132 lines
5.6 KiB
Matlab
132 lines
5.6 KiB
Matlab
function oo_ = ...
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conditional_variance_decomposition_mc_analysis(NumberOfSimulations, type, dname, fname, Steps, exonames, exo, var_list, endo, 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' conditional variance decomposition.
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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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% Steps [integers] horizons at which to conduct decomposition
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% exonames [string] (n_exo*char_length) character array with names of exogenous variables
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% exo [string] name of current exogenous
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% variable
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% var_list [string] (n_endo*char_length) character array with name
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% of endogenous variables
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% endogenous_variable_index [integer] index of the current
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% endogenous 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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%
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% OUTPUTS
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% oo_ [structure] Dynare structure where the results are saved.
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% Copyright © 2009-2018 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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exogenous_variable_index = check_name(exonames,exo);
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if isempty(exogenous_variable_index)
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if ~isequal(exo,'ME')
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disp([ type '_analysis:: ' exo ' is not a declared exogenous variable!'])
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end
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return
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end
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endogenous_variable_index = check_name(var_list, endo);
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if isempty(endogenous_variable_index)
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disp([ type '_analysis:: Can''t find ' endo '!'])
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return
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end
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name_1 = endo;
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name_2 = exo;
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name = [ name_1 '.' name_2 ];
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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,'ConditionalVarianceDecomposition')
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temporary_structure = oo_.([TYPE 'TheoreticalMoments']).dsge.ConditionalVarianceDecomposition.Mean;
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if isfield(temporary_structure,name)
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if sum(Steps-temporary_structure.(name)(1,:)) == 0
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% Nothing (new) to do here...
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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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ListOfFiles = dir([ PATH fname '_' TYPE 'ConditionalVarianceDecomposition*.mat']);
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i1 = 1; tmp = zeros(NumberOfSimulations,length(Steps));
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for file = 1:length(ListOfFiles)
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load([ PATH ListOfFiles(file).name ]);
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% 4D-array (endovar,time,exovar,simul)
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i2 = i1 + size(Conditional_decomposition_array,4) - 1;
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tmp(i1:i2,:) = transpose(dynare_squeeze(Conditional_decomposition_array(endogenous_variable_index,:,exogenous_variable_index,:)));
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i1 = i2+1;
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end
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p_mean = NaN(1,length(Steps));
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p_median = NaN(1,length(Steps));
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p_variance = NaN(1,length(Steps));
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p_deciles = NaN(9,length(Steps));
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if options_.estimation.moments_posterior_density.indicator
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p_density = NaN(2^9,2,length(Steps));
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end
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p_hpdinf = NaN(1,length(Steps));
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p_hpdsup = NaN(1,length(Steps));
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for i=1:length(Steps)
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if options_.estimation.moments_posterior_density.indicator
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[pp_mean, pp_median, pp_var, hpd_interval, pp_deciles, pp_density] = ...
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posterior_moments(tmp(:,i),1,mh_conf_sig);
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p_density(:,:,i) = pp_density;
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else
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[pp_mean, pp_median, pp_var, hpd_interval, pp_deciles] = ...
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posterior_moments(tmp(:,i),0,mh_conf_sig);
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end
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p_mean(i) = pp_mean;
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p_median(i) = pp_median;
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p_variance(i) = pp_var;
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p_deciles(:,i) = pp_deciles;
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p_hpdinf(i) = hpd_interval(1);
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p_hpdsup(i) = hpd_interval(2);
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end
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FirstField = sprintf('%sTheoreticalMoments', TYPE);
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oo_.(FirstField).dsge.ConditionalVarianceDecomposition.Steps = Steps;
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oo_.(FirstField).dsge.ConditionalVarianceDecomposition.Mean.(name_1).(name_2) = p_mean;
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oo_.(FirstField).dsge.ConditionalVarianceDecomposition.Median.(name_1).(name_2) = p_median;
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oo_.(FirstField).dsge.ConditionalVarianceDecomposition.Variance.(name_1).(name_2) = p_variance;
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oo_.(FirstField).dsge.ConditionalVarianceDecomposition.HPDinf.(name_1).(name_2) = p_hpdinf;
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oo_.(FirstField).dsge.ConditionalVarianceDecomposition.HPDsup.(name_1).(name_2) = p_hpdsup;
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oo_.(FirstField).dsge.ConditionalVarianceDecomposition.deciles.(name_1).(name_2) = p_deciles;
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if options_.estimation.moments_posterior_density.indicator
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oo_.(FirstField).dsge.ConditionalVarianceDecomposition.density.(name_1).(name_2) = p_density;
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end |