order conditions
|
||||||
n = nnz(i_lag)+exo_nbr;
|
n = nnz(i_lag)+exo_nbr;
|
||||||
iH = reshape(1:n^2,n,n);
|
iH = reshape(1:n^2,n,n);
|
||||||
rJ = zeros(2*endo_nbr-inst_nbr,2*endo_nbr-inst_nbr);
|
rJ = zeros(2*endo_nbr-inst_nbr,2*endo_nbr-inst_nbr);
|
||||||
|
|
||||||
rJ(i_endo,i_endo) = Uyy;
|
rJ(i_endo,i_endo) = Uyy;
|
||||||
for i=1:max_lag+max_lead+1
|
for i=1:max_lag+max_lead+1
|
||||||
% select variables present in the model at a given lag
|
% select variables present in the model at a given lag
|
||||||
[junk,k1,k2] = find(i_lag(i,:));
|
[junk,k1,k2] = find(i_lag(i,:));
|
||||||
k3 = length(k2);
|
k3 = length(k2);
|
||||||
rJ(k1,k1) = rJ(k1,k1) + beta^(max_lag-i+1)*reshape(fH(:,iH(k2,k2))'*x(i_mult),k3,k3);
|
rJ(k1,k1) = rJ(k1,k1) + beta^(max_lag-i+1)*reshape(fH(:,iH(k2,k2))'*x(i_mult),k3,k3);
|
||||||
rJ(k1,i_mult) = rJ(k1,i_mult) + beta^(max_lag-1+1)*fJ(:,k2)';
|
rJ(k1,i_mult) = rJ(k1,i_mult) + beta^(max_lag-1+1)*fJ(:,k2)';
|
||||||
rJ(i_mult,k1) = rJ(i_mult,k1) + fJ(:,k2);
|
rJ(i_mult,k1) = rJ(i_mult,k1) + fJ(:,k2);
|
||||||
end
|
end
|
||||||
|
|
||||||
% rJ = 1e-3*rJ;
|
% rJ = 1e-3*rJ;
|
||||||
% rJ(209,210) = rJ(209,210)+1-1e-3;
|
% rJ(209,210) = rJ(209,210)+1-1e-3;
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -59,13 +59,13 @@ end
|
||||||
warning_config()
|
warning_config()
|
||||||
|
|
||||||
if exist('OCTAVE_VERSION')
|
if exist('OCTAVE_VERSION')
|
||||||
if octave_ver_less_than('3.0.0')
|
if octave_ver_less_than('3.0.0')
|
||||||
warning('This version of Dynare has only been tested on Octave 3.0.0 and above. Since your Octave version is older than that, Dynare may fail to run, or give unexpected results. Consider upgrading your Octave installation.');
|
warning('This version of Dynare has only been tested on Octave 3.0.0 and above. Since your Octave version is older than that, Dynare may fail to run, or give unexpected results. Consider upgrading your Octave installation.');
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
if matlab_ver_less_than('6.5')
|
if matlab_ver_less_than('6.5')
|
||||||
warning('This version of Dynare has only been tested on Matlab 6.5 and above. Since your Matlab version is older than that, Dynare may fail to run, or give unexpected results. Consider upgrading your Matlab installation (or switch to Octave).');
|
warning('This version of Dynare has only been tested on Matlab 6.5 and above. Since your Matlab version is older than that, Dynare may fail to run, or give unexpected results. Consider upgrading your Matlab installation (or switch to Octave).');
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
||||||
% disable output paging (it is on by default on Octave)
|
% disable output paging (it is on by default on Octave)
|
||||||
|
|
@ -73,44 +73,44 @@ more off
|
||||||
|
|
||||||
% sets default format for save() command
|
% sets default format for save() command
|
||||||
if exist('OCTAVE_VERSION')
|
if exist('OCTAVE_VERSION')
|
||||||
default_save_options('-mat')
|
default_save_options('-mat')
|
||||||
end
|
end
|
||||||
|
|
||||||
% detect if MEX files are present; if not, use alternative M-files
|
% detect if MEX files are present; if not, use alternative M-files
|
||||||
dynareroot = dynare_config;
|
dynareroot = dynare_config;
|
||||||
|
|
||||||
if nargin < 1
|
if nargin < 1
|
||||||
error('DYNARE: you must provide the name of the MOD file in argument')
|
error('DYNARE: you must provide the name of the MOD file in argument')
|
||||||
end
|
end
|
||||||
|
|
||||||
if ~ischar(fname)
|
if ~ischar(fname)
|
||||||
error('DYNARE: argument of dynare must be a text string')
|
error('DYNARE: argument of dynare must be a text string')
|
||||||
end
|
end
|
||||||
|
|
||||||
% Testing if file have extension
|
% Testing if file have extension
|
||||||
% If no extension default .mod is added
|
% If no extension default .mod is added
|
||||||
if isempty(strfind(fname,'.'))
|
if isempty(strfind(fname,'.'))
|
||||||
fname1 = [fname '.dyn'];
|
fname1 = [fname '.dyn'];
|
||||||
d = dir(fname1);
|
d = dir(fname1);
|
||||||
if length(d) == 0
|
if length(d) == 0
|
||||||
fname1 = [fname '.mod'];
|
fname1 = [fname '.mod'];
|
||||||
end
|
end
|
||||||
fname = fname1;
|
fname = fname1;
|
||||||
% Checking file extension
|
% Checking file extension
|
||||||
else
|
else
|
||||||
if ~strcmp(upper(fname(size(fname,2)-3:size(fname,2))),'.MOD') ...
|
if ~strcmp(upper(fname(size(fname,2)-3:size(fname,2))),'.MOD') ...
|
||||||
&& ~strcmp(upper(fname(size(fname,2)-3:size(fname,2))),'.DYN')
|
&& ~strcmp(upper(fname(size(fname,2)-3:size(fname,2))),'.DYN')
|
||||||
error('DYNARE: argument must be a filename with .mod or .dyn extension')
|
error('DYNARE: argument must be a filename with .mod or .dyn extension')
|
||||||
end;
|
end;
|
||||||
end;
|
end;
|
||||||
d = dir(fname);
|
d = dir(fname);
|
||||||
if length(d) == 0
|
if length(d) == 0
|
||||||
error(['DYNARE: can''t open ' fname])
|
error(['DYNARE: can''t open ' fname])
|
||||||
end
|
end
|
||||||
|
|
||||||
command = ['"' dynareroot 'dynare_m" ' fname] ;
|
command = ['"' dynareroot 'dynare_m" ' fname] ;
|
||||||
for i=2:nargin
|
for i=2:nargin
|
||||||
command = [command ' ' varargin{i-1}];
|
command = [command ' ' varargin{i-1}];
|
||||||
end
|
end
|
||||||
|
|
||||||
% Workaround for bug in Octave >= 3.2
|
% Workaround for bug in Octave >= 3.2
|
||||||
|
|
@ -122,11 +122,11 @@ end
|
||||||
[status, result] = system(command);
|
[status, result] = system(command);
|
||||||
disp(result)
|
disp(result)
|
||||||
if status
|
if status
|
||||||
% Should not use "error(result)" since message will be truncated if too long
|
% Should not use "error(result)" since message will be truncated if too long
|
||||||
error('DYNARE: preprocessing failed')
|
error('DYNARE: preprocessing failed')
|
||||||
end
|
end
|
||||||
|
|
||||||
if ~ isempty(find(abs(fname) == 46))
|
if ~ isempty(find(abs(fname) == 46))
|
||||||
fname = fname(:,1:find(abs(fname) == 46)-1) ;
|
fname = fname(:,1:find(abs(fname) == 46)-1) ;
|
||||||
end
|
end
|
||||||
evalin('base',fname) ;
|
evalin('base',fname) ;
|
||||||
|
|
|
||||||
|
|
@ -189,29 +189,29 @@ end
|
||||||
|
|
||||||
% Test if bytecode DLL is present
|
% Test if bytecode DLL is present
|
||||||
if exist('bytecode') == 3
|
if exist('bytecode') == 3
|
||||||
if ~multithread_flag
|
if ~multithread_flag
|
||||||
message = '[mex] ';
|
message = '[mex] ';
|
||||||
else
|
else
|
||||||
message = [ '[mex][multithread version, ' int2str(multithread_flag+1) ' threads are used] ' ];
|
message = [ '[mex][multithread version, ' int2str(multithread_flag+1) ' threads are used] ' ];
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
message = '[no] ';
|
message = '[no] ';
|
||||||
end
|
end
|
||||||
disp([ message 'Bytecode evaluation.' ])
|
disp([ message 'Bytecode evaluation.' ])
|
||||||
|
|
||||||
% Test if k-order perturbation DLL is present
|
% Test if k-order perturbation DLL is present
|
||||||
if exist('k_order_perturbation') == 3
|
if exist('k_order_perturbation') == 3
|
||||||
message = '[mex] ';
|
message = '[mex] ';
|
||||||
else
|
else
|
||||||
message = '[no] ';
|
message = '[no] ';
|
||||||
end
|
end
|
||||||
disp([ message 'k-order perturbation solver.' ])
|
disp([ message 'k-order perturbation solver.' ])
|
||||||
|
|
||||||
% Test if dynare_simul_ DLL is present
|
% Test if dynare_simul_ DLL is present
|
||||||
if exist('dynare_simul_') == 3
|
if exist('dynare_simul_') == 3
|
||||||
message = '[mex] ';
|
message = '[mex] ';
|
||||||
else
|
else
|
||||||
message = '[no] ';
|
message = '[no] ';
|
||||||
end
|
end
|
||||||
disp([ message 'k-order solution simulation.' ])
|
disp([ message 'k-order solution simulation.' ])
|
||||||
|
|
||||||
|
|
|
||||||
File diff suppressed because it is too large
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Reference in New Issue