Posterior moments: fix bug that prevented updating decision rules for parameter vector, leading to wrong results/crashes when computing second moments
When removing globals in 24cd423671
the call to set_parameters.m, which relies on M_ being global, was not removed. The problem arises
1. When computing second moments for big models with drsize*SampleSize>MaxMegaBytes (in which case decision rules dr were not saved, but recomputed)
2. When computing the conditional variance decomposition for all models regardless of size (dsge_simulated_theoretical_conditional_variance_decomposition.m relied on the wrong M_.Sigma_e in this case)
time-shift
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acace4899d
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0b9244dc01
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@ -109,10 +109,10 @@ for file = 1:NumberOfDrawsFiles
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for linee = 1:NumberOfDraws
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linea = linea+1;
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if isdrsaved
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set_parameters(pdraws{linee,1});% Needed to update the covariance matrix of the state innovations.
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M_=set_parameters_locally(M_,pdraws{linee,1});% Needed to update the covariance matrix of the state innovations.
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dr = pdraws{linee,2};
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else
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set_parameters(pdraws{linee,1});
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M_=set_parameters_locally(M_,pdraws{linee,1});
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[dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
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end
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if first_call
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@ -106,7 +106,7 @@ for file = 1:NumberOfDrawsFiles
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if isdrsaved
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dr = pdraws{linee,2};
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else
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set_parameters(pdraws{linee,1});
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M_=set_parameters_locally(M_,pdraws{linee,1});
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[dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
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end
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tmp = th_autocovariances(dr,ivar,M_,options_,nodecomposition);
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@ -105,7 +105,7 @@ for file = 1:NumberOfDrawsFiles
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if isdrsaved
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dr = pdraws{linee,2};
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else
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set_parameters(pdraws{linee,1});
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M_=set_parameters_locally(M_,pdraws{linee,1});
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[dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
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end
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tmp = th_autocovariances(dr,ivar,M_,options_,nodecomposition);
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@ -113,7 +113,7 @@ for file = 1:NumberOfDrawsFiles
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if isdrsaved
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dr = pdraws{linee,2};
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else
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set_parameters(pdraws{linee,1});
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M_=set_parameters_locally(M_,pdraws{linee,1});
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[dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
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end
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if file==1 && linee==1
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@ -0,0 +1,85 @@
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function M_=set_parameters_locally(M_,xparam1)
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% function M_out=set_parameters(M_,xparam1)
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% Sets parameters value (except measurement errors)
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% This is called for computations such as IRF and forecast
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% when measurement errors aren't taken into account; in contrast to
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% set_parameters.m, the global M_-structure is not altered
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%
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% INPUTS
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% xparam1: vector of parameters to be estimated (initial values)
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% M_: Dynare model-structure
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%
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% OUTPUTS
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% M_: Dynare model-structure
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%
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% SPECIAL REQUIREMENTS
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% none
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% Copyright (C) 2017 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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global estim_params_
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nvx = estim_params_.nvx;
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ncx = estim_params_.ncx;
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nvn = estim_params_.nvn;
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ncn = estim_params_.ncn;
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np = estim_params_.np;
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Sigma_e = M_.Sigma_e;
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Correlation_matrix = M_.Correlation_matrix;
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offset = 0;
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% setting shocks variance on the diagonal of Covariance matrix; used later
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% for updating covariances
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if nvx
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var_exo = estim_params_.var_exo;
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for i=1:nvx
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k = var_exo(i,1);
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Sigma_e(k,k) = xparam1(i)^2;
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end
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end
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% and update offset
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offset = offset + nvx + nvn;
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% correlations amonx shocks (ncx)
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if ncx
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corrx = estim_params_.corrx;
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for i=1:ncx
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k1 = corrx(i,1);
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k2 = corrx(i,2);
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Correlation_matrix(k1,k2) = xparam1(i+offset);
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Correlation_matrix(k2,k1) = Correlation_matrix(k1,k2);
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end
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end
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%build covariance matrix from correlation matrix and variances already on
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%diagonal
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Sigma_e = diag(sqrt(diag(Sigma_e)))*Correlation_matrix*diag(sqrt(diag(Sigma_e)));
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if isfield(estim_params_,'calibrated_covariances')
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Sigma_e(estim_params_.calibrated_covariances.position)=estim_params_.calibrated_covariances.cov_value;
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end
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% and update offset
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offset = offset + ncx + ncn;
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% structural parameters
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if np
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M_.params(estim_params_.param_vals(:,1)) = xparam1(offset+1:end);
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end
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M_.Sigma_e = Sigma_e;
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M_.Correlation_matrix=Correlation_matrix;
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