Prototype DR1 subset for running k_order_perturbation
git-svn-id: https://www.dynare.org/svn/dynare/trunk@2387 ac1d8469-bf42-47a9-8791-bf33cf982152time-shift
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function [dr,info,M_,options_,oo_] = dr1(dr,task,M_,options_,oo_)
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% Computes the reduced form solution of a rational expectation model (first or second order
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% approximation of the stochastic model around the deterministic steady state).
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%
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% INPUTS
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% dr [matlab structure] Decision rules for stochastic simulations.
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% task [integer] if task = 0 then dr1 computes decision rules.
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% if task = 1 then dr1 computes eigenvalues.
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% M_ [matlab structure] Definition of the model.
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% options_ [matlab structure] Global options.
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% oo_ [matlab structure] Results
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%
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% OUTPUTS
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% dr [matlab structure] Decision rules for stochastic simulations.
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% info [integer] info=1: the model doesn't define current variables uniquely
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% info=2: problem in mjdgges.dll info(2) contains error code.
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% info=3: BK order condition not satisfied info(2) contains "distance"
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% absence of stable trajectory.
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% info=4: BK order condition not satisfied info(2) contains "distance"
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% indeterminacy.
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% info=5: BK rank condition not satisfied.
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% M_ [matlab structure]
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% options_ [matlab structure]
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% oo_ [matlab structure]
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%
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% ALGORITHM
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% ...
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%
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% SPECIAL REQUIREMENTS
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% none.
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%
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% Copyright (C) 1996-2008 Dynare Team
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%
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% This file is part of Dynare.
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%
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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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% 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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%
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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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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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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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info = 0;
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options_ = set_default_option(options_,'loglinear',0);
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options_ = set_default_option(options_,'noprint',0);
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options_ = set_default_option(options_,'olr',0);
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options_ = set_default_option(options_,'olr_beta',1);
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options_ = set_default_option(options_,'qz_criterium',1.000001);
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xlen = M_.maximum_endo_lead + M_.maximum_endo_lag + 1;
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klen = M_.maximum_endo_lag + M_.maximum_endo_lead + 1;
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iyv = M_.lead_lag_incidence';
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iyv = iyv(:);
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iyr0 = find(iyv) ;
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it_ = M_.maximum_lag + 1 ;
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if M_.exo_nbr == 0
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oo_.exo_steady_state = [] ;
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end
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% expanding system for Optimal Linear Regulator
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if options_.ramsey_policy
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if isfield(M_,'orig_model')
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orig_model = M_.orig_model;
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M_.endo_nbr = orig_model.endo_nbr;
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M_.endo_names = orig_model.endo_names;
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M_.lead_lag_incidence = orig_model.lead_lag_incidence;
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M_.maximum_lead = orig_model.maximum_lead;
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M_.maximum_endo_lead = orig_model.maximum_endo_lead;
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M_.maximum_lag = orig_model.maximum_lag;
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M_.maximum_endo_lag = orig_model.maximum_endo_lag;
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end
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old_solve_algo = options_.solve_algo;
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% options_.solve_algo = 1;
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oo_.steady_state = dynare_solve('ramsey_static',oo_.steady_state,0,M_,options_,oo_,it_);
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options_.solve_algo = old_solve_algo;
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[junk,junk,multbar] = ramsey_static(oo_.steady_state,M_,options_,oo_,it_);
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[jacobia_,M_] = ramsey_dynamic(oo_.steady_state,multbar,M_,options_,oo_,it_);
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klen = M_.maximum_lag + M_.maximum_lead + 1;
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dr.ys = [oo_.steady_state;zeros(M_.exo_nbr,1);multbar];
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else
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klen = M_.maximum_lag + M_.maximum_lead + 1;
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iyv = M_.lead_lag_incidence';
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iyv = iyv(:);
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iyr0 = find(iyv) ;
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it_ = M_.maximum_lag + 1 ;
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if M_.exo_nbr == 0
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oo_.exo_steady_state = [] ;
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end
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it_ = M_.maximum_lag + 1;
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z = repmat(dr.ys,1,klen);
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z = z(iyr0) ;
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end
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if options_.debug
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save([M_.fname '_debug.mat'],'jacobia_')
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end
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dr=set_state_space(dr,M_);
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kstate = dr.kstate;
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kad = dr.kad;
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kae = dr.kae;
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nstatic = dr.nstatic;
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nfwrd = dr.nfwrd;
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npred = dr.npred;
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nboth = dr.nboth;
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order_var = dr.order_var;
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nd = size(kstate,1);
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nz = nnz(M_.lead_lag_incidence);
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sdyn = M_.endo_nbr - nstatic;
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k0 = M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var);
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k1 = M_.lead_lag_incidence(find([1:klen] ~= M_.maximum_endo_lag+1),:);
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if options_.order == 1
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M_.var_order_endo_names=M_.endo_names(dr.order_var,:);
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% z = repmat(dr.ys,1,klen);
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% z = z(iyr0) ;
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% oo_.dyn_ys=z; % extended ys
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try
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[ysteady, gx, gu]=k_order_perturbation(dr,task,M_,options_, oo_ );
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load(M_.fname);
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ghxu = eval([M_.fname '_g_1']);
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sss= size(ghxu,2);
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dr.ghx= ghxu(:,1:sss-M_.exo_nbr);
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dr.ghu= ghxu(:,sss-M_.exo_nbr+1:end);
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dr.ys=eval([M_.fname '_ss']);
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catch
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disp('*************************************************************************************');
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% disp('Problem with using k_order perturbation solver - Using Dynare solver instead');
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% warning('Problem with using k_order perturbation solver - Using Dynare solver instead');
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error('Problem with using k_order perturbation solver ');
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disp('*****************************************************************************');
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options_.use_k_order=0; % and then try mjdgges instead
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info(1) = 4;
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info(2) = 1000;
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return
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end
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elseif options_.order > 1
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error(' can not use order > 1 with K-Order yet!')
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% or ???
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disp('********************************************************************');
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disp(' can not use order > 1 with K-Order yet - Using Dynare solver instead');
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disp('********************************************************************');
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options_.use_k_order= 0; % and then try mjdgges instead
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info(1) = 4;
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info(2) = 1000;
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return
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end
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if M_.maximum_endo_lead == 0; % backward models
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% If required, try Gary Anderson and G Moore AIM solver if not
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% check only and if 1st order (added by GP July'08)
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dr.eigval = eig(transition_matrix(dr));
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dr.rank = 0;
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if any(abs(dr.eigval) > options_.qz_criterium)
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temp = sort(abs(dr.eigval));
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nba = nnz(abs(dr.eigval) > options_.qz_criterium);
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temp = temp(nd-nba+1:nd)-1-options_.qz_criterium;
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info(1) = 3;
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info(2) = temp'*temp;
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end
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return;
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end
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%forward--looking models
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[A,B] =transition_matrix(dr);
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dr.eigval = eig(A);
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% if any(abs(dr.eigval) > options_.qz_criterium)
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% temp = sort(abs(dr.eigval));
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% nba = nnz(abs(dr.eigval) > options_.qz_criterium);
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% temp = temp(nd-nba+1:nd)-1-options_.qz_criterium;
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% info(1) = 3;
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% info(2) = temp'*temp;
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% return
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% end
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sdim = sum( abs(dr.eigval) < options_.qz_criterium );
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nba = nd-sdim;
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nyf = sum(kstate(:,2) > M_.maximum_endo_lag+1);
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if nba ~= nyf
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temp = sort(abs(dr.eigval));
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if nba > nyf
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temp = temp(nd-nba+1:nd-nyf)-1-options_.qz_criterium;
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info(1) = 3;
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elseif nba < nyf;
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temp = temp(nd-nyf+1:nd-nba)-1-options_.qz_criterium;
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info(1) = 4;
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end
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info(2) = temp'*temp;
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return
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end
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if options_.loglinear == 1
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k = find(dr.kstate(:,2) <= M_.maximum_endo_lag+1);
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klag = dr.kstate(k,[1 2]);
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k1 = dr.order_var;
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dr.ghx = repmat(1./dr.ys(k1),1,size(dr.ghx,2)).*dr.ghx.* ...
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repmat(dr.ys(k1(klag(:,1)))',size(dr.ghx,1),1);
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dr.ghu = repmat(1./dr.ys(k1),1,size(dr.ghu,2)).*dr.ghu;
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end
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dr.ghx = real(dr.ghx);
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dr.ghu = real(dr.ghu);
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return
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%exogenous deterministic variables
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if M_.exo_det_nbr > 0
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f1 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+2:end,order_var))));
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f0 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var))));
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fudet = sparse(jacobia_(:,nz+M_.exo_nbr+1:end));
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M1 = inv(f0+[zeros(M_.endo_nbr,nstatic) f1*gx zeros(M_.endo_nbr,nyf-nboth)]);
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M2 = M1*f1;
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dr.ghud = cell(M_.exo_det_length,1);
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dr.ghud{1} = -M1*fudet;
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for i = 2:M_.exo_det_length
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dr.ghud{i} = -M2*dr.ghud{i-1}(end-nyf+1:end,:);
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end
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end
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if options_.order == 1
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return
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end
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% Second order
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%tempex = oo_.exo_simul ;
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[junk,jacobia_,hessian] = feval([M_.fname '_dynamic'],z,...
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[oo_.exo_simul ...
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oo_.exo_det_simul], M_.params, it_);
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%hessian = real(hessext('ff1_',[z; oo_.exo_steady_state]))' ;
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kk = flipud(cumsum(flipud(M_.lead_lag_incidence(M_.maximum_endo_lag+1:end,order_var)),1));
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if M_.maximum_endo_lag > 0
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kk = [cumsum(M_.lead_lag_incidence(1:M_.maximum_endo_lag,order_var),1); kk];
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end
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kk = kk';
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kk = find(kk(:));
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nk = size(kk,1) + M_.exo_nbr + M_.exo_det_nbr;
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k1 = M_.lead_lag_incidence(:,order_var);
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k1 = k1';
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k1 = k1(:);
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k1 = k1(kk);
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k2 = find(k1);
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kk1(k1(k2)) = k2;
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kk1 = [kk1 length(k1)+1:length(k1)+M_.exo_nbr+M_.exo_det_nbr];
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kk = reshape([1:nk^2],nk,nk);
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kk1 = kk(kk1,kk1);
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%[junk,junk,hessian] = feval([M_.fname '_dynamic'],z, oo_.exo_steady_state);
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hessian(:,kk1(:)) = hessian;
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%oo_.exo_simul = tempex ;
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%clear tempex
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n1 = 0;
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n2 = np;
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zx = zeros(np,np);
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zu=zeros(np,M_.exo_nbr);
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for i=2:M_.maximum_endo_lag+1
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k1 = sum(kstate(:,2) == i);
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zx(n1+1:n1+k1,n2-k1+1:n2)=eye(k1);
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n1 = n1+k1;
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n2 = n2-k1;
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end
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kk = flipud(cumsum(flipud(M_.lead_lag_incidence(M_.maximum_endo_lag+1:end,order_var)),1));
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k0 = [1:M_.endo_nbr];
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gx1 = dr.ghx;
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hu = dr.ghu(nstatic+[1:npred],:);
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zx = [zx; gx1];
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zu = [zu; dr.ghu];
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for i=1:M_.maximum_endo_lead
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k1 = find(kk(i+1,k0) > 0);
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zu = [zu; gx1(k1,1:npred)*hu];
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gx1 = gx1(k1,:)*hx;
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zx = [zx; gx1];
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kk = kk(:,k0);
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k0 = k1;
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end
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zx=[zx; zeros(M_.exo_nbr,np);zeros(M_.exo_det_nbr,np)];
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zu=[zu; eye(M_.exo_nbr);zeros(M_.exo_det_nbr,M_.exo_nbr)];
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[nrzx,nczx] = size(zx);
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rhs = -sparse_hessian_times_B_kronecker_C(hessian,zx);
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%lhs
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n = M_.endo_nbr+sum(kstate(:,2) > M_.maximum_endo_lag+1 & kstate(:,2) < M_.maximum_endo_lag+M_.maximum_endo_lead+1);
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A = zeros(n,n);
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B = zeros(n,n);
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A(1:M_.endo_nbr,1:M_.endo_nbr) = jacobia_(:,M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var));
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% variables with the highest lead
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k1 = find(kstate(:,2) == M_.maximum_endo_lag+M_.maximum_endo_lead+1);
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if M_.maximum_endo_lead > 1
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k2 = find(kstate(:,2) == M_.maximum_endo_lag+M_.maximum_endo_lead);
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[junk,junk,k3] = intersect(kstate(k1,1),kstate(k2,1));
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else
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k2 = [1:M_.endo_nbr];
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k3 = kstate(k1,1);
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end
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% Jacobian with respect to the variables with the highest lead
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B(1:M_.endo_nbr,end-length(k2)+k3) = jacobia_(:,kstate(k1,3)+M_.endo_nbr);
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offset = M_.endo_nbr;
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k0 = [1:M_.endo_nbr];
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gx1 = dr.ghx;
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for i=1:M_.maximum_endo_lead-1
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k1 = find(kstate(:,2) == M_.maximum_endo_lag+i+1);
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[k2,junk,k3] = find(kstate(k1,3));
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A(1:M_.endo_nbr,offset+k2) = jacobia_(:,k3+M_.endo_nbr);
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n1 = length(k1);
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A(offset+[1:n1],nstatic+[1:npred]) = -gx1(kstate(k1,1),1:npred);
|
||||||
|
gx1 = gx1*hx;
|
||||||
|
A(offset+[1:n1],offset+[1:n1]) = eye(n1);
|
||||||
|
n0 = length(k0);
|
||||||
|
E = eye(n0);
|
||||||
|
if i == 1
|
||||||
|
[junk,junk,k4]=intersect(kstate(k1,1),[1:M_.endo_nbr]);
|
||||||
|
else
|
||||||
|
[junk,junk,k4]=intersect(kstate(k1,1),kstate(k0,1));
|
||||||
|
end
|
||||||
|
i1 = offset-n0+n1;
|
||||||
|
B(offset+[1:n1],offset-n0+[1:n0]) = -E(k4,:);
|
||||||
|
k0 = k1;
|
||||||
|
offset = offset + n1;
|
||||||
|
end
|
||||||
|
[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:npred])+jacobia_(:,k2)*gx1(k1,1:npred);
|
||||||
|
C = hx;
|
||||||
|
D = [rhs; zeros(n-M_.endo_nbr,size(rhs,2))];
|
||||||
|
|
||||||
|
|
||||||
|
dr.ghxx = gensylv(2,A,B,C,D);
|
||||||
|
|
||||||
|
%ghxu
|
||||||
|
%rhs
|
||||||
|
hu = dr.ghu(nstatic+1:nstatic+npred,:);
|
||||||
|
%kk = reshape([1:np*np],np,np);
|
||||||
|
%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 = sparse_hessian_times_B_kronecker_C(hessian,zx,zu);
|
||||||
|
|
||||||
|
nyf1 = sum(kstate(:,2) == M_.maximum_endo_lag+2);
|
||||||
|
hu1 = [hu;zeros(np-npred,M_.exo_nbr)];
|
||||||
|
%B1 = [B(1:M_.endo_nbr,:);zeros(size(A,1)-M_.endo_nbr,size(B,2))];
|
||||||
|
[nrhx,nchx] = size(hx);
|
||||||
|
[nrhu1,nchu1] = size(hu1);
|
||||||
|
|
||||||
|
B1 = B*A_times_B_kronecker_C(dr.ghxx,hx,hu1);
|
||||||
|
rhs = -[rhs; zeros(n-M_.endo_nbr,size(rhs,2))]-B1;
|
||||||
|
|
||||||
|
|
||||||
|
%lhs
|
||||||
|
dr.ghxu = A\rhs;
|
||||||
|
|
||||||
|
%ghuu
|
||||||
|
%rhs
|
||||||
|
kk = reshape([1:np*np],np,np);
|
||||||
|
kk = kk(1:npred,1:npred);
|
||||||
|
|
||||||
|
rhs = sparse_hessian_times_B_kronecker_C(hessian,zu);
|
||||||
|
|
||||||
|
|
||||||
|
B1 = A_times_B_kronecker_C(B*dr.ghxx,hu1);
|
||||||
|
rhs = -[rhs; zeros(n-M_.endo_nbr,size(rhs,2))]-B1;
|
||||||
|
|
||||||
|
%lhs
|
||||||
|
dr.ghuu = A\rhs;
|
||||||
|
|
||||||
|
dr.ghxx = dr.ghxx(1:M_.endo_nbr,:);
|
||||||
|
dr.ghxu = dr.ghxu(1:M_.endo_nbr,:);
|
||||||
|
dr.ghuu = dr.ghuu(1:M_.endo_nbr,:);
|
||||||
|
|
||||||
|
|
||||||
|
% dr.ghs2
|
||||||
|
% derivatives of F with respect to forward variables
|
||||||
|
% reordering predetermined variables in diminishing lag order
|
||||||
|
O1 = zeros(M_.endo_nbr,nstatic);
|
||||||
|
O2 = zeros(M_.endo_nbr,M_.endo_nbr-nstatic-npred);
|
||||||
|
LHS = jacobia_(:,M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var));
|
||||||
|
RHS = zeros(M_.endo_nbr,M_.exo_nbr^2);
|
||||||
|
kk = find(kstate(:,2) == M_.maximum_endo_lag+2);
|
||||||
|
gu = dr.ghu;
|
||||||
|
guu = dr.ghuu;
|
||||||
|
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)];
|
||||||
|
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));
|
||||||
|
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];
|
||||||
|
end
|
||||||
|
M_.lead_lag_incidenceordered = M_.lead_lag_incidenceordered';
|
||||||
|
M_.lead_lag_incidenceordered = M_.lead_lag_incidenceordered(:);
|
||||||
|
k1 = find(M_.lead_lag_incidenceordered);
|
||||||
|
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)';
|
||||||
|
kh = reshape([1:nk^2],nk,nk);
|
||||||
|
kp = sum(kstate(:,2) <= M_.maximum_endo_lag+1);
|
||||||
|
E1 = [eye(npred); zeros(kp-npred,npred)];
|
||||||
|
H = E1;
|
||||||
|
hxx = dr.ghxx(nstatic+[1:npred],:);
|
||||||
|
for i=1: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,k3a,k3] = ...
|
||||||
|
find(M_.lead_lag_incidenceordered(M_.maximum_endo_lag+j+1,:));
|
||||||
|
nk3a = length(k3a);
|
||||||
|
B1 = sparse_hessian_times_B_kronecker_C(hessian(:,kh(k3,k3)),gu(k3a,:));
|
||||||
|
RHS = RHS + jacobia_(:,k2)*guu(k2a,:)+B1;
|
||||||
|
end
|
||||||
|
% LHS
|
||||||
|
[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,:)]);
|
||||||
|
|
||||||
|
if i == M_.maximum_endo_lead
|
||||||
|
break
|
||||||
|
end
|
||||||
|
|
||||||
|
kk = find(kstate(:,2) == M_.maximum_endo_lag+i+1);
|
||||||
|
gu = dr.ghx*Gu;
|
||||||
|
[nrGu,ncGu] = size(Gu);
|
||||||
|
G1 = A_times_B_kronecker_C(dr.ghxx,Gu);
|
||||||
|
G2 = A_times_B_kronecker_C(hxx,Gu);
|
||||||
|
guu = dr.ghx*Guu+G1;
|
||||||
|
Gu = hx*Gu;
|
||||||
|
Guu = hx*Guu;
|
||||||
|
Guu(end-npred+1:end,:) = Guu(end-npred+1:end,:) + G2;
|
||||||
|
H = E1 + hx*H;
|
||||||
|
end
|
||||||
|
RHS = RHS*M_.Sigma_e(:);
|
||||||
|
dr.fuu = RHS;
|
||||||
|
%RHS = -RHS-dr.fbias;
|
||||||
|
RHS = -RHS;
|
||||||
|
dr.ghs2 = LHS\RHS;
|
||||||
|
|
||||||
|
% deterministic exogenous variables
|
||||||
|
if M_.exo_det_nbr > 0
|
||||||
|
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)];
|
||||||
|
R1 = hessian*kron(zx,zud);
|
||||||
|
dr.ghxud = cell(M_.exo_det_length,1);
|
||||||
|
kf = [M_.endo_nbr-nyf+1:M_.endo_nbr];
|
||||||
|
kp = nstatic+[1:npred];
|
||||||
|
dr.ghxud{1} = -M1*(R1+f1*dr.ghxx(kf,:)*kron(dr.ghx(kp,:),dr.ghud{1}(kp,:)));
|
||||||
|
Eud = eye(M_.exo_det_nbr);
|
||||||
|
for i = 2:M_.exo_det_length
|
||||||
|
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)];
|
||||||
|
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;
|
||||||
|
end
|
||||||
|
R1 = hessian*kron(zu,zud);
|
||||||
|
dr.ghudud = cell(M_.exo_det_length,1);
|
||||||
|
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,:)));
|
||||||
|
Eud = eye(M_.exo_det_nbr);
|
||||||
|
for i = 2:M_.exo_det_length
|
||||||
|
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)];
|
||||||
|
R2 = hessian*kron(zu,zudi);
|
||||||
|
dr.ghuud{i} = -M2*dr.ghxud{i-1}(kf,:)*kron(hu,Eud)-M1*R2;
|
||||||
|
end
|
||||||
|
R1 = hessian*kron(zud,zud);
|
||||||
|
dr.ghudud = cell(M_.exo_det_length,M_.exo_det_length);
|
||||||
|
dr.ghudud{1,1} = -M1*R1-M2*dr.ghxx(kf,:)*kron(hud,hud);
|
||||||
|
for i = 2:M_.exo_det_length
|
||||||
|
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)];
|
||||||
|
R2 = hessian*kron(zudi,zudi);
|
||||||
|
dr.ghudud{i,i} = -M2*(dr.ghudud{i-1,i-1}(kf,:)+...
|
||||||
|
2*dr.ghxud{i-1}(kf,:)*kron(hudi,Eud) ...
|
||||||
|
+dr.ghxx(kf,:)*kron(hudi,hudi))-M1*R2;
|
||||||
|
R2 = hessian*kron(zud,zudi);
|
||||||
|
dr.ghudud{1,i} = -M2*(dr.ghxud{i-1}(kf,:)*kron(hud,Eud)+...
|
||||||
|
dr.ghxx(kf,:)*kron(hud,hudi))...
|
||||||
|
-M1*R2;
|
||||||
|
for j=2:i-1
|
||||||
|
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)];
|
||||||
|
R2 = hessian*kron(zudj,zudi);
|
||||||
|
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(hudj,Eud)+dr.ghxx(kf,:)*kron(hudj,hudi))-M1*R2;
|
||||||
|
end
|
||||||
|
|
||||||
|
end
|
||||||
|
end
|
Loading…
Reference in New Issue