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function oo_ = correlation_mc_analysis(SampleSize,type,dname,fname,vartan,nvar,var1,var2,nar,mh_conf_sig,oo_,M_,options_)
% This function analyses the (posterior or prior) distribution of the
% endogenous variables correlation function.
% 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,'correlation')
temporary_structure = oo_.([TYPE, 'TheoreticalMoments']).dsge.correlation.Mean;
if isfield(temporary_structure,deblank(var1))
temporary_structure_1 = oo_.([TYPE, 'TheoreticalMoments']).dsge.correlation.Mean.(var1);
if isfield(temporary_structure_1,deblank(var2))
temporary_structure_2 = temporary_structure_1.(var2);
l1 = length(temporary_structure_2);
if l1<nar
% INITIALIZATION:
oo_ = initialize_output_structure(var1,var2,nar,type,oo_);
delete([PATH fname '_' TYPE 'Correlations*'])
[nvar,vartan,NumberOfFiles] = ...
dsge_simulated_theoretical_correlation(SampleSize,nar,M_,options_,oo_,type);
else
if ~isnan(temporary_structure_2(nar))
%Nothing to do.
return
end
end
else
oo_ = initialize_output_structure(var1,var2,nar,TYPE,oo_,options_);
end
else
oo_ = initialize_output_structure(var1,var2,nar,TYPE,oo_,options_);
end
else
oo_ = initialize_output_structure(var1,var2,nar,TYPE,oo_,options_);
end
else
oo_ = initialize_output_structure(var1,var2,nar,TYPE,oo_,options_);
end
else
oo_ = initialize_output_structure(var1,var2,nar,TYPE,oo_,options_);
end
ListOfFiles = dir([ PATH fname '_' TYPE 'Correlations*.mat']);
i1 = 1; tmp = zeros(SampleSize,1);
for file = 1:length(ListOfFiles)
load([ PATH ListOfFiles(file).name ]);
i2 = i1 + rows(Correlation_array) - 1;
tmp(i1:i2) = Correlation_array(:,indx1,indx2,nar);
i1 = i2+1;
end
name = [ var1 '.' var2 ];
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);
else
[p_mean, p_median, p_var, hpd_interval, p_deciles] = ...
posterior_moments(tmp,0,mh_conf_sig);
end
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,'correlation')
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'Mean',nar,p_mean);
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'Median',nar,p_median);
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'Variance',nar,p_var);
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'HPDinf',nar,hpd_interval(1));
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'HPDsup',nar,hpd_interval(2));
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'deciles',nar,p_deciles);
if options_.estimation.moments_posterior_density.indicator
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'density',nar,density);
end
end
end
end
function oo_ = initialize_output_structure(var1,var2,nar,type,oo_,options_)
oo_.([type, 'TheoreticalMoments']).dsge.correlation.Mean.(var1).(var2) = NaN(nar,1);
oo_.([type, 'TheoreticalMoments']).dsge.correlation.Median.(var1).(var2) = NaN(nar,1);
oo_.([type, 'TheoreticalMoments']).dsge.correlation.Variance.(var1).(var2) = NaN(nar,1);
oo_.([type, 'TheoreticalMoments']).dsge.correlation.HPDinf.(var1).(var2) = NaN(nar,1);
oo_.([type, 'TheoreticalMoments']).dsge.correlation.HPDsup.(var1).(var2) = NaN(nar,1);
oo_.([type, 'TheoreticalMoments']).dsge.correlation.deciles.(var1).(var2) = cell(nar,1);
if options_.estimation.moments_posterior_density.indicator
oo_.([type, 'TheoreticalMoments']).dsge.correlation.density.(var1).(var2) = cell(nar,1);
end
for i=1:nar
if options_.estimation.moments_posterior_density.indicator
oo_.([type, 'TheoreticalMoments']).dsge.correlation.density.(var1).(var2)(i,1) = {NaN};
end
oo_.([type, 'TheoreticalMoments']).dsge.correlation.deciles.(var1).(var2)(i,1) = {NaN};
end
function oo_ = fill_output_structure(var1,var2,type,oo_,moment,lag,result)
switch moment
case {'Mean','Median','Variance','HPDinf','HPDsup'}
oo_.([type, 'TheoreticalMoments']).dsge.correlation.(moment).(var1).(var2)(lag,1) = result;
case {'deciles','density'}
oo_.([type, 'TheoreticalMoments']).dsge.correlation.(moment).(var1).(var2)(lag,1) = {result};
otherwise
disp('fill_output_structure:: Unknown field!')
end
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