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