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function oo_ = ...
conditional_variance_decomposition_ME_mc_analysis(NumberOfSimulations, type, dname, fname, Steps, exonames, exo, var_list, endo, mh_conf_sig, oo_,options_)
% This function analyses the (posterior or prior) distribution of the
% endogenous variables' conditional variance decomposition with measurement error.
%
% 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
% Steps [integers] horizons at which to conduct decomposition
% exonames [string] (n_exo*char_length) character array with names of exogenous variables
% exo [string] name of current exogenous
% variable
% var_list [string] (n_endo*char_length) character array with name
% of endogenous variables
% endo [integer] Current endogenous 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.
%
% OUTPUTS
% oo_ [structure] Dynare structure where the results are saved.
% Copyright (C) 2017-2018 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
endogenous_variable_index = check_name(var_list, endo);
if isempty(endogenous_variable_index)
disp([ type '_analysis:: Can''t find ' endo '!'])
return
end
exogenous_variable_index = check_name(exonames,exo);
if isempty(exogenous_variable_index)
if isequal(exo,'ME')
exogenous_variable_index=length(exonames)+1;
else
disp([ type '_analysis:: ' exo ' is not a declared exogenous variable!'])
return
end
end
if (isoctave && octave_ver_less_than('6')) || (~isoctave && matlab_ver_less_than('8.1'))
[observable_pos_requested_vars,index_subset,index_observables]=intersect_stable(var_list,options_.varobs);
else
[observable_pos_requested_vars,index_subset,index_observables]=intersect(var_list,options_.varobs,'stable');
end
matrix_pos=strmatch(endo, var_list(index_subset),'exact');
name_1 = endo;
name_2 = exo;
name = [ name_1 '.' name_2 ];
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,'ConditionalVarianceDecompositionME')
temporary_structure = oo_.([TYPE 'TheoreticalMoments']).dsge.ConditionalVarianceDecompositionME.Mean;
if isfield(temporary_structure,name)
if sum(Steps-temporary_structure.(name)(1,:)) == 0
% Nothing (new) to do here...
return
end
end
end
end
end
ListOfFiles = dir([ PATH fname '_' TYPE 'ConditionalVarianceDecompME*.mat']);
i1 = 1; tmp = zeros(NumberOfSimulations,length(Steps));
for file = 1:length(ListOfFiles)
load([ PATH ListOfFiles(file).name ]);
% 4D-array (endovar,time,exovar,simul)
i2 = i1 + size(Conditional_decomposition_array_ME,4) - 1;
tmp(i1:i2,:) = transpose(dynare_squeeze(Conditional_decomposition_array_ME(matrix_pos,:,exogenous_variable_index,:)));
i1 = i2+1;
end
p_mean = NaN(1,length(Steps));
p_median = NaN(1,length(Steps));
p_variance = NaN(1,length(Steps));
p_deciles = NaN(9,length(Steps));
if options_.estimation.moments_posterior_density.indicator
p_density = NaN(2^9,2,length(Steps));
end
p_hpdinf = NaN(1,length(Steps));
p_hpdsup = NaN(1,length(Steps));
for i=1:length(Steps)
if options_.estimation.moments_posterior_density.indicator
[pp_mean, pp_median, pp_var, hpd_interval, pp_deciles, pp_density] = ...
posterior_moments(tmp(:,i),1,mh_conf_sig);
p_density(:,:,i) = pp_density;
else
[pp_mean, pp_median, pp_var, hpd_interval, pp_deciles] = ...
posterior_moments(tmp(:,i),0,mh_conf_sig);
end
p_mean(i) = pp_mean;
p_median(i) = pp_median;
p_variance(i) = pp_var;
p_deciles(:,i) = pp_deciles;
p_hpdinf(i) = hpd_interval(1);
p_hpdsup(i) = hpd_interval(2);
end
FirstField = sprintf('%sTheoreticalMoments', TYPE);
oo_.(FirstField).dsge.ConditionalVarianceDecompositionME.Steps = Steps;
oo_.(FirstField).dsge.ConditionalVarianceDecompositionME.Mean.(name_1).(name_2) = p_mean;
oo_.(FirstField).dsge.ConditionalVarianceDecompositionME.Median.(name_1).(name_2) = p_median;
oo_.(FirstField).dsge.ConditionalVarianceDecompositionME.Variance.(name_1).(name_2) = p_variance;
oo_.(FirstField).dsge.ConditionalVarianceDecompositionME.HPDinf.(name_1).(name_2) = p_hpdinf;
oo_.(FirstField).dsge.ConditionalVarianceDecompositionME.HPDsup.(name_1).(name_2) = p_hpdsup;
oo_.(FirstField).dsge.ConditionalVarianceDecompositionME.deciles.(name_1).(name_2) = p_deciles;
if options_.estimation.moments_posterior_density.indicator
oo_.(FirstField).dsge.ConditionalVarianceDecompositionME.density.(name_1).(name_2) = p_density;
end
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