142 lines
6.5 KiB
Matlab
142 lines
6.5 KiB
Matlab
function oo_ = covariance_mc_analysis(NumberOfSimulations,type,dname,fname,vartan,nvar,var1,var2,mh_conf_sig,oo_,options_)
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% This function analyses the (posterior or prior) distribution of the
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% endogenous variables' covariance matrix.
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%
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% INPUTS
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% NumberOfSimulations [integer] scalar, number of simulations.
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% type [string] 'prior' or 'posterior'
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% dname [string] directory name where to save
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% fname [string] name of the mod-file
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% vartan [char] array of characters (with nvar rows).
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% nvar [integer] nvar is the number of stationary variables.
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% var1 [string] name of the first variable
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% var2 [string] name of the second variable
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% mh_conf_sig [double] 2 by 1 vector with upper
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% and lower bound of HPD intervals
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% oo_ [structure] Dynare structure where the results are saved.
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% options_ [structure] Dynare options structure
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%
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% OUTPUTS
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% oo_ [structure] Dynare structure where the results are saved.
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% Copyright © 2008-2017 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 <https://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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indx1 = check_name(vartan,var1);
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if isempty(indx1)
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disp([ type '_analysis:: ' var1 ' is not a stationary endogenous variable!'])
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return
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end
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if ~isempty(var2)
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indx2 = check_name(vartan,var2);
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if isempty(indx2)
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disp([ type '_analysis:: ' var2 ' is not a stationary endogenous variable!'])
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return
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end
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else
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indx2 = indx1;
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var2 = var1;
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end
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var1=deblank(var1);
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var2=deblank(var2);
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if isfield(oo_,[ TYPE 'TheoreticalMoments'])
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temporary_structure = oo_.([TYPE, 'TheoreticalMoments']);
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if isfield(temporary_structure,'dsge')
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temporary_structure = oo_.([TYPE, 'TheoreticalMoments']).dsge;
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if isfield(temporary_structure,'covariance')
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temporary_structure = oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.Mean;
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if isfield(temporary_structure,var1)
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temporary_structure_1 = oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.Mean.(var1);
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if isfield(temporary_structure_1,var2)
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% Nothing to do (the covariance matrix is symmetric!).
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return
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end
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else
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if isfield(temporary_structure,var2)
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temporary_structure_2 = oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.Mean.(var2);
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if isfield(temporary_structure_2,var1)
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% Nothing to do (the covariance matrix is symmetric!).
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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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end
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ListOfFiles = dir([ PATH fname '_' TYPE '2ndOrderMoments*.mat']);
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i1 = 1; tmp = zeros(NumberOfSimulations,1);
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if options_.contemporaneous_correlation
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tmp_corr_mat = zeros(NumberOfSimulations,1);
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cov_pos=symmetric_matrix_index(indx1,indx2,nvar);
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var_pos_1=symmetric_matrix_index(indx1,indx1,nvar);
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var_pos_2=symmetric_matrix_index(indx2,indx2,nvar);
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end
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for file = 1:length(ListOfFiles)
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load([ PATH ListOfFiles(file).name ]);
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i2 = i1 + rows(Covariance_matrix) - 1;
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tmp(i1:i2) = Covariance_matrix(:,symmetric_matrix_index(indx1,indx2,nvar));
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if options_.contemporaneous_correlation
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temp=Covariance_matrix(:,cov_pos)./(sqrt(Covariance_matrix(:,var_pos_1)).*sqrt(Covariance_matrix(:,var_pos_2)));
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temp(Covariance_matrix(:,cov_pos)==0)=0; %filter out 0 correlations that would result in 0/0
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tmp_corr_mat(i1:i2)=temp;
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end
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i1 = i2+1;
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end
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if options_.estimation.moments_posterior_density.indicator
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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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oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.density.(var1).(var2) = density;
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else
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[p_mean, p_median, p_var, hpd_interval, p_deciles] = ...
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posterior_moments(tmp,0,mh_conf_sig);
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end
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oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.Mean.(var1).(var2) = p_mean;
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oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.Median.(var1).(var2) = p_median;
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oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.Variance.(var1).(var2) = p_var;
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oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.HPDinf.(var1).(var2) = hpd_interval(1);
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oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.HPDsup.(var1).(var2) = hpd_interval(2);
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oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.deciles.(var1).(var2) = p_deciles;
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if options_.contemporaneous_correlation
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if options_.estimation.moments_posterior_density.indicator
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[p_mean, p_median, p_var, hpd_interval, p_deciles, density] = ...
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posterior_moments(tmp_corr_mat,1,mh_conf_sig);
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oo_.([TYPE, 'TheoreticalMoments']).dsge.contemporeaneous_correlation.density.(var1).(var2) = density;
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else
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[p_mean, p_median, p_var, hpd_interval, p_deciles] = ...
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posterior_moments(tmp_corr_mat,0,mh_conf_sig);
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end
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oo_.([TYPE, 'TheoreticalMoments']).dsge.contemporeaneous_correlation.Mean.(var1).(var2) = p_mean;
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oo_.([TYPE, 'TheoreticalMoments']).dsge.contemporeaneous_correlation.Median.(var1).(var2) = p_median;
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oo_.([TYPE, 'TheoreticalMoments']).dsge.contemporeaneous_correlation.Variance.(var1).(var2) = p_var;
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oo_.([TYPE, 'TheoreticalMoments']).dsge.contemporeaneous_correlation.HPDinf.(var1).(var2) = hpd_interval(1);
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oo_.([TYPE, 'TheoreticalMoments']).dsge.contemporeaneous_correlation.HPDsup.(var1).(var2) = hpd_interval(2);
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oo_.([TYPE, 'TheoreticalMoments']).dsge.contemporeaneous_correlation.deciles.(var1).(var2) = p_deciles;
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end
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