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Removed globals from resol.m (changed calling sequence). Added texinfo header. 

Removed trailing whitespace.
time-shift
Stéphane Adjemian (Scylla) 2011-09-17 12:33:07 +02:00
parent d81fd1b55b
commit 24cd423671
23 changed files with 326 additions and 284 deletions
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26
matlab/PosteriorFilterSmootherAndForecast.m
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@ -5,14 +5,14 @@ function PosteriorFilterSmootherAndForecast(Y,gend, type,data_index)
% %
% 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
@ -59,8 +59,8 @@ 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;
@ -136,17 +136,17 @@ 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,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
[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
@ -191,7 +191,7 @@ for b=1:B
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;
@ -206,28 +206,28 @@ for b=1:B
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;

2
matlab/PosteriorIRF_core1.m
View File

@ -150,7 +150,7 @@ while fpar<npar
end end
stock_param(irun2,:) = deep; stock_param(irun2,:) = deep;
set_parameters(deep); set_parameters(deep);
[dr,info] = resol(oo_.steady_state,0); [dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
if info(1) if info(1)
nosaddle = nosaddle + 1; nosaddle = nosaddle + 1;
fpar = fpar - 1; fpar = fpar - 1;

8
matlab/dsge_simulated_theoretical_conditional_variance_decomposition.m
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@ -2,8 +2,8 @@ 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).
% %
% INPUTS % INPUTS
% SampleSize [integer] scalar, number of simulations. % SampleSize [integer] scalar, number of simulations.
% M_ [structure] Dynare structure describing the model. % M_ [structure] Dynare structure describing the model.
% options_ [structure] Dynare structure defining global options. % options_ [structure] Dynare structure defining global options.
@ -11,7 +11,7 @@ function [nvar,vartan,NumberOfConditionalDecompFiles] = ...
% type [string] 'prior' or 'posterior' % type [string] 'prior' or 'posterior'
% %
% %
% OUTPUTS % OUTPUTS
% nvar [integer] nvar is the number of stationary variables. % nvar [integer] nvar is the number of stationary variables.
% vartan [char] array of characters (with nvar rows). % vartan [char] array of characters (with nvar rows).
% NumberOfConditionalDecompFiles [integer] scalar, number of prior or posterior data files (for covariance). % NumberOfConditionalDecompFiles [integer] scalar, number of prior or posterior data files (for covariance).
@ -103,7 +103,7 @@ for file = 1:NumberOfDrawsFiles
dr = pdraws{linee,2}; dr = pdraws{linee,2};
else else
set_parameters(pdraws{linee,1}); set_parameters(pdraws{linee,1});
[dr,info] = resol(oo_.steady_state,0); [dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
end end
if first_call if first_call
endo_nbr = M_.endo_nbr; endo_nbr = M_.endo_nbr;

12
matlab/dsge_simulated_theoretical_correlation.m
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@ -1,16 +1,16 @@
function [nvar,vartan,CorrFileNumber] = dsge_simulated_theoretical_correlation(SampleSize,nar,M_,options_,oo_,type) function [nvar,vartan,CorrFileNumber] = dsge_simulated_theoretical_correlation(SampleSize,nar,M_,options_,oo_,type)
% This function computes the posterior or prior distribution of the endogenous % This function computes the posterior or prior distribution of the endogenous
% variables second order moments. % variables second order moments.
% %
% INPUTS % INPUTS
% SampleSize [integer] % SampleSize [integer]
% nar [integer] % nar [integer]
% M_ [structure] % M_ [structure]
% options_ [structure] % options_ [structure]
% oo_ [structure] % oo_ [structure]
% type [string] % type [string]
% %
% OUTPUTS % OUTPUTS
% nvar [integer] % nvar [integer]
% vartan [char] % vartan [char]
% CorrFileNumber [integer] % CorrFileNumber [integer]
@ -98,7 +98,7 @@ for file = 1:NumberOfDrawsFiles
dr = pdraws{linee,2}; dr = pdraws{linee,2};
else else
set_parameters(pdraws{linee,1}); set_parameters(pdraws{linee,1});
[dr,info] = resol(oo_.steady_state,0); [dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
end end
tmp = th_autocovariances(dr,ivar,M_,options_,nodecomposition); tmp = th_autocovariances(dr,ivar,M_,options_,nodecomposition);
for i=1:nar for i=1:nar

10
matlab/dsge_simulated_theoretical_covariance.m
View File

@ -1,8 +1,8 @@
function [nvar,vartan,CovarFileNumber] = dsge_simulated_theoretical_covariance(SampleSize,M_,options_,oo_,type) function [nvar,vartan,CovarFileNumber] = dsge_simulated_theoretical_covariance(SampleSize,M_,options_,oo_,type)
% This function computes the posterior or prior distribution of the endogenous % This function computes the posterior or prior distribution of the endogenous
% variables second order moments. % variables second order moments.
% %
% INPUTS % INPUTS
% SampleSize [integer] scalar, number of simulations. % SampleSize [integer] scalar, number of simulations.
% M_ [structure] Dynare structure describing the model. % M_ [structure] Dynare structure describing the model.
% options_ [structure] Dynare structure defining global options. % options_ [structure] Dynare structure defining global options.
@ -10,7 +10,7 @@ function [nvar,vartan,CovarFileNumber] = dsge_simulated_theoretical_covariance(S
% type [string] 'prior' or 'posterior' % type [string] 'prior' or 'posterior'
% %
% %
% OUTPUTS % OUTPUTS
% nvar [integer] nvar is the number of stationary variables. % nvar [integer] nvar is the number of stationary variables.
% vartan [char] array of characters (with nvar rows). % vartan [char] array of characters (with nvar rows).
% CovarFileNumber [integer] scalar, number of prior or posterior data files (for covariance). % CovarFileNumber [integer] scalar, number of prior or posterior data files (for covariance).
@ -98,7 +98,7 @@ for file = 1:NumberOfDrawsFiles
dr = pdraws{linee,2}; dr = pdraws{linee,2};
else else
set_parameters(pdraws{linee,1}); set_parameters(pdraws{linee,1});
[dr,info] = resol(oo_.steady_state,0); [dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
end end
tmp = th_autocovariances(dr,ivar,M_,options_,nodecomposition); tmp = th_autocovariances(dr,ivar,M_,options_,nodecomposition);
for i=1:nvar for i=1:nvar

12
matlab/dsge_simulated_theoretical_variance_decomposition.m
View File

@ -2,8 +2,8 @@ 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.
% %
% INPUTS % INPUTS
% SampleSize [integer] scalar, number of simulations. % SampleSize [integer] scalar, number of simulations.
% M_ [structure] Dynare structure describing the model. % M_ [structure] Dynare structure describing the model.
% options_ [structure] Dynare structure defining global options. % options_ [structure] Dynare structure defining global options.
@ -11,7 +11,7 @@ function [nvar,vartan,NumberOfDecompFiles] = ...
% type [string] 'prior' or 'posterior' % type [string] 'prior' or 'posterior'
% %
% %
% OUTPUTS % OUTPUTS
% nvar [integer] nvar is the number of stationary variables. % nvar [integer] nvar is the number of stationary variables.
% vartan [char] array of characters (with nvar rows). % vartan [char] array of characters (with nvar rows).
% CovarFileNumber [integer] scalar, number of prior or posterior data files (for covariance). % CovarFileNumber [integer] scalar, number of prior or posterior data files (for covariance).
@ -39,7 +39,7 @@ nodecomposition = 0;
if strcmpi(type,'posterior') if strcmpi(type,'posterior')
DrawsFiles = dir([M_.dname '/metropolis/' M_.fname '_' type '_draws*' ]); DrawsFiles = dir([M_.dname '/metropolis/' M_.fname '_' type '_draws*' ]);
posterior = 1; posterior = 1;
elseif strcmpi(type,'prior') elseif strcmpi(type,'prior')
DrawsFiles = dir([M_.dname '/prior/draws/' type '_draws*' ]); DrawsFiles = dir([M_.dname '/prior/draws/' type '_draws*' ]);
CheckPath('prior/moments'); CheckPath('prior/moments');
posterior = 0; posterior = 0;
@ -66,7 +66,7 @@ nvar = length(ivar);
% Set the size of the auto-correlation function to zero. % Set the size of the auto-correlation function to zero.
nar = options_.ar; nar = options_.ar;
options_.ar = 0; options_.ar = 0;
@ -105,7 +105,7 @@ for file = 1:NumberOfDrawsFiles
dr = pdraws{linee,2}; dr = pdraws{linee,2};
else else
set_parameters(pdraws{linee,1}); set_parameters(pdraws{linee,1});
[dr,info] = resol(oo_.steady_state,0); [dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
end end
tmp = th_autocovariances(dr,ivar,M_,options_,nodecomposition); tmp = th_autocovariances(dr,ivar,M_,options_,nodecomposition);
for i=1:nvar for i=1:nvar

4
matlab/dynare_resolve.m
View File

@ -38,9 +38,9 @@ function [A,B,ys,info] = dynare_resolve(mode)
% 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_ global oo_ M_ oo_
[oo_.dr,info] = resol(oo_.steady_state,0); [oo_.dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
if info(1) > 0 if info(1) > 0
A = []; A = [];

16
matlab/evaluate_likelihood.m
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@ -2,20 +2,20 @@ function [llik,parameters] = evaluate_likelihood(parameters)
% Evaluate the logged likelihood at parameters. % Evaluate the logged likelihood at parameters.
% %
% INPUTS % INPUTS
% o parameters a string ('posterior mode','posterior mean','posterior median','prior mode','prior mean') or a vector of values for % o parameters a string ('posterior mode','posterior mean','posterior median','prior mode','prior mean') or a vector of values for
% the (estimated) parameters of the model. % the (estimated) parameters of the model.
% %
% %
% OUTPUTS % OUTPUTS
% o ldens [double] value of the sample logged density at parameters. % o ldens [double] value of the sample logged density at parameters.
% o parameters [double] vector of values for the estimated parameters. % o parameters [double] vector of values for the estimated parameters.
% %
% SPECIAL REQUIREMENTS % SPECIAL REQUIREMENTS
% None % None
% %
% REMARKS % REMARKS
% [1] This function cannot evaluate the likelihood of a dsge-var model... % [1] This function cannot evaluate the likelihood of a dsge-var model...
% [2] This function use persistent variables for the dataset and the description of the missing observations. Consequently, if this function % [2] This function use persistent variables for the dataset and the description of the missing observations. Consequently, if this function
% is called more than once (by changing the value of parameters) the sample *must not* change. % is called more than once (by changing the value of parameters) the sample *must not* change.
% Copyright (C) 2009-2010 Dynare Team % Copyright (C) 2009-2010 Dynare Team
@ -77,7 +77,7 @@ if isempty(load_data)
% Transform the data. % Transform the data.
if options_.loglinear if options_.loglinear
if ~options_.logdata if ~options_.logdata
rawdata = log(rawdata); rawdata = log(rawdata);
end end
end end
% Test if the data set is real. % Test if the data set is real.
@ -109,7 +109,7 @@ if isempty(load_data)
[ys,tchek] = feval([M_.fname '_steadystate'],... [ys,tchek] = feval([M_.fname '_steadystate'],...
[zeros(M_.exo_nbr,1);... [zeros(M_.exo_nbr,1);...
oo_.exo_det_steady_state]); oo_.exo_det_steady_state]);
if size(ys,1) < M_.endo_nbr if size(ys,1) < M_.endo_nbr
if length(M_.aux_vars) > 0 if length(M_.aux_vars) > 0
ys = add_auxiliary_variables_to_steadystate(ys,M_.aux_vars,... ys = add_auxiliary_variables_to_steadystate(ys,M_.aux_vars,...
M_.fname,... M_.fname,...
@ -123,7 +123,7 @@ if isempty(load_data)
end end
oo_.steady_state = ys; oo_.steady_state = ys;
else% if the steady state file is not provided. else% if the steady state file is not provided.
[dd,info] = resol(oo_.steady_state,0); [dd,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
oo_.steady_state = dd.ys; clear('dd'); oo_.steady_state = dd.ys; clear('dd');
end end
if all(abs(oo_.steady_state(bayestopt_.mfys))<1e-9) if all(abs(oo_.steady_state(bayestopt_.mfys))<1e-9)

16
matlab/evaluate_smoother.m
View File

@ -2,10 +2,10 @@ function oo = evaluate_smoother(parameters)
% Evaluate the smoother at parameters. % Evaluate the smoother at parameters.
% %
% INPUTS % INPUTS
% o parameters a string ('posterior mode','posterior mean','posterior median','prior mode','prior mean') or a vector of values for % o parameters a string ('posterior mode','posterior mean','posterior median','prior mode','prior mean') or a vector of values for
% the (estimated) parameters of the model. % the (estimated) parameters of the model.
% %
% %
% OUTPUTS % OUTPUTS
% o oo [structure] results: % o oo [structure] results:
% - SmoothedVariables % - SmoothedVariables
@ -16,12 +16,12 @@ function oo = evaluate_smoother(parameters)
% - SmoothedVariables % - SmoothedVariables
% - SmoothedVariables % - SmoothedVariables
% - SmoothedVariables % - SmoothedVariables
% %
% SPECIAL REQUIREMENTS % SPECIAL REQUIREMENTS
% None % None
% %
% REMARKS % REMARKS
% [1] This function use persistent variables for the dataset and the description of the missing observations. Consequently, if this function % [1] This function use persistent variables for the dataset and the description of the missing observations. Consequently, if this function
% is called more than once (by changing the value of parameters) the sample *must not* change. % is called more than once (by changing the value of parameters) the sample *must not* change.
% Copyright (C) 2010-2011 Dynare Team % Copyright (C) 2010-2011 Dynare Team
@ -83,7 +83,7 @@ if isempty(load_data)
% Transform the data. % Transform the data.
if options_.loglinear if options_.loglinear
if ~options_.logdata if ~options_.logdata
rawdata = log(rawdata); rawdata = log(rawdata);
end end
end end
% Test if the data set is real. % Test if the data set is real.
@ -115,7 +115,7 @@ if isempty(load_data)
[ys,tchek] = feval([M_.fname '_steadystate'],... [ys,tchek] = feval([M_.fname '_steadystate'],...
[zeros(M_.exo_nbr,1);... [zeros(M_.exo_nbr,1);...
oo_.exo_det_steady_state]); oo_.exo_det_steady_state]);
if size(ys,1) < M_.endo_nbr if size(ys,1) < M_.endo_nbr
if length(M_.aux_vars) > 0 if length(M_.aux_vars) > 0
ys = add_auxiliary_variables_to_steadystate(ys,M_.aux_vars,... ys = add_auxiliary_variables_to_steadystate(ys,M_.aux_vars,...
M_.fname,... M_.fname,...
@ -129,7 +129,7 @@ if isempty(load_data)
end end
oo_.steady_state = ys; oo_.steady_state = ys;
else% if the steady state file is not provided. else% if the steady state file is not provided.
[dd,info] = resol(oo_.steady_state,0); [dd,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
oo_.steady_state = dd.ys; clear('dd'); oo_.steady_state = dd.ys; clear('dd');
end end
if all(abs(oo_.steady_state(bayestopt_.mfys))<1e-9) if all(abs(oo_.steady_state(bayestopt_.mfys))<1e-9)

40
matlab/extended_path.m
View File

@ -1,7 +1,7 @@
function time_series = extended_path(initial_conditions,sample_size,init) function time_series = extended_path(initial_conditions,sample_size,init)
% Stochastic simulation of a non linear DSGE model using the Extended Path method (Fair and Taylor 1983). A time % Stochastic simulation of a non linear DSGE model using the Extended Path method (Fair and Taylor 1983). A time
% series of size T is obtained by solving T perfect foresight models. % series of size T is obtained by solving T perfect foresight models.
% %
% INPUTS % INPUTS
% o initial_conditions [double] m*nlags array, where m is the number of endogenous variables in the model and % o initial_conditions [double] m*nlags array, where m is the number of endogenous variables in the model and
% nlags is the maximum number of lags. % nlags is the maximum number of lags.
@ -9,13 +9,13 @@ function time_series = extended_path(initial_conditions,sample_size,init)
% o init [integer] scalar, method of initialization of the perfect foresight equilibrium paths % o init [integer] scalar, method of initialization of the perfect foresight equilibrium paths
% init=0 previous solution is used, % init=0 previous solution is used,
% init=1 a path generated with the first order reduced form is used. % init=1 a path generated with the first order reduced form is used.
% init=2 mix of cases 0 and 1. % init=2 mix of cases 0 and 1.
% %
% OUTPUTS % OUTPUTS
% o time_series [double] m*sample_size array, the simulations. % o time_series [double] m*sample_size array, the simulations.
% %
% ALGORITHM % ALGORITHM
% %
% SPECIAL REQUIREMENTS % SPECIAL REQUIREMENTS
% Copyright (C) 2009-2010 Dynare Team % Copyright (C) 2009-2010 Dynare Team
@ -34,11 +34,11 @@ function time_series = extended_path(initial_conditions,sample_size,init)
% %
% 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_
% Set default initial conditions. % Set default initial conditions.
if isempty(initial_conditions) if isempty(initial_conditions)
initial_conditions = repmat(oo_.steady_state,1,M_.maximum_lag); initial_conditions = repmat(oo_.steady_state,1,M_.maximum_lag);
end end
% Set default value for the last input argument % Set default value for the last input argument
@ -50,7 +50,7 @@ end
%options_.periods = 40; %options_.periods = 40;
% Initialize the exogenous variables. % Initialize the exogenous variables.
make_ex_; make_ex_;
% Initialize the endogenous variables. % Initialize the endogenous variables.
make_y_; make_y_;
@ -59,7 +59,7 @@ make_y_;
if init if init
oldopt = options_; oldopt = options_;
options_.order = 1; options_.order = 1;
[dr,info]=resol(oo_.steady_state,0); [dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
oo_.dr = dr; oo_.dr = dr;
options_ = oldopt; options_ = oldopt;
if init==2 if init==2
@ -68,16 +68,16 @@ if init
end end
% Initialize the output array. % Initialize the output array.
time_series = NaN(M_.endo_nbr,sample_size+1); time_series = NaN(M_.endo_nbr,sample_size+1);
% Set the covariance matrix of the structural innovations. % Set the covariance matrix of the structural innovations.
variances = diag(M_.Sigma_e); variances = diag(M_.Sigma_e);
positive_var_indx = find(variances>0); positive_var_indx = find(variances>0);
covariance_matrix = M_.Sigma_e(positive_var_indx,positive_var_indx); covariance_matrix = M_.Sigma_e(positive_var_indx,positive_var_indx);
number_of_structural_innovations = length(covariance_matrix); number_of_structural_innovations = length(covariance_matrix);
covariance_matrix_upper_cholesky = chol(covariance_matrix); covariance_matrix_upper_cholesky = chol(covariance_matrix);
tdx = M_.maximum_lag+1; tdx = M_.maximum_lag+1;
norme = 0; norme = 0;
% Set verbose option % Set verbose option
@ -106,7 +106,7 @@ while (t<=sample_size)
if init==1 if init==1
oo_.endo_simul = initial_path(:,1:end-1); oo_.endo_simul = initial_path(:,1:end-1);
else else
oo_.endo_simul = initial_path(:,1:end-1)*lambda + oo_.endo_simul*(1-lambda); oo_.endo_simul = initial_path(:,1:end-1)*lambda + oo_.endo_simul*(1-lambda);
end end
end end
if init if init
@ -141,7 +141,7 @@ while (t<=sample_size)
if new_draw if new_draw
info.time = info.time+time; info.time = info.time+time;
time_series(:,t+1) = oo_.endo_simul(:,tdx); time_series(:,t+1) = oo_.endo_simul(:,tdx);
oo_.endo_simul(:,1:end-1) = oo_.endo_simul(:,2:end); oo_.endo_simul(:,1:end-1) = oo_.endo_simul(:,2:end);
oo_.endo_simul(:,end) = oo_.steady_state; oo_.endo_simul(:,end) = oo_.steady_state;
end end
end end

8
matlab/forcst_unc.m
View File

@ -71,7 +71,7 @@ for i=1:replic
params = rndprior(bayestopt_); params = rndprior(bayestopt_);
set_parameters(params); set_parameters(params);
% solve the model % solve the model
[dr,info] = resol(oo_.steady_state,0); [dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
% discard problematic cases % discard problematic cases
if info if info
continue continue
@ -123,7 +123,7 @@ end
% compute shock uncertainty around forecast with mean prior % compute shock uncertainty around forecast with mean prior
set_parameters(bayestopt_.p1); set_parameters(bayestopt_.p1);
[dr,info] = resol(oo_.steady_state,0); [dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
[yf3,yf3_intv] = forcst(dr,y0,periods,var_list); [yf3,yf3_intv] = forcst(dr,y0,periods,var_list);
yf3_1 = yf3'-[zeros(maximum_lag,n); yf3_intv]; yf3_1 = yf3'-[zeros(maximum_lag,n); yf3_intv];
yf3_2 = yf3'+[zeros(maximum_lag,n); yf3_intv]; yf3_2 = yf3'+[zeros(maximum_lag,n); yf3_intv];
@ -147,7 +147,7 @@ dynare_graph_close;
% saving results % saving results
save_results(yf_mean,'oo_.forecast.mean.',var_list); save_results(yf_mean,'oo_.forecast.mean.',var_list);
save_results(yf1(:,:,k1(1)),'oo_.forecast.HPDinf.',var_list); save_results(yf1(:,:,k1(1)),'oo_.forecast.HPDinf.',var_list);
save_results(yf1(:,:,k1(2)),'oo_.forecast.HPDsup.',var_list); save_results(yf1(:,:,k1(2)),'oo_.forecast.HPDsup.',var_list);
save_results(yf2(:,:,k2(1)),'oo_.forecast.HPDTotalinf.',var_list); save_results(yf2(:,:,k2(1)),'oo_.forecast.HPDTotalinf.',var_list);
save_results(yf2(:,:,k2(2)),'oo_.forecast.HPDTotalsup.',var_list); save_results(yf2(:,:,k2(2)),'oo_.forecast.HPDTotalsup.',var_list);

14
matlab/gsa/dynare_MC.m
View File

@ -9,10 +9,10 @@ function dynare_MC(var_list_,OutDir,data,rawdata,data_info)
% Written by Marco Ratto, 2006 % Written by Marco Ratto, 2006
% Joint Research Centre, The European Commission, % Joint Research Centre, The European Commission,
% (http://eemc.jrc.ec.europa.eu/), % (http://eemc.jrc.ec.europa.eu/),
% marco.ratto@jrc.it % marco.ratto@jrc.it
% %
% Disclaimer: This software is not subject to copyright protection and is in the public domain. % Disclaimer: This software is not subject to copyright protection and is in the public domain.
% It is an experimental system. The Joint Research Centre of European Commission % It is an experimental system. The Joint Research Centre of European Commission
% assumes no responsibility whatsoever for its use by other parties % assumes no responsibility whatsoever for its use by other parties
% and makes no guarantees, expressed or implied, about its quality, reliability, or any other % and makes no guarantees, expressed or implied, about its quality, reliability, or any other
% characteristic. We would appreciate acknowledgement if the software is used. % characteristic. We would appreciate acknowledgement if the software is used.
@ -20,7 +20,7 @@ function dynare_MC(var_list_,OutDir,data,rawdata,data_info)
% M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006. % M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
% %
global M_ options_ oo_ estim_params_ global M_ options_ oo_ estim_params_
global bayestopt_ global bayestopt_
% if options_.filtered_vars ~= 0 & options_.filter_step_ahead == 0 % if options_.filtered_vars ~= 0 & options_.filter_step_ahead == 0
@ -31,7 +31,7 @@ global bayestopt_
% else % else
% options_.nk = 0; % options_.nk = 0;
% end % end
% %
options_.filter_step_ahead=1; options_.filter_step_ahead=1;
options_.nk = 1; options_.nk = 1;
@ -98,7 +98,7 @@ for b=1:B
ib=ib+1; ib=ib+1;
deep = x(b,:)'; deep = x(b,:)';
set_all_parameters(deep); set_all_parameters(deep);
dr = resol(oo_.steady_state,0); [dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
%deep(1:offset) = xparam1(1:offset); %deep(1:offset) = xparam1(1:offset);
logpo2(b,1) = DsgeLikelihood(deep,gend,data,data_index,number_of_observations,no_more_missing_observations); logpo2(b,1) = DsgeLikelihood(deep,gend,data,data_index,number_of_observations,no_more_missing_observations);
if opt_gsa.lik_only==0, if opt_gsa.lik_only==0,
@ -115,7 +115,7 @@ for b=1:B
stock_filter = zeros(M_.endo_nbr,gend+1,40); stock_filter = zeros(M_.endo_nbr,gend+1,40);
stock_ys = zeros(40, M_.endo_nbr); stock_ys = zeros(40, M_.endo_nbr);
end end
end end
waitbar(b/B,h,['MC smoother ...',num2str(b),'/',num2str(B)]); waitbar(b/B,h,['MC smoother ...',num2str(b),'/',num2str(B)]);
end end
close(h) close(h)

32
matlab/gsa/stab_map_.m
View File

@ -101,12 +101,12 @@ if fload==0,
% if prepSA % if prepSA
% T=zeros(size(dr_.ghx,1),size(dr_.ghx,2)+size(dr_.ghu,2),Nsam/2); % T=zeros(size(dr_.ghx,1),size(dr_.ghx,2)+size(dr_.ghu,2),Nsam/2);
% end % end
if isfield(dr_,'ghx'), if isfield(dr_,'ghx'),
egg=zeros(length(dr_.eigval),Nsam); egg=zeros(length(dr_.eigval),Nsam);
end end
yys=zeros(length(dr_.ys),Nsam); yys=zeros(length(dr_.ys),Nsam);
if opt_gsa.morris == 1 if opt_gsa.morris == 1
[lpmat, OutFact] = Sampling_Function_2(nliv, np+nshock, ntra, ones(np+nshock, 1), zeros(np+nshock,1), []); [lpmat, OutFact] = Sampling_Function_2(nliv, np+nshock, ntra, ones(np+nshock, 1), zeros(np+nshock,1), []);
lpmat = lpmat.*(nliv-1)/nliv+1/nliv/2; lpmat = lpmat.*(nliv-1)/nliv+1/nliv/2;
@ -129,7 +129,7 @@ if fload==0,
for j=1:np, for j=1:np,
lpmat(:,j) = randperm(Nsam)'./(Nsam+1); %latin hypercube lpmat(:,j) = randperm(Nsam)'./(Nsam+1); %latin hypercube
end end
end end
end end
% try % try
@ -220,7 +220,7 @@ if fload==0,
ub=min([bayestopt_.ub(j+nshock) xparam1(j+nshock)*(1+neighborhood_width)]); ub=min([bayestopt_.ub(j+nshock) xparam1(j+nshock)*(1+neighborhood_width)]);
lb=max([bayestopt_.lb(j+nshock) xparam1(j+nshock)*(1-neighborhood_width)]); lb=max([bayestopt_.lb(j+nshock) xparam1(j+nshock)*(1-neighborhood_width)]);
lpmat(:,j)=lpmat(:,j).*(ub-lb)+lb; lpmat(:,j)=lpmat(:,j).*(ub-lb)+lb;
end end
else else
d = chol(inv(hh)); d = chol(inv(hh));
lp=randn(Nsam*2,nshock+np)*d+kron(ones(Nsam*2,1),xparam1'); lp=randn(Nsam*2,nshock+np)*d+kron(ones(Nsam*2,1),xparam1');
@ -318,7 +318,7 @@ if fload==0,
iunstable=iunstable(find(iunstable)); % unstable params iunstable=iunstable(find(iunstable)); % unstable params
iindeterm=iindeterm(find(iindeterm)); % indeterminacy iindeterm=iindeterm(find(iindeterm)); % indeterminacy
iwrong=iwrong(find(iwrong)); % dynare could not find solution iwrong=iwrong(find(iwrong)); % dynare could not find solution
% % map stable samples % % map stable samples
% istable=[1:Nsam]; % istable=[1:Nsam];
% for j=1:Nsam, % for j=1:Nsam,
@ -368,7 +368,7 @@ if fload==0,
'bkpprior','lpmat','lpmat0','iunstable','istable','iindeterm','iwrong', ... 'bkpprior','lpmat','lpmat0','iunstable','istable','iindeterm','iwrong', ...
'egg','yys','T','nspred','nboth','nfwrd') 'egg','yys','T','nspred','nboth','nfwrd')
end end
else else
if ~prepSA if ~prepSA
save([OutputDirectoryName '/' fname_ '_mc'], ... save([OutputDirectoryName '/' fname_ '_mc'], ...
@ -388,8 +388,8 @@ else
end end
load(filetoload,'lpmat','lpmat0','iunstable','istable','iindeterm','iwrong','egg','yys','nspred','nboth','nfwrd') load(filetoload,'lpmat','lpmat0','iunstable','istable','iindeterm','iwrong','egg','yys','nspred','nboth','nfwrd')
Nsam = size(lpmat,1); Nsam = size(lpmat,1);
if prepSA & isempty(strmatch('T',who('-file', filetoload),'exact')), if prepSA & isempty(strmatch('T',who('-file', filetoload),'exact')),
h = waitbar(0,'Please wait...'); h = waitbar(0,'Please wait...');
options_.periods=0; options_.periods=0;
@ -486,7 +486,7 @@ if length(iunstable)>0 & length(iunstable)<Nsam,
stab_map_1(lpmat, [1:Nsam], iindeterm, [aname, '_indet'], 1, indindet, OutputDirectoryName); stab_map_1(lpmat, [1:Nsam], iindeterm, [aname, '_indet'], 1, indindet, OutputDirectoryName);
end end
end end
if ~isempty(ixun), if ~isempty(ixun),
[proba, dproba] = stab_map_1(lpmat, [1:Nsam], ixun, [aname, '_unst'],0); [proba, dproba] = stab_map_1(lpmat, [1:Nsam], ixun, [aname, '_unst'],0);
% indunst=find(dproba>ksstat); % indunst=find(dproba>ksstat);
@ -500,7 +500,7 @@ if length(iunstable)>0 & length(iunstable)<Nsam,
stab_map_1(lpmat, [1:Nsam], ixun, [aname, '_unst'], 1, indunst, OutputDirectoryName); stab_map_1(lpmat, [1:Nsam], ixun, [aname, '_unst'], 1, indunst, OutputDirectoryName);
end end
end end
if ~isempty(iwrong), if ~isempty(iwrong),
[proba, dproba] = stab_map_1(lpmat, [1:Nsam], iwrong, [aname, '_wrong'],0); [proba, dproba] = stab_map_1(lpmat, [1:Nsam], iwrong, [aname, '_wrong'],0);
% indwrong=find(dproba>ksstat); % indwrong=find(dproba>ksstat);
@ -514,13 +514,13 @@ if length(iunstable)>0 & length(iunstable)<Nsam,
stab_map_1(lpmat, [1:Nsam], iwrong, [aname, '_wrong'], 1, indwrong, OutputDirectoryName); stab_map_1(lpmat, [1:Nsam], iwrong, [aname, '_wrong'], 1, indwrong, OutputDirectoryName);
end end
end end
disp(' ') disp(' ')
disp('Starting bivariate analysis:') disp('Starting bivariate analysis:')
c0=corrcoef(lpmat(istable,:)); c0=corrcoef(lpmat(istable,:));
c00=tril(c0,-1); c00=tril(c0,-1);
stab_map_2(lpmat(istable,:),alpha2, pvalue_corr, asname, OutputDirectoryName); stab_map_2(lpmat(istable,:),alpha2, pvalue_corr, asname, OutputDirectoryName);
if length(iunstable)>10, if length(iunstable)>10,
stab_map_2(lpmat(iunstable,:),alpha2, pvalue_corr, auname, OutputDirectoryName); stab_map_2(lpmat(iunstable,:),alpha2, pvalue_corr, auname, OutputDirectoryName);
@ -534,12 +534,12 @@ if length(iunstable)>0 & length(iunstable)<Nsam,
if length(iwrong)>10, if length(iwrong)>10,
stab_map_2(lpmat(iwrong,:),alpha2, pvalue_corr, awrongname, OutputDirectoryName); stab_map_2(lpmat(iwrong,:),alpha2, pvalue_corr, awrongname, OutputDirectoryName);
end end
x0=0.5.*(bayestopt_.ub(1:nshock)-bayestopt_.lb(1:nshock))+bayestopt_.lb(1:nshock); x0=0.5.*(bayestopt_.ub(1:nshock)-bayestopt_.lb(1:nshock))+bayestopt_.lb(1:nshock);
x0 = [x0; lpmat(istable(1),:)']; x0 = [x0; lpmat(istable(1),:)'];
if istable(end)~=Nsam if istable(end)~=Nsam
M_.params(estim_params_.param_vals(:,1)) = lpmat(istable(1),:)'; M_.params(estim_params_.param_vals(:,1)) = lpmat(istable(1),:)';
[oo_.dr, info] = resol(oo_.steady_state,0); [oo_.dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
% stoch_simul([]); % stoch_simul([]);
end end
else else
@ -551,7 +551,7 @@ else
disp('All parameter values in the specified ranges are not acceptable!') disp('All parameter values in the specified ranges are not acceptable!')
x0=[]; x0=[];
end end
end end

4
matlab/osr1.m
View File

@ -46,7 +46,7 @@ dr = set_state_space(oo_.dr,M_);
if exist([M_.fname '_steadystate']) if exist([M_.fname '_steadystate'])
[ys,check1] = feval([M_.fname '_steadystate'],oo_.steady_state,... [ys,check1] = feval([M_.fname '_steadystate'],oo_.steady_state,...
[oo_.exo_steady_state; oo_.exo_det_steady_state]); [oo_.exo_steady_state; oo_.exo_det_steady_state]);
if size(ys,1) < M_.endo_nbr if size(ys,1) < M_.endo_nbr
if length(M_.aux_vars) > 0 if length(M_.aux_vars) > 0
ys = add_auxiliary_variables_to_steadystate(ys,M_.aux_vars,... ys = add_auxiliary_variables_to_steadystate(ys,M_.aux_vars,...
M_.fname,... M_.fname,...
@ -114,6 +114,6 @@ for i=1:np
end end
disp(sprintf('Objective function : %16.6g\n',f)); disp(sprintf('Objective function : %16.6g\n',f));
disp(' ') disp(' ')
oo_.dr=resol(oo_.steady_state,0); [oo_.dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
% 05/10/03 MJ modified to work with osr.m and give full report % 05/10/03 MJ modified to work with osr.m and give full report

6
matlab/osr_obj.m
View File

@ -18,7 +18,7 @@ function [loss,vx,info]=osr_obj(x,i_params,i_var,weights);
% 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_ optimal_Q_ it_ global M_ oo_ options_ optimal_Q_ it_
% global ys_ Sigma_e_ endo_nbr exo_nbr optimal_Q_ it_ ykmin_ options_ % global ys_ Sigma_e_ endo_nbr exo_nbr optimal_Q_ it_ ykmin_ options_
vx = []; vx = [];
@ -27,7 +27,7 @@ M_.params(i_params) = x;
% don't change below until the part where the loss function is computed % don't change below until the part where the loss function is computed
it_ = M_.maximum_lag+1; it_ = M_.maximum_lag+1;
[dr,info] = resol(oo_.steady_state,0); [dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
switch info(1) switch info(1)
case 1 case 1
@ -54,7 +54,7 @@ switch info(1)
otherwise otherwise
end end
vx = get_variance_of_endogenous_variables(dr,i_var); vx = get_variance_of_endogenous_variables(dr,i_var);
loss = weights(:)'*vx(:); loss = weights(:)'*vx(:);

74
matlab/perfect_foresight_simulation.m
View File

@ -4,8 +4,8 @@ function info = perfect_foresight_simulation(compute_linear_solution,steady_stat
% INPUTS % INPUTS
% endo_simul [double] n*T matrix, where n is the number of endogenous variables. % endo_simul [double] n*T matrix, where n is the number of endogenous variables.
% exo_simul [double] q*T matrix, where q is the number of shocks. % exo_simul [double] q*T matrix, where q is the number of shocks.
% compute_linear_solution [integer] scalar equal to zero or one. % compute_linear_solution [integer] scalar equal to zero or one.
% %
% OUTPUTS % OUTPUTS
% none % none
% %
@ -40,19 +40,19 @@ global M_ options_ it_ oo_
persistent lead_lag_incidence dynamic_model ny nyp nyf nrs nrc iyf iyp isp is isf isf1 iz icf ghx iflag persistent lead_lag_incidence dynamic_model ny nyp nyf nrs nrc iyf iyp isp is isf isf1 iz icf ghx iflag
if ~nargin && isempty(iflag)% Initialization of the persistent variables. if ~nargin && isempty(iflag)% Initialization of the persistent variables.
lead_lag_incidence = M_.lead_lag_incidence; lead_lag_incidence = M_.lead_lag_incidence;
dynamic_model = [M_.fname '_dynamic']; dynamic_model = [M_.fname '_dynamic'];
ny = size(oo_.endo_simul,1); ny = size(oo_.endo_simul,1);
nyp = nnz(lead_lag_incidence(1,:));% number of lagged variables. nyp = nnz(lead_lag_incidence(1,:));% number of lagged variables.
nyf = nnz(lead_lag_incidence(3,:));% number of leaded variables. nyf = nnz(lead_lag_incidence(3,:));% number of leaded variables.
nrs = ny+nyp+nyf+1; nrs = ny+nyp+nyf+1;
nrc = nyf+1; nrc = nyf+1;
iyf = find(lead_lag_incidence(3,:)>0);% indices for leaded variables. iyf = find(lead_lag_incidence(3,:)>0);% indices for leaded variables.
iyp = find(lead_lag_incidence(1,:)>0);% indices for lagged variables. iyp = find(lead_lag_incidence(1,:)>0);% indices for lagged variables.
isp = 1:nyp; isp = 1:nyp;
is = (nyp+1):(nyp+ny); % Indices for contemporaneaous variables. is = (nyp+1):(nyp+ny); % Indices for contemporaneaous variables.
isf = iyf+nyp; isf = iyf+nyp;
isf1 = (nyp+ny+1):(nyf+nyp+ny+1); isf1 = (nyp+ny+1):(nyf+nyp+ny+1);
iz = 1:(ny+nyp+nyf); iz = 1:(ny+nyp+nyf);
icf = 1:size(iyf,2); icf = 1:size(iyf,2);
info = []; info = [];
@ -73,8 +73,8 @@ else
end end
end end
if ~isstruct(compute_linear_solution) && compute_linear_solution if ~isstruct(compute_linear_solution) && compute_linear_solution
[dr,info]=resol(steady_state,0); [dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
elseif isstruct(compute_linear_solution) elseif isstruct(compute_linear_solution)
dr = compute_linear_solution; dr = compute_linear_solution;
compute_linear_solution = 1; compute_linear_solution = 1;
@ -85,22 +85,22 @@ if compute_linear_solution
ghx = ghx(iyf,:); ghx = ghx(iyf,:);
end end
periods = options_.periods; periods = options_.periods;
stop = 0 ; stop = 0 ;
it_init = M_.maximum_lag+1; it_init = M_.maximum_lag+1;
info.convergence = 1; info.convergence = 1;
info.time = 0; info.time = 0;
info.error = 0; info.error = 0;
info.iterations.time = zeros(options_.maxit_,1); info.iterations.time = zeros(options_.maxit_,1);
info.iterations.error = info.iterations.time; info.iterations.error = info.iterations.time;
last_line = options_.maxit_; last_line = options_.maxit_;
error_growth = 0; error_growth = 0;
h1 = clock; h1 = clock;
for iter = 1:options_.maxit_ for iter = 1:options_.maxit_
h2 = clock; h2 = clock;
if options_.terminal_condition if options_.terminal_condition
c = zeros(ny*(periods+1),nrc); c = zeros(ny*(periods+1),nrc);
@ -108,23 +108,23 @@ for iter = 1:options_.maxit_
c = zeros(ny*periods,nrc); c = zeros(ny*periods,nrc);
end end
it_ = it_init; it_ = it_init;
z = [ oo_.endo_simul(iyp,it_-1) ; oo_.endo_simul(:,it_) ; oo_.endo_simul(iyf,it_+1) ]; z = [ oo_.endo_simul(iyp,it_-1) ; oo_.endo_simul(:,it_) ; oo_.endo_simul(iyf,it_+1) ];
[d1,jacobian] = feval(dynamic_model,z,oo_.exo_simul, M_.params, it_); [d1,jacobian] = feval(dynamic_model,z,oo_.exo_simul, M_.params, it_);
jacobian = [jacobian(:,iz) , -d1]; jacobian = [jacobian(:,iz) , -d1];
ic = 1:ny; ic = 1:ny;
icp = iyp; icp = iyp;
c(ic,:) = jacobian(:,is)\jacobian(:,isf1) ; c(ic,:) = jacobian(:,is)\jacobian(:,isf1) ;
for it_ = it_init+(1:periods-1-(options_.terminal_condition==2)) for it_ = it_init+(1:periods-1-(options_.terminal_condition==2))
z = [ oo_.endo_simul(iyp,it_-1) ; oo_.endo_simul(:,it_) ; oo_.endo_simul(iyf,it_+1)]; z = [ oo_.endo_simul(iyp,it_-1) ; oo_.endo_simul(:,it_) ; oo_.endo_simul(iyf,it_+1)];
[d1,jacobian] = feval(dynamic_model,z,oo_.exo_simul, M_.params, it_); [d1,jacobian] = feval(dynamic_model,z,oo_.exo_simul, M_.params, it_);
jacobian = [jacobian(:,iz) , -d1]; jacobian = [jacobian(:,iz) , -d1];
jacobian(:,[isf nrs]) = jacobian(:,[isf nrs])-jacobian(:,isp)*c(icp,:); jacobian(:,[isf nrs]) = jacobian(:,[isf nrs])-jacobian(:,isp)*c(icp,:);
ic = ic + ny; ic = ic + ny;
icp = icp + ny; icp = icp + ny;
c(ic,:) = jacobian(:,is)\jacobian(:,isf1); c(ic,:) = jacobian(:,is)\jacobian(:,isf1);
end end
if options_.terminal_condition if options_.terminal_condition
if options_.terminal_condition==1% Terminal condition is Y_{T} = Y_{T+1} if options_.terminal_condition==1% Terminal condition is Y_{T} = Y_{T+1}
s = eye(ny); s = eye(ny);
s(:,isf) = s(:,isf)+c(ic,1:nyf); s(:,isf) = s(:,isf)+c(ic,1:nyf);
ic = ic + ny; ic = ic + ny;
@ -147,10 +147,10 @@ for iter = 1:options_.maxit_
else% Terminal condition is Y_{T}=Y^{\star} else% Terminal condition is Y_{T}=Y^{\star}
c = bksup0(c,ny,nrc,iyf,icf,periods); c = bksup0(c,ny,nrc,iyf,icf,periods);
c = reshape(c,ny,periods); c = reshape(c,ny,periods);
oo_.endo_simul(:,it_init+(0:periods-1)) = oo_.endo_simul(:,it_init+(0:periods-1))+options_.slowc*c; oo_.endo_simul(:,it_init+(0:periods-1)) = oo_.endo_simul(:,it_init+(0:periods-1))+options_.slowc*c;
end end
err = max(max(abs(c))); err = max(max(abs(c)));
info.iterations.time(iter) = etime(clock,h2); info.iterations.time(iter) = etime(clock,h2);
info.iterations.error(iter) = err; info.iterations.error(iter) = err;
if iter>1 if iter>1
error_growth = error_growth + (info.iterations.error(iter)>info.iterations.error(iter-1)); error_growth = error_growth + (info.iterations.error(iter)>info.iterations.error(iter-1));
@ -161,16 +161,16 @@ for iter = 1:options_.maxit_
end end
if err < options_.dynatol if err < options_.dynatol
stop = 1; stop = 1;
info.time = etime(clock,h1); info.time = etime(clock,h1);
info.error = err; info.error = err;
info.iterations.time = info.iterations.time(1:iter); info.iterations.time = info.iterations.time(1:iter);
info.iterations.error = info.iterations.error(1:iter); info.iterations.error = info.iterations.error(1:iter);
break break
end end
end end
if stop && options_.terminal_condition==2 if stop && options_.terminal_condition==2
% Compute the distance to the deterministic steady state (for the subset of endogenous variables with a non zero % Compute the distance to the deterministic steady state (for the subset of endogenous variables with a non zero
% steady state) at the last perdiod. % steady state) at the last perdiod.
idx = find(abs(oo_.steady_state)>0); idx = find(abs(oo_.steady_state)>0);
distance_to_steady_state = abs(((oo_.endo_simul(idx,end)-oo_.steady_state(idx))./oo_.steady_state(idx)))*100; distance_to_steady_state = abs(((oo_.endo_simul(idx,end)-oo_.steady_state(idx))./oo_.steady_state(idx)))*100;

2
matlab/prior_posterior_statistics_core.m
View File

@ -162,7 +162,7 @@ for b=fpar:B
end end
end end
set_all_parameters(deep); set_all_parameters(deep);
[dr,info] = resol(oo_.steady_state,0); [dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
if run_smoother if run_smoother
[alphahat,etahat,epsilonhat,alphatilde,SteadyState,trend_coeff,aK] = ... [alphahat,etahat,epsilonhat,alphatilde,SteadyState,trend_coeff,aK] = ...

10
matlab/prior_sampler.m
View File

@ -2,13 +2,13 @@ function results = prior_sampler(drsave,M_,bayestopt_,options_,oo_)
% This function builds a (big) prior sample. % This function builds a (big) prior sample.
% %
% INPUTS % INPUTS
% drsave [integer] Scalar. If equal to 1, then dr structure is saved with each prior draw. % drsave [integer] Scalar. If equal to 1, then dr structure is saved with each prior draw.
% M_ [structure] Model description. % M_ [structure] Model description.
% bayestopt_ [structure] Prior distribution description. % bayestopt_ [structure] Prior distribution description.
% options_ [structure] Global options of Dynare. % options_ [structure] Global options of Dynare.
% %
% OUTPUTS: % OUTPUTS:
% results [structure] Various statistics. % results [structure] Various statistics.
% %
% SPECIAL REQUIREMENTS % SPECIAL REQUIREMENTS
% none % none
@ -80,7 +80,7 @@ while iteration < NumberOfSimulations
loop_indx = loop_indx+1; loop_indx = loop_indx+1;
params = prior_draw(); params = prior_draw();
set_all_parameters(params); set_all_parameters(params);
[dr,INFO] = resol(oo_.steady_state,work); [dr,INFO,M_,options_,oo_] = resol(work,M_,options_,oo_);
switch INFO(1) switch INFO(1)
case 0 case 0
file_line_number = file_line_number + 1 ; file_line_number = file_line_number + 1 ;

228
matlab/resol.m
View File

@ -1,32 +1,80 @@
function [dr,info]=resol(steady_state_0,check_flag) function [dr,info,M,options,oo] = resol(check_flag,M,options,oo)
% function [dr,info]=resol(steady_state_0,check_flag)
% Computes first and second order approximations %@info:
% %! @deftypefn {Function File} {[@var{dr},@var{info},@var{M},@var{options},@var{oo}] =} resol (@var{check_flag},@var{M},@var{options},@var{oo})
% INPUTS %! @anchor{resol}
% steady_state_0: vector of variables in steady state %! @sp 1
% check_flag=0: all the approximation is computed %! Computes first and second order reduced form of the DSGE model.
% check_flag=1: computes only the eigenvalues %! @sp 2
% %! @strong{Inputs}
% OUTPUTS %! @sp 1
% dr: structure of decision rules for stochastic simulations %! @table @ @var
% info=1: the model doesn't determine the current variables '...' uniquely %! @item check_flag
% info=2: MJDGGES returns the following error code' %! Integer scalar, equal to 0 if all the approximation is required, positive if only the eigenvalues are to be computed.
% info=3: Blanchard Kahn conditions are not satisfied: no stable '...' equilibrium %! @item M
% info=4: Blanchard Kahn conditions are not satisfied:'...' indeterminacy %! Matlab's structure describing the model (initialized by @code{dynare}).
% info=5: Blanchard Kahn conditions are not satisfied:'...' indeterminacy due to rank failure %! @item options
% info=6: The jacobian evaluated at the steady state is complex. %! Matlab's structure describing the options (initialized by @code{dynare}).
% info=19: The steadystate file did not compute the steady state (inconsistent deep parameters). %! @item oo
% info=20: can't find steady state info(2) contains sum of sqare residuals %! Matlab's structure gathering the results (initialized by @code{dynare}).
% info=21: steady state is complex valued scalars %! @end table
% info(2) contains sum of square of %! @sp 2
% imaginary part of steady state %! @strong{Outputs}
% info=22: steady state has NaNs %! @sp 1
% info=23: M_.params has been updated in the steady state file and has complex valued scalars. %! @table @ @var
% info=24: M_.params has been updated in the steady state file and has some NaNs. %! @item dr
% info=30: Variance can't be computed %! Matlab's structure describing the reduced form solution of the model.
% %! @item info
% SPECIAL REQUIREMENTS %! Integer scalar, error code.
% none %! @sp 1
%! @table @ @code
%! @item info==0
%! No error.
%! @item info==1
%! The model doesn't determine the current variables uniquely.
%! @item info==2
%! MJDGGES returned an error code.
%! @item info==3
%! Blanchard & Kahn conditions are not satisfied: no stable equilibrium.
%! @item info==4
%! Blanchard & Kahn conditions are not satisfied: indeterminacy.
%! @item info==5
%! Blanchard & Kahn conditions are not satisfied: indeterminacy due to rank failure.
%! @item info==6
%! The jacobian evaluated at the deterministic steady state is complex.
%! @item info==19
%! The steadystate routine thrown an exception (inconsistent deep parameters).
%! @item info==20
%! Cannot find the steady state, info(2) contains the sum of square residuals (of the static equations).
%! @item info==21
%! The steady state is complex, info(2) contains the sum of square of imaginary parts of the steady state.
%! @item info==22
%! The steady has NaNs.
%! @item info==23
%! M_.params has been updated in the steadystate routine and has complex valued scalars.
%! @item info==24
%! M_.params has been updated in the steadystate routine and has some NaNs.
%! @item info==30
%! Ergodic variance can't be computed.
%! @end table
%! @sp 1
%! @item M
%! Matlab's structure describing the model (initialized by @code{dynare}).
%! @item options
%! Matlab's structure describing the options (initialized by @code{dynare}).
%! @item oo
%! Matlab's structure gathering the results (initialized by @code{dynare}).
%! @end table
%! @sp 2
%! @strong{This function is called by:}
%! @sp 1
%! @ref{dynare_estimation_init}
%! @sp 2
%! @strong{This function calls:}
%! @sp 1
%! None.
%! @end deftypefn
%@eod:
% Copyright (C) 2001-2011 Dynare Team % Copyright (C) 2001-2011 Dynare Team
% %
@ -45,94 +93,93 @@ function [dr,info]=resol(steady_state_0,check_flag)
% 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 it_ global it_
jacobian_flag = 0; jacobian_flag = 0;
if isfield(oo_,'dr'); if isfield(oo,'dr');
dr = oo_.dr; dr = oo.dr;
end end
options_ = set_default_option(options_,'jacobian_flag',1); options = set_default_option(options,'jacobian_flag',1);
info = 0; info = 0;
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 end
params0 = M_.params; params0 = M.params;
% check if steady_state_0 is steady state % check if steady_state_0 is steady state
tempex = oo_.exo_simul; tempex = oo.exo_simul;
oo_.exo_simul = repmat(oo_.exo_steady_state',M_.maximum_lag+M_.maximum_lead+1,1); oo.exo_simul = repmat(oo.exo_steady_state',M.maximum_lag+M.maximum_lead+1,1);
if M_.exo_det_nbr > 0 if M.exo_det_nbr > 0
tempexdet = oo_.exo_det_simul; tempexdet = oo.exo_det_simul;
oo_.exo_det_simul = repmat(oo_.exo_det_steady_state',M_.maximum_lag+M_.maximum_lead+1,1); oo.exo_det_simul = repmat(oo.exo_det_steady_state',M.maximum_lag+M.maximum_lead+1,1);
end end
steady_state = steady_state_0; steady_state = steady_state_0;
check1 = 0; check1 = 0;
% testing for steadystate file % testing for steadystate file
if (~options_.bytecode) if (~options.bytecode)
fh = str2func([M_.fname '_static']); fh = str2func([M.fname '_static']);
end end
if options_.steadystate_flag if options.steadystate_flag
[steady_state,check1] = feval([M_.fname '_steadystate'],steady_state,... [steady_state,check1] = feval([M.fname '_steadystate'],steady_state,...
[oo_.exo_steady_state; ... [oo.exo_steady_state; ...
oo_.exo_det_steady_state]); oo.exo_det_steady_state]);
if size(steady_state,1) < M_.endo_nbr if size(steady_state,1) < M.endo_nbr
if length(M_.aux_vars) > 0 if length(M.aux_vars) > 0
steady_state = add_auxiliary_variables_to_steadystate(steady_state,M_.aux_vars,... steady_state = add_auxiliary_variables_to_steadystate(steady_state,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,...
options_.bytecode); options.bytecode);
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
else else
% testing if steady_state_0 isn't a steady state or if we aren't computing Ramsey policy % testing if steady_state_0 isn't a steady state or if we aren't computing Ramsey policy
if options_.ramsey_policy == 0 if options.ramsey_policy == 0
if options_.linear == 0 if options.linear == 0
% nonlinear models % nonlinear models
if (options_.block == 0 && options_.bytecode == 0) if (options.block == 0 && options.bytecode == 0)
if max(abs(feval(fh,steady_state,[oo_.exo_steady_state; ... if max(abs(feval(fh,steady_state,[oo.exo_steady_state; ...
oo_.exo_det_steady_state], M_.params))) > options_.dynatol oo.exo_det_steady_state], M.params))) > options.dynatol
[steady_state,check1] = dynare_solve(fh,steady_state,options_.jacobian_flag,... [steady_state,check1] = dynare_solve(fh,steady_state,options.jacobian_flag,...
[oo_.exo_steady_state; ... [oo.exo_steady_state; ...
oo_.exo_det_steady_state], M_.params); oo.exo_det_steady_state], M.params);
end end
else else
[steady_state,check1] = dynare_solve_block_or_bytecode(steady_state,... [steady_state,check1] = dynare_solve_block_or_bytecode(steady_state,...
[oo_.exo_steady_state; ... [oo.exo_steady_state; ...
oo_.exo_det_steady_state], M_.params); oo.exo_det_steady_state], M.params);
end; end;
else else
if (options_.block == 0 && options_.bytecode == 0) if (options.block == 0 && options.bytecode == 0)
% linear models % linear models
[fvec,jacob] = feval(fh,steady_state,[oo_.exo_steady_state;... [fvec,jacob] = feval(fh,steady_state,[oo.exo_steady_state;...
oo_.exo_det_steady_state], M_.params); oo.exo_det_steady_state], M.params);
if max(abs(fvec)) > 1e-12 if max(abs(fvec)) > 1e-12
steady_state = steady_state-jacob\fvec; steady_state = steady_state-jacob\fvec;
end end
else else
[steady_state,check1] = dynare_solve_block_or_bytecode(steady_state,... [steady_state,check1] = dynare_solve_block_or_bytecode(steady_state,...
[oo_.exo_steady_state; ... [oo.exo_steady_state; ...
oo_.exo_det_steady_state], M_.params); oo.exo_det_steady_state], M.params);
end; end;
end end
end end
end end
% test if M_.params_has changed. % test if M.params_has changed.
if options_.steadystate_flag if options.steadystate_flag
updated_params_flag = max(abs(M_.params-params0))>1e-12; updated_params_flag = max(abs(M.params-params0))>1e-12;
else else
updated_params_flag = 0; updated_params_flag = 0;
end end
@ -141,12 +188,12 @@ end
dr.ys = steady_state; dr.ys = steady_state;
if check1 if check1
if options_.steadystate_flag if options.steadystate_flag
info(1)= 19; info(1)= 19;
resid = check1 ; resid = check1 ;
else else
info(1)= 20; info(1)= 20;
resid = feval(fh,steady_state_0,oo_.exo_steady_state, M_.params); resid = feval(fh,steady_state_0,oo.exo_steady_state, M.params);
end end
info(2) = resid'*resid ; info(2) = resid'*resid ;
return return
@ -167,14 +214,14 @@ if ~isempty(find(isnan(steady_state)))
return return
end end
if options_.steadystate_flag && updated_params_flag && ~isreal(M_.params) if options.steadystate_flag && updated_params_flag && ~isreal(M.params)
info(1) = 23; info(1) = 23;
info(2) = sum(imag(M_.params).^2); info(2) = sum(imag(M.params).^2);
dr.ys = steady_state; dr.ys = steady_state;
return return
end end
if options_.steadystate_flag && updated_params_flag && ~isempty(find(isnan(M_.params))) if options.steadystate_flag && updated_params_flag && ~isempty(find(isnan(M.params)))
info(1) = 24; info(1) = 24;
info(2) = NaN; info(2) = NaN;
dr.ys = steady_state; dr.ys = steady_state;
@ -182,22 +229,17 @@ if options_.steadystate_flag && updated_params_flag && ~isempty(find(isnan(M_.p
end end
if options_.block if options.block
[dr,info,M_,options_,oo_] = dr_block(dr,check_flag,M_,options_,oo_); [dr,info,M,options,oo] = dr_block(dr,check_flag,M,options,oo);
else else
[dr,info,M_,options_,oo_] = dr1(dr,check_flag,M_,options_,oo_); [dr,info,M,options,oo] = dr1(dr,check_flag,M,options,oo);
end end
if info(1) if info(1)
return return
end end
if M_.exo_det_nbr > 0 if M.exo_det_nbr > 0
oo_.exo_det_simul = tempexdet; oo.exo_det_simul = tempexdet;
end end
oo_.exo_simul = tempex; oo.exo_simul = tempex;
tempex = []; tempex = [];
% 01/01/2003 MJ added dr_algo == 1
% 08/24/2001 MJ uses Schmitt-Grohe and Uribe (2001) constant correction
% in dr.ghs2
% 05/26/2003 MJ added temporary values for oo_.exo_simul

24
matlab/reversed_extended_path.m
View File

@ -1,17 +1,17 @@
function innovation_paths = reversed_extended_path(controlled_variable_names, control_innovation_names, dataset) function innovation_paths = reversed_extended_path(controlled_variable_names, control_innovation_names, dataset)
% Inversion of the extended path simulation approach. This routine computes the innovations needed to % Inversion of the extended path simulation approach. This routine computes the innovations needed to
% reproduce the time path of a subset of endogenous variables. The initial condition is teh deterministic % reproduce the time path of a subset of endogenous variables. The initial condition is teh deterministic
% steady state. % steady state.
% %
% INPUTS % INPUTS
% o controlled_variable_names [string] n*1 matlab's cell. % o controlled_variable_names [string] n*1 matlab's cell.
% o control_innovation_names [string] n*1 matlab's cell. % o control_innovation_names [string] n*1 matlab's cell.
% o dataset [structure] % o dataset [structure]
% OUTPUTS % OUTPUTS
% o innovations [double] n*T matrix. % o innovations [double] n*T matrix.
% %
% ALGORITHM % ALGORITHM
% %
% SPECIAL REQUIREMENTS % SPECIAL REQUIREMENTS
% Copyright (C) 2010 Dynare Team. % Copyright (C) 2010 Dynare Team.
@ -31,7 +31,7 @@ function innovation_paths = reversed_extended_path(controlled_variable_names, co
% 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_
%% Initialization %% Initialization
@ -48,14 +48,14 @@ steady_;
% Compute the first order perturbation reduced form. % Compute the first order perturbation reduced form.
old_options_order = options_.order; options_.order = 1; old_options_order = options_.order; options_.order = 1;
[oo_.dr,info] = resol(oo_.steady_state,0); [oo_.dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
options_.order = old_options_order; options_.order = old_options_order;
% Set various options. % Set various options.
options_.periods = 100; options_.periods = 100;
% Set-up oo_.exo_simul. % Set-up oo_.exo_simul.
make_ex_; make_ex_;
% Set-up oo_.endo_simul. % Set-up oo_.endo_simul.
make_y_; make_y_;
@ -98,13 +98,13 @@ for t=1:T
total_variation = y_target-transpose(oo_.endo_simul(t+M_.maximum_lag,iy)); total_variation = y_target-transpose(oo_.endo_simul(t+M_.maximum_lag,iy));
for i=1:100 for i=1:100
[t,i] [t,i]
y = transpose(oo_.endo_simul(t+M_.maximum_lag,iy)) + (i/100)*y_target y = transpose(oo_.endo_simul(t+M_.maximum_lag,iy)) + (i/100)*y_target
[tmp,fval,exitflag] = fsolve('ep_residuals', x0, options, y, ix, iy, oo_.steady_state, oo_.dr, M_.maximum_lag, M_.endo_nbr); [tmp,fval,exitflag] = fsolve('ep_residuals', x0, options, y, ix, iy, oo_.steady_state, oo_.dr, M_.maximum_lag, M_.endo_nbr);
end end
if exitflag==1 if exitflag==1
innovation_paths(:,t) = tmp; innovation_paths(:,t) = tmp;
end end
% Update endo_simul. % Update endo_simul.
oo_.endo_simul(:,1:end-1) = oo_.endo_simul(:,2:end); oo_.endo_simul(:,1:end-1) = oo_.endo_simul(:,2:end);
oo_.endo_simul(:,end) = oo_.steady_state; oo_.endo_simul(:,end) = oo_.steady_state;
end end

34
matlab/selec_posterior_draws.m
View File

@ -1,24 +1,24 @@
function SampleAddress = selec_posterior_draws(SampleSize,drsize) function SampleAddress = selec_posterior_draws(SampleSize,drsize)
% Selects a sample of draws from the posterior distribution and if nargin>1 % Selects a sample of draws from the posterior distribution and if nargin>1
% saves the draws in _pdraws mat files (metropolis folder). If drsize>0 % saves the draws in _pdraws mat files (metropolis folder). If drsize>0
% the dr structure, associated to the parameters, is also saved in _pdraws. % the dr structure, associated to the parameters, is also saved in _pdraws.
% This routine is more efficient than metropolis_draw.m because here an % This routine is more efficient than metropolis_draw.m because here an
% _mh file cannot be opened twice. % _mh file cannot be opened twice.
% %
% INPUTS % INPUTS
% o SampleSize [integer] Size of the sample to build. % o SampleSize [integer] Size of the sample to build.
% o drsize [double] structure dr is drsize megaoctets. % o drsize [double] structure dr is drsize megaoctets.
% %
% OUTPUTS % OUTPUTS
% o SampleAddress [integer] A (SampleSize*4) array, each line specifies the % o SampleAddress [integer] A (SampleSize*4) array, each line specifies the
% location of a posterior draw: % location of a posterior draw:
% Column 2 --> Chain number % Column 2 --> Chain number
% Column 3 --> (mh) File number % Column 3 --> (mh) File number
% Column 4 --> (mh) line number % Column 4 --> (mh) line number
% %
% SPECIAL REQUIREMENTS % SPECIAL REQUIREMENTS
% None. % None.
% %
% Copyright (C) 2006-2011 Dynare Team % Copyright (C) 2006-2011 Dynare Team
% %
@ -46,7 +46,7 @@ npar = npar + estim_params_.ncx;
npar = npar + estim_params_.ncn; npar = npar + estim_params_.ncn;
npar = npar + estim_params_.np; npar = npar + estim_params_.np;
% Select one task: % Select one task:
switch nargin switch nargin
case 1 case 1
info = 0; info = 0;
@ -62,14 +62,14 @@ switch nargin
error(['selec_posterior_draws:: Unexpected number of input arguments!']) error(['selec_posterior_draws:: Unexpected number of input arguments!'])
end end
% Get informations about the mcmc: % Get informations about the mcmc:
MhDirectoryName = CheckPath('metropolis'); MhDirectoryName = CheckPath('metropolis');
fname = [ MhDirectoryName '/' M_.fname]; fname = [ MhDirectoryName '/' M_.fname];
load([ fname '_mh_history.mat']); load([ 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)); TotalNumberOfMhFiles = sum(record.MhDraws(:,2));
LastMhFile = TotalNumberOfMhFiles; 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);
MAX_nruns = ceil(options_.MaxNumberOfBytes/(npar+2)/8); MAX_nruns = ceil(options_.MaxNumberOfBytes/(npar+2)/8);
@ -87,7 +87,7 @@ for i = 1:SampleSize
MhLineNumber = FirstLine+DrawNumber-1; MhLineNumber = FirstLine+DrawNumber-1;
else else
DrawNumber = DrawNumber-(MAX_nruns-FirstLine+1); DrawNumber = DrawNumber-(MAX_nruns-FirstLine+1);
MhFileNumber = FirstMhFile+ceil(DrawNumber/MAX_nruns); MhFileNumber = FirstMhFile+ceil(DrawNumber/MAX_nruns);
MhLineNumber = DrawNumber-(MhFileNumber-FirstMhFile-1)*MAX_nruns; MhLineNumber = DrawNumber-(MhFileNumber-FirstMhFile-1)*MAX_nruns;
end end
SampleAddress(i,3) = MhFileNumber; SampleAddress(i,3) = MhFileNumber;
@ -111,7 +111,7 @@ if info
pdraws(i,1) = {x2(SampleAddress(i,4),:)}; pdraws(i,1) = {x2(SampleAddress(i,4),:)};
if info-1 if info-1
set_parameters(pdraws{i,1}); set_parameters(pdraws{i,1});
[dr,info] = resol(oo_.steady_state,0); [dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
pdraws(i,2) = { dr }; pdraws(i,2) = { dr };
end end
old_mhfile = mhfile; old_mhfile = mhfile;
@ -121,7 +121,7 @@ if info
save([fname '_posterior_draws1.mat'],'pdraws') save([fname '_posterior_draws1.mat'],'pdraws')
else% The posterior draws are saved in xx files. else% The posterior draws are saved in xx files.
NumberOfDrawsPerFile = fix(MAX_mega_bytes/drawsize); NumberOfDrawsPerFile = fix(MAX_mega_bytes/drawsize);
NumberOfFiles = ceil(SampleSize*drawsize/MAX_mega_bytes); NumberOfFiles = ceil(SampleSize*drawsize/MAX_mega_bytes);
NumberOfLines = SampleSize - (NumberOfFiles-1)*NumberOfDrawsPerFile; NumberOfLines = SampleSize - (NumberOfFiles-1)*NumberOfDrawsPerFile;
linee = 0; linee = 0;
fnum = 1; fnum = 1;
@ -138,7 +138,7 @@ if info
pdraws(linee,1) = {x2(SampleAddress(i,4),:)}; pdraws(linee,1) = {x2(SampleAddress(i,4),:)};
if info-1 if info-1
set_parameters(pdraws{linee,1}); set_parameters(pdraws{linee,1});
[dr,info] = resol(oo_.steady_state,0); [dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
pdraws(linee,2) = { dr }; pdraws(linee,2) = { dr };
end end
old_mhfile = mhfile; old_mhfile = mhfile;

14
matlab/stoch_simul.m
View File

@ -69,7 +69,7 @@ elseif options_.discretionary_policy
end end
[oo_.dr,ys,info] = discretionary_policy_1(oo_,options_.instruments); [oo_.dr,ys,info] = discretionary_policy_1(oo_,options_.instruments);
else else
[oo_.dr, info] = resol(oo_.steady_state,0); [oo_.dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
end end
if info(1) if info(1)
@ -137,14 +137,14 @@ if options_.nomoments == 0
if PI_PCL_solver if PI_PCL_solver
PCL_Part_info_moments (0, PCL_varobs, oo_.dr, i_var); PCL_Part_info_moments (0, PCL_varobs, oo_.dr, i_var);
elseif options_.periods == 0 elseif options_.periods == 0
disp_th_moments(oo_.dr,var_list); disp_th_moments(oo_.dr,var_list);
else else
disp_moments(oo_.endo_simul,var_list); disp_moments(oo_.endo_simul,var_list);
end end
end end
if options_.irf if options_.irf
var_listTeX = M_.endo_names_tex(i_var,:); var_listTeX = M_.endo_names_tex(i_var,:);
if TeX if TeX
@ -169,7 +169,7 @@ if options_.irf
options_.replic, options_.order); options_.replic, options_.order);
end end
if options_.relative_irf if options_.relative_irf
y = 100*y/cs(i,i); y = 100*y/cs(i,i);
end end
irfs = []; irfs = [];
mylist = []; mylist = [];
@ -180,7 +180,7 @@ if options_.irf
assignin('base',[deblank(M_.endo_names(i_var(j),:)) '_' deblank(M_.exo_names(i,:))],... assignin('base',[deblank(M_.endo_names(i_var(j),:)) '_' deblank(M_.exo_names(i,:))],...
y(i_var(j),:)'); y(i_var(j),:)');
eval(['oo_.irfs.' deblank(M_.endo_names(i_var(j),:)) '_' ... eval(['oo_.irfs.' deblank(M_.endo_names(i_var(j),:)) '_' ...
deblank(M_.exo_names(i,:)) ' = y(i_var(j),:);']); deblank(M_.exo_names(i,:)) ' = y(i_var(j),:);']);
if max(y(i_var(j),:)) - min(y(i_var(j),:)) > 1e-10 if max(y(i_var(j),:)) - min(y(i_var(j),:)) > 1e-10
irfs = cat(1,irfs,y(i_var(j),:)); irfs = cat(1,irfs,y(i_var(j),:));
if isempty(mylist) if isempty(mylist)
@ -280,7 +280,7 @@ if options_.irf
% close(hh); % close(hh);
end end
hh = figure('Name',['Orthogonalized shock to ' tit(i,:) ' figure ' int2str(nbplt) '.']); hh = figure('Name',['Orthogonalized shock to ' tit(i,:) ' figure ' int2str(nbplt) '.']);
m = 0; m = 0;
for plt = 1:number_of_plots_to_draw-(nbplt-1)*nstar; for plt = 1:number_of_plots_to_draw-(nbplt-1)*nstar;
m = m+1; m = m+1;
subplot(lr,lc,m); subplot(lr,lc,m);
@ -333,4 +333,4 @@ end
options_ = options_old; options_ = options_old;
% temporary fix waiting for local options % temporary fix waiting for local options
options_.partial_information = 0; options_.partial_information = 0;

14
matlab/stoch_simul_sparse.m
View File

@ -38,13 +38,13 @@ end
check_model; check_model;
[oo_.dr, info] = resol(oo_.steady_state,0); [oo_.dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
if info(1) if info(1)
options_ = options_old; options_ = options_old;
print_info(info, options_.noprint); print_info(info, options_.noprint);
return return
end end
oo_dr_kstate = []; oo_dr_kstate = [];
oo_dr_nstatic = 0; oo_dr_nstatic = 0;
@ -74,7 +74,7 @@ if ~options_.noprint
end end
if options_.periods == 0 && options_.nomoments == 0 if options_.periods == 0 && options_.nomoments == 0
disp_th_moments(oo_.dr,var_list); disp_th_moments(oo_.dr,var_list);
elseif options_.periods ~= 0 elseif options_.periods ~= 0
if options_.periods < options_.drop if options_.periods < options_.drop
disp(['STOCH_SIMUL error: The horizon of simulation is shorter' ... disp(['STOCH_SIMUL error: The horizon of simulation is shorter' ...
@ -91,7 +91,7 @@ end
if options_.irf if options_.irf
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, :);
if TeX if TeX
@ -138,7 +138,7 @@ if options_.irf
y=irf(oo_.dr,cs(M_.exo_names_orig_ord,i), options_.irf, options_.drop, ... y=irf(oo_.dr,cs(M_.exo_names_orig_ord,i), options_.irf, options_.drop, ...
options_.replic, options_.order); options_.replic, options_.order);
if options_.relative_irf if options_.relative_irf
y = 100*y/cs(i,i); y = 100*y/cs(i,i);
end end
irfs = []; irfs = [];
mylist = []; mylist = [];
@ -149,7 +149,7 @@ if options_.irf
assignin('base',[deblank(M_.endo_names(ivar(j),:)) '_' deblank(M_.exo_names(i,:))],... assignin('base',[deblank(M_.endo_names(ivar(j),:)) '_' deblank(M_.exo_names(i,:))],...
y(ivar(j),:)'); y(ivar(j),:)');
eval(['oo_.irfs.' deblank(M_.endo_names(ivar(j),:)) '_' ... eval(['oo_.irfs.' deblank(M_.endo_names(ivar(j),:)) '_' ...
deblank(M_.exo_names(i,:)) ' = y(ivar(j),:);']); deblank(M_.exo_names(i,:)) ' = y(ivar(j),:);']);
if max(y(ivar(j),:)) - min(y(ivar(j),:)) > 1e-10 if max(y(ivar(j),:)) - min(y(ivar(j),:)) > 1e-10
irfs = cat(1,irfs,y(ivar(j),:)); irfs = cat(1,irfs,y(ivar(j),:));
if isempty(mylist) if isempty(mylist)
@ -249,7 +249,7 @@ if options_.irf
% close(hh); % close(hh);
end end
hh = figure('Name',['Orthogonalized shock to ' tit(i,:) ' figure ' int2str(nbplt) '.']); hh = figure('Name',['Orthogonalized shock to ' tit(i,:) ' figure ' int2str(nbplt) '.']);
m = 0; m = 0;
for plt = 1:number_of_plots_to_draw-(nbplt-1)*nstar; for plt = 1:number_of_plots_to_draw-(nbplt-1)*nstar;
m = m+1; m = m+1;
subplot(lr,lc,m); subplot(lr,lc,m);