146 lines
6.3 KiB
Matlab
146 lines
6.3 KiB
Matlab
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 © 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 <https://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 |