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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
Johannes Pfeifer 2017-07-05 18:03:14 +02:00 committed by Stéphane Adjemian (Scylla)
parent acace4899d
commit 0b9244dc01
5 changed files with 90 additions and 5 deletions
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4
matlab/dsge_simulated_theoretical_conditional_variance_decomposition.m
View File

@ -109,10 +109,10 @@ for file = 1:NumberOfDrawsFiles
for linee = 1:NumberOfDraws
linea = linea+1;
if isdrsaved
set_parameters(pdraws{linee,1});% Needed to update the covariance matrix of the state innovations.
M_=set_parameters_locally(M_,pdraws{linee,1});% Needed to update the covariance matrix of the state innovations.
dr = pdraws{linee,2};
else
set_parameters(pdraws{linee,1});
M_=set_parameters_locally(M_,pdraws{linee,1});
[dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
end
if first_call

2
matlab/dsge_simulated_theoretical_correlation.m
View File

@ -106,7 +106,7 @@ for file = 1:NumberOfDrawsFiles
if isdrsaved
dr = pdraws{linee,2};
else
set_parameters(pdraws{linee,1});
M_=set_parameters_locally(M_,pdraws{linee,1});
[dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
end
tmp = th_autocovariances(dr,ivar,M_,options_,nodecomposition);

2
matlab/dsge_simulated_theoretical_covariance.m
View File

@ -105,7 +105,7 @@ for file = 1:NumberOfDrawsFiles
if isdrsaved
dr = pdraws{linee,2};
else
set_parameters(pdraws{linee,1});
M_=set_parameters_locally(M_,pdraws{linee,1});
[dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
end
tmp = th_autocovariances(dr,ivar,M_,options_,nodecomposition);

2
matlab/dsge_simulated_theoretical_variance_decomposition.m
View File

@ -113,7 +113,7 @@ for file = 1:NumberOfDrawsFiles
if isdrsaved
dr = pdraws{linee,2};
else
set_parameters(pdraws{linee,1});
M_=set_parameters_locally(M_,pdraws{linee,1});
[dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
end
if file==1 && linee==1

85
matlab/set_parameters_locally.m Normal file
View File

@ -0,0 +1,85 @@
function M_=set_parameters_locally(M_,xparam1)
% function M_out=set_parameters(M_,xparam1)
% Sets parameters value (except measurement errors)
% This is called for computations such as IRF and forecast
% when measurement errors aren't taken into account; in contrast to
% set_parameters.m, the global M_-structure is not altered
%
% INPUTS
% xparam1: vector of parameters to be estimated (initial values)
% M_: Dynare model-structure
%
% OUTPUTS
% M_: Dynare model-structure
%
% SPECIAL REQUIREMENTS
% none
% Copyright (C) 2017 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
global estim_params_
nvx = estim_params_.nvx;
ncx = estim_params_.ncx;
nvn = estim_params_.nvn;
ncn = estim_params_.ncn;
np = estim_params_.np;
Sigma_e = M_.Sigma_e;
Correlation_matrix = M_.Correlation_matrix;
offset = 0;
% setting shocks variance on the diagonal of Covariance matrix; used later
% for updating covariances
if nvx
var_exo = estim_params_.var_exo;
for i=1:nvx
k = var_exo(i,1);
Sigma_e(k,k) = xparam1(i)^2;
end
end
% and update offset
offset = offset + nvx + nvn;
% correlations amonx shocks (ncx)
if ncx
corrx = estim_params_.corrx;
for i=1:ncx
k1 = corrx(i,1);
k2 = corrx(i,2);
Correlation_matrix(k1,k2) = xparam1(i+offset);
Correlation_matrix(k2,k1) = Correlation_matrix(k1,k2);
end
end
%build covariance matrix from correlation matrix and variances already on
%diagonal
Sigma_e = diag(sqrt(diag(Sigma_e)))*Correlation_matrix*diag(sqrt(diag(Sigma_e)));
if isfield(estim_params_,'calibrated_covariances')
Sigma_e(estim_params_.calibrated_covariances.position)=estim_params_.calibrated_covariances.cov_value;
end
% and update offset
offset = offset + ncx + ncn;
% structural parameters
if np
M_.params(estim_params_.param_vals(:,1)) = xparam1(offset+1:end);
end
M_.Sigma_e = Sigma_e;
M_.Correlation_matrix=Correlation_matrix;