+ Changed some files so that they can handle prior montecarlo.
+ New version of prior_analysis.m. + Changed names and calls. + Bug fix. + Cosmetic changes. git-svn-id: https://www.dynare.org/svn/dynare/trunk@2765 ac1d8469-bf42-47a9-8791-bf33cf982152time-shift
stepan
parent
bdabde9b58
commit
5695c1d528
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@ -0,0 +1,96 @@
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function oo_ = conditional_variance_decomposition_mc_analysis(NumberOfSimulations, typr, dname, fname, Steps, exonames, exo, vartan, var, mh_conf_sig, oo_)
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% This function analyses the (posterior or prior) distribution of the
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% endogenous conditional variance decomposition.
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% Copyright (C) 2009 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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if strcmpi(type,'posterior')
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TYPE = 'Posterior';
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PATH = [dname '/metropolis/'];
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else
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TYPE = 'Prior';
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PATH = [dname '/prior/moments/'];
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end
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indx = check_name(vartan,var);
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if isempty(indx)
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disp([ type '_analysis:: ' var ' is not a stationary endogenous variable!'])
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return
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end
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endogenous_variable_index = sum(1:indx);
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exogenous_variable_index = check_name(exonames,exo);
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if isempty(exogenous_variable_index)
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disp([ type '_analysis:: ' exo ' is not a declared exogenous variable!'])
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return
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end
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name = [ var '.' exo ];
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if isfield(oo_, [ TYPE 'TheoreticalMoments' ])
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eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments;'])
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if isfield(temporary_structure,'dsge')
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eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge;'])
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if isfield(temporary_structure,'ConditionalVarianceDecomposition')
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eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge.VarianceDecomposition.mean;'])
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if isfield(temporary_structure,name)
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if sum(Steps-temporary_structure.(name)(1,:)) == 0
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% Nothing (new) to do here...
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return
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end
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end
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end
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end
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end
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ListOfFiles = dir([ PATH fname '_' TYPE 'ConditionalVarianceDecomposition*.mat']);
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i1 = 1; tmp = zeros(NumberOfSimulations,length(Steps));
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for file = 1:length(ListOfFiles)
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load([ PATH fname ListOfFiles(file).name ]);
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% 4D-array (endovar,time,exovar,simul)
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i2 = i1 + size(Conditional_decomposition_array,4) - 1;
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tmp(i1:i2,:) = transpose(dynare_squeeze(Conditional_decomposition_array(endogenous_variable_index,:,exogenous_variable_index,:)));
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i1 = i2+1;
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end
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p_mean = NaN(2,length(Steps));
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p_mean(1,:) = Steps;
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p_median = NaN(1,length(Steps));
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p_variance = NaN(1,length(Steps));
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p_deciles = NaN(9,length(Steps));
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p_density = NaN(2^9,2,length(Steps));
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p_hpdinf = NaN(1,length(Steps));
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p_hpdsup = NaN(1,length(Steps));
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for i=1:length(Steps)
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if ~isconst(tmp(:,i))
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[pp_mean, pp_median, pp_var, hpd_interval, pp_deciles, pp_density] = ...
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posterior_moments(tmp(:,i),1,mh_conf_sig);
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p_mean(2,i) = pp_mean;
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p_median(i) = pp_median;
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p_variance(i) = pp_var;
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p_deciles(:,i) = pp_deciles;
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p_hpdinf(i) = hpd_interval(1);
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p_hpdinf(i) = hpd_interval(2);
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p_density(:,:,i) = pp_density;
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end
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end
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eval(['oo_.' TYPE 'TheoreticalMoments.dsge.ConditionalVarianceDecomposition.mean.' name ' = p_mean;']);
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eval(['oo_.' TYPE 'TheoreticalMoments.dsge.ConditionalVarianceDecomposition.median.' name ' = p_median;']);
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eval(['oo_.' TYPE 'TheoreticalMoments.dsge.ConditionalVarianceDecomposition.variance.' name ' = p_variance;']);
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eval(['oo_.' TYPE 'TheoreticalMoments.dsge.ConditionalVarianceDecomposition.hpdinf.' name ' = p_hpdinf;']);
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eval(['oo_.' TYPE 'TheoreticalMoments.dsge.ConditionalVarianceDecomposition.hpdsup.' name ' = p_hpdsup;']);
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eval(['oo_.' TYPE 'TheoreticalMoments.dsge.ConditionalVarianceDecomposition.deciles.' name ' = p_deciles;']);
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eval(['oo_.' TYPE 'TheoreticalMoments.dsge.ConditionalVarianceDecomposition.density.' name ' = p_density;']);
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@ -1,83 +0,0 @@
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function oo_ = conditional_variance_decomposition_posterior_analysis(NumberOfSimulations, dname, fname, ...
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Steps, exonames, exo, vartan, var, mh_conf_sig, oo_)
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% Copyright (C) 2009 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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indx = check_name(vartan,var);
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if isempty(indx)
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disp(['posterior_analysis:: ' var ' is not a stationary endogenous variable!'])
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return
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end
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endogenous_variable_index = sum(1:indx);
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exogenous_variable_index = check_name(exonames,exo);
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if isempty(exogenous_variable_index)
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disp(['posterior_analysis:: ' exo ' is not a declared exogenous variable!'])
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return
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end
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tmp = dir([ dname '/metropolis/' fname '_PosteriorConditionalVarianceDecomposition*.mat']);
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NumberOfFiles = length(tmp);
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i1 = 1; tmp = zeros(NumberOfSimulations,length(Steps));
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for file = 1:NumberOfFiles
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load([dname '/metropolis/' fname '_PosteriorConditionalVarianceDecomposition' int2str(file) '.mat']);
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% (endovar,time,exovar,simul)
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i2 = i1 + size(Conditional_decomposition_array,4) - 1;
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tmp(i1:i2,:) = transpose(dynare_squeeze(Conditional_decomposition_array(endogenous_variable_index,:,exogenous_variable_index,:)));
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i1 = i2+1;
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end
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name = [ var '.' exo ];
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if isfield(oo_,'PosteriorTheoreticalMoments')
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if isfield(oo_.PosteriorTheoreticalMoments,'dsge')
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if isfield(oo_.PosteriorTheoreticalMoments.dsge,'ConditionalVarianceDecomposition')
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if isfield(oo_.PosteriorTheoreticalMoments.dsge.VarianceDecomposition.mean,name)
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if sum(Steps-oo_.PosteriorTheoreticalMoments.dsge.VarianceDecomposition.mean.(name)(1,:)) == 0
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% Nothing (new) to do here...
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return
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end
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end
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end
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end
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end
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posterior_mean = NaN(2,length(Steps));
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posterior_mean(1,:) = Steps;
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posterior_median = NaN(1,length(Steps));
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posterior_variance = NaN(1,length(Steps));
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posterior_deciles = NaN(9,length(Steps));
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posterior_density = NaN(2^9,2,length(Steps));
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posterior_hpdinf = NaN(1,length(Steps));
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posterior_hpdsup = NaN(1,length(Steps));
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for i=1:length(Steps)
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if ~isconst(tmp(:,i))
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[post_mean, post_median, post_var, hpd_interval, post_deciles, density] = ...
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posterior_moments(tmp(:,i),1,mh_conf_sig);
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posterior_mean(2,i) = post_mean;
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posterior_median(i) = post_median;
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posterior_variance(i) = post_var;
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posterior_deciles(:,i) = post_deciles;
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posterior_hpdinf(i) = hpd_interval(1);
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posterior_hpdinf(i) = hpd_interval(2);
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posterior_density(:,:,i) = density;
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end
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end
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eval(['oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecomposition.mean.' name ' = posterior_mean;']);
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eval(['oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecomposition.median.' name ' = posterior_median;']);
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eval(['oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecomposition.variance.' name ' = posterior_variance;']);
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eval(['oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecomposition.hpdinf.' name ' = posterior_hpdinf;']);
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eval(['oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecomposition.hpdsup.' name ' = posterior_hpdsup;']);
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eval(['oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecomposition.deciles.' name ' = posterior_deciles;']);
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eval(['oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecomposition.density.' name ' = posterior_density;']);
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@ -21,10 +21,10 @@ function oo_ = correlation_mc_analysis(SampleSize,type,dname,fname,vartan,nvar,v
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if strcmpi(type,'posterior')
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TYPE = 'Posterior';
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PATH = [dname '/metropolis/']
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PATH = [dname '/metropolis/'];
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else
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TYPE = 'Prior';
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PATH = [dname '/prior/moments/']
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PATH = [dname '/prior/moments/'];
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end
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indx1 = check_name(vartan,var1);
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@ -84,7 +84,7 @@ function oo_ = correlation_mc_analysis(SampleSize,type,dname,fname,vartan,nvar,v
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ListOfFiles = dir([ PATH fname '_' TYPE 'Correlations*.mat']);
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i1 = 1; tmp = zeros(SampleSize,1);
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for file = 1:length(ListOfFiles)
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load([ PATH fname '_' TYPE 'PosteriorCorrelations' int2str(file) '.mat']);
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load([ PATH ListOfFiles(file).name ]);
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i2 = i1 + rows(Correlation_array) - 1;
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tmp(i1:i2) = Correlation_array(:,indx1,indx2,nar);
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i1 = i2+1;
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@ -93,7 +93,7 @@ function oo_ = correlation_mc_analysis(SampleSize,type,dname,fname,vartan,nvar,v
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if ~isconst(tmp)
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[p_mean, p_median, p_var, hpd_interval, p_deciles, density] = ...
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posterior_moments(tmp,1,mh_conf_sig);
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if isfield(oo_,'PosteriorTheoreticalMoments')
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if isfield(oo_,[ TYPE 'TheoreticalMoments'])
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eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments;'])
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if isfield(temporary_structure,'dsge')
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eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge;'])
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@ -61,7 +61,7 @@ function oo_ = job(type,SampleSize,arg1,arg2,arg3,options_,M_,oo_,nvar,vartan)
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[nvar,vartan,NumberOfFiles] = ...
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dsge_simulated_theoretical_variance_decomposition(SampleSize,M_,options_,oo_,'posterior');
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end
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oo_ = variance_decomposition_posterior_analysis(SampleSize,M_.dname,M_.fname,...
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oo_ = variance_decomposition_mc_analysis(SampleSize,'posterior',M_.dname,M_.fname,...
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M_.exo_names,arg2,vartan,arg1,options_.mh_conf_sig,oo_);
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case OB'correlation'
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if nargin==narg1
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@ -75,7 +75,7 @@ function oo_ = job(type,SampleSize,arg1,arg2,arg3,options_,M_,oo_,nvar,vartan)
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[nvar,vartan,NumberOfFiles] = ...
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dsge_simulated_theoretical_conditional_variance_decomposition(SampleSize,arg3,M_,options_,oo_,'posterior');
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end
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oo_ = conditional_variance_decomposition_posterior_analysis(SampleSize,M_.dname,M_.fname,...
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oo_ = conditional_variance_decomposition_mc_analysis(SampleSize,'posterior',M_.dname,M_.fname,...
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arg3,M_.exo_names,arg2,vartan,arg1,options_.mh_conf_sig,oo_);
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otherwise
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disp('Not yet implemented')
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@ -1,47 +1,83 @@
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function prior_analysis(var_list)
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% function prior_analysis(var_list)
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% performs stochastic simulations for value of parameters drawn from
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% the prior
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%
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% INPUTS:
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% var_list: list of variable names for which results are requested
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%
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% OUTPUTS:
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% none
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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) 2006-2008 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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global M_ options_ oo_ estim_params_ bayestopt_
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old_options = options_;
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if options_.replic < 100
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warning('Prior analysis requires at least 100 replications, preferably many more! options replic reset to 100')
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options_.replic = 100;
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end
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options_.order = 1;
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if options_.forecast
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forcst_unc(oo_.endo_simul(:,1:M_.maximum_lag),var_list);
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end
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options_ = old_options;
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function oo_ = prior_analysis(type,arg1,arg2,arg3,options_,M_,oo_)
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% Copyright (C) 2009 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 = check_prior_analysis_data(type,M_);
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SampleSize = options_.prior_mc;
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switch info
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case {0,1,2}
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MaxMegaBytes = options_.MaximumNumberOfMegaBytes;
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drsize = size_of_the_reduced_form_model(oo_.dr);
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if drsize*SampleSize>MaxMegaBytes
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drsave=0;
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else
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drsave=1;
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end
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load([M_.dname '/prior/definition.mat']);
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prior_sampler(drsave,M_,bayestopt_,options_,oo_);
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clear('bayestopt_');
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oo_ = job(type,SampleSize,arg1,arg2,arg3,options_,M_,oo_);
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case {4,5}
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oo_ = job(type,SampleSize,arg1,arg2,arg3,options_,M_,oo_);
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case 6
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[ivar,vartan] = set_stationary_variables_list;
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nvar = length(ivar);
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oo_ = job(type,SampleSize,arg1,arg2,arg3,options_,M_,oo_,nvar,vartan);
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otherwise
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error(['prior_analysis:: Check_prior_analysis_data gave a meaningless output!'])
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end
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function oo_ = job(type,SampleSize,arg1,arg2,arg3,options_,M_,oo_,nvar,vartan)
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narg1 = 8;
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narg2 = 10;
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if ~(nargin==narg1 || nargin==narg2)
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error('prior_analysis:: Call to function job is buggy!')
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end
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switch type
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case 'variance'
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if nargin==narg1
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[nvar,vartan,NumberOfFiles] = ...
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dsge_simulated_theoretical_covariance(SampleSize,M_,options_,oo_,'prior');
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end
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oo_ = covariance_mc_analysis(SampleSize,'prior',M_.dname,M_.fname,...
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vartan,nvar,arg1,arg2,options_.mh_conf_sig,oo_);
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case 'decomposition'
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if nargin==narg1
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[nvar,vartan,NumberOfFiles] = ...
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dsge_simulated_theoretical_variance_decomposition(SampleSize,M_,options_,oo_,'prior');
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end
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oo_ = variance_decomposition_mc_analysis(SampleSize,'prior',M_.dname,M_.fname,...
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M_.exo_names,arg2,vartan,arg1,options_.mh_conf_sig,oo_);
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case 'correlation'
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if nargin==narg1
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[nvar,vartan,NumberOfFiles] = ...
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dsge_simulated_theoretical_correlation(SampleSize,arg3,M_,options_,oo_,'prior');
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end
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oo_ = correlation_mc_analysis(SampleSize,'prior',M_.dname,M_.fname,...
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vartan,nvar,arg1,arg2,arg3,options_.mh_conf_sig,oo_,M_,options_);
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case 'conditional decomposition'
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if nargin==narg1
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[nvar,vartan,NumberOfFiles] = ...
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dsge_simulated_theoretical_conditional_variance_decomposition(SampleSize,arg3,M_,options_,oo_,'prior');
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end
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oo_ = conditional_variance_decomposition_mc_analysis(SampleSize,'prior',M_.dname,M_.fname,...
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arg3,M_.exo_names,arg2,vartan,arg1,options_.mh_conf_sig,oo_);
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otherwise
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disp('Not yet implemented')
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end
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@ -0,0 +1,91 @@
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function oo_ = variance_decomposition_mc_analysis(NumberOfSimulations,type,dname,fname,exonames,exo,vartan,var,mh_conf_sig,oo_)
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% This function analyses the (posterior or prior) distribution of the
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% endogenous variance decomposition.
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% Copyright (C) 2008-2009 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
|
||||
% 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/>.
|
||||
|
||||
if strcmpi(type,'posterior')
|
||||
TYPE = 'Posterior';
|
||||
PATH = [dname '/metropolis/'];
|
||||
else
|
||||
TYPE = 'Prior';
|
||||
PATH = [dname '/prior/moments/'];
|
||||
end
|
||||
|
||||
indx = check_name(vartan,var);
|
||||
if isempty(indx)
|
||||
disp([ type '_analysis:: ' var ' is not a stationary endogenous variable!'])
|
||||
return
|
||||
end
|
||||
jndx = check_name(exonames,exo);
|
||||
if isempty(jndx)
|
||||
disp([ type '_analysis:: ' exo ' is not a declared exogenous variable!'])
|
||||
return
|
||||
end
|
||||
|
||||
name = [ var '.' exo ];
|
||||
if isfield(oo_, [ TYPE 'TheoreticalMoments'])
|
||||
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments;'])
|
||||
if isfield(temporary_structure,'dsge')
|
||||
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge;'])
|
||||
if isfield(temporary_structure,'VarianceDecomposition')
|
||||
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge.VarianceDecomposition.mean;'])
|
||||
if isfield(temporary_structure,name)
|
||||
% Nothing to do.
|
||||
return
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
ListOfFiles = dir([ PATH fname '_' Type 'VarianceDecomposition*.mat']);
|
||||
i1 = 1; tmp = zeros(NumberOfSimulations,1);
|
||||
indice = (indx-1)*rows(exonames)+jndx;
|
||||
for file = 1:length(LengthOfFiles)
|
||||
load([ PATH ListOfFiles(file).name ]);
|
||||
i2 = i1 + rows(Decomposition_array) - 1;
|
||||
tmp(i1:i2) = Decomposition_array(:,indice);
|
||||
i1 = i2+1;
|
||||
end
|
||||
|
||||
t1 = min(tmp); t2 = max(tmp);
|
||||
t3 = t2-t1;% How to normalize ? t1 and t2 may be zero...
|
||||
if t3<1.0e-12
|
||||
if t1<1.0e-12
|
||||
t1 = 0;
|
||||
end
|
||||
if abs(t1-1)<1.0e-12
|
||||
t1 = 1;
|
||||
end
|
||||
p_mean = t1;
|
||||
p_median = t1;
|
||||
p_var = 0;
|
||||
hpd_interval = NaN(2,1);
|
||||
post_deciles = NaN(9,1);
|
||||
density = NaN;
|
||||
else
|
||||
[p_mean, p_median, p_var, hpd_interval, p_deciles, density] = ...
|
||||
posterior_moments(tmp,1,mh_conf_sig);
|
||||
end
|
||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.VarianceDecomposition.mean.' name ' = p_mean;']);
|
||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.VarianceDecomposition.median.' name ' = p_median;']);
|
||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.VarianceDecomposition.variance.' name ' = p_var;']);
|
||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.VarianceDecomposition.hpdinf.' name ' = hpd_interval(1);']);
|
||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.VarianceDecomposition.hpdsup.' name ' = hpd_interval(2);']);
|
||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.VarianceDecomposition.deciles.' name ' = p_deciles;']);
|
||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.VarianceDecomposition.density.' name ' = density;']);
|
||||
|
|
@ -1,77 +0,0 @@
|
|||
function oo_ = variance_decomposition_posterior_analysis(NumberOfSimulations,dname,fname, ...
|
||||
exonames,exo,vartan,var,mh_conf_sig,oo_)
|
||||
|
||||
% Copyright (C) 2008 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/>.
|
||||
|
||||
indx = check_name(vartan,var);
|
||||
if isempty(indx)
|
||||
disp(['posterior_analysis:: ' var ' is not a stationary endogenous variable!'])
|
||||
return
|
||||
end
|
||||
jndx = check_name(exonames,exo);
|
||||
if isempty(jndx)
|
||||
disp(['posterior_analysis:: ' exo ' is not a declared exogenous variable!'])
|
||||
return
|
||||
end
|
||||
tmp = dir([ dname '/metropolis/' fname '_PosteriorVarianceDecomposition*.mat']);
|
||||
NumberOfFiles = length(tmp);
|
||||
i1 = 1; tmp = zeros(NumberOfSimulations,1);
|
||||
indice = (indx-1)*rows(exonames)+jndx;
|
||||
for file = 1:NumberOfFiles
|
||||
load([dname '/metropolis/' fname '_PosteriorVarianceDecomposition' int2str(file) '.mat']);
|
||||
i2 = i1 + rows(Decomposition_array) - 1;
|
||||
tmp(i1:i2) = Decomposition_array(:,indice);
|
||||
i1 = i2+1;
|
||||
end
|
||||
name = [ var '.' exo ];
|
||||
if isfield(oo_,'PosteriorTheoreticalMoments')
|
||||
if isfield(oo_.PosteriorTheoreticalMoments,'dsge')
|
||||
if isfield(oo_.PosteriorTheoreticalMoments.dsge,'VarianceDecomposition')
|
||||
if isfield(oo_.PosteriorTheoreticalMoments.dsge.VarianceDecomposition.mean,name)
|
||||
% Nothing to do.
|
||||
return
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
t1 = min(tmp); t2 = max(tmp);
|
||||
t3 = t2-t1;% How to normalize ? t1 and t2 may be zero...
|
||||
if t3<1.0e-12
|
||||
if t1<1.0e-12
|
||||
t1 = 0;
|
||||
end
|
||||
if abs(t1-1)<1.0e-12
|
||||
t1 = 1;
|
||||
end
|
||||
post_mean = t1;
|
||||
post_median = t1;
|
||||
post_var = 0;
|
||||
hpd_interval = NaN(2,1);
|
||||
post_deciles = NaN(9,1);
|
||||
density = NaN;
|
||||
else
|
||||
[post_mean, post_median, post_var, hpd_interval, post_deciles, density] = ...
|
||||
posterior_moments(tmp,1,mh_conf_sig);
|
||||
end
|
||||
eval(['oo_.PosteriorTheoreticalMoments.dsge.VarianceDecomposition.mean.' name ' = post_mean;']);
|
||||
eval(['oo_.PosteriorTheoreticalMoments.dsge.VarianceDecomposition.median.' name ' = post_median;']);
|
||||
eval(['oo_.PosteriorTheoreticalMoments.dsge.VarianceDecomposition.variance.' name ' = post_var;']);
|
||||
eval(['oo_.PosteriorTheoreticalMoments.dsge.VarianceDecomposition.hpdinf.' name ' = hpd_interval(1);']);
|
||||
eval(['oo_.PosteriorTheoreticalMoments.dsge.VarianceDecomposition.hpdsup.' name ' = hpd_interval(2);']);
|
||||
eval(['oo_.PosteriorTheoreticalMoments.dsge.VarianceDecomposition.deciles.' name ' = post_deciles;']);
|
||||
eval(['oo_.PosteriorTheoreticalMoments.dsge.VarianceDecomposition.density.' name ' = density;']);
|
||||
Loading…
Reference in New Issue