1function oo_ = ... 2 conditional_variance_decomposition_ME_mc_analysis(NumberOfSimulations, type, dname, fname, Steps, exonames, exo, var_list, endo, mh_conf_sig, oo_,options_) 3% This function analyses the (posterior or prior) distribution of the 4% endogenous variables' conditional variance decomposition with measurement error. 5% 6% INPUTS 7% NumberOfSimulations [integer] scalar, number of simulations. 8% type [string] 'prior' or 'posterior' 9% dname [string] directory name where to save 10% fname [string] name of the mod-file 11% Steps [integers] horizons at which to conduct decomposition 12% exonames [string] (n_exo*char_length) character array with names of exogenous variables 13% exo [string] name of current exogenous 14% variable 15% var_list [string] (n_endo*char_length) character array with name 16% of endogenous variables 17% endo [integer] Current endogenous variable 18% mh_conf_sig [double] 2 by 1 vector with upper 19% and lower bound of HPD intervals 20% oo_ [structure] Dynare structure where the results are saved. 21% 22% OUTPUTS 23% oo_ [structure] Dynare structure where the results are saved. 24 25% Copyright (C) 2017-2018 Dynare Team 26% 27% This file is part of Dynare. 28% 29% Dynare is free software: you can redistribute it and/or modify 30% it under the terms of the GNU General Public License as published by 31% the Free Software Foundation, either version 3 of the License, or 32% (at your option) any later version. 33% 34% Dynare is distributed in the hope that it will be useful, 35% but WITHOUT ANY WARRANTY; without even the implied warranty of 36% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 37% GNU General Public License for more details. 38% 39% You should have received a copy of the GNU General Public License 40% along with Dynare. If not, see <http://www.gnu.org/licenses/>. 41 42if strcmpi(type,'posterior') 43 TYPE = 'Posterior'; 44 PATH = [dname '/metropolis/']; 45else 46 TYPE = 'Prior'; 47 PATH = [dname '/prior/moments/']; 48end 49 50endogenous_variable_index = check_name(var_list, endo); 51if isempty(endogenous_variable_index) 52 disp([ type '_analysis:: Can''t find ' endo '!']) 53 return 54end 55 56exogenous_variable_index = check_name(exonames,exo); 57if isempty(exogenous_variable_index) 58 if isequal(exo,'ME') 59 exogenous_variable_index=length(exonames)+1; 60 else 61 disp([ type '_analysis:: ' exo ' is not a declared exogenous variable!']) 62 return 63 end 64end 65 66if (isoctave && octave_ver_less_than('6')) || (~isoctave && matlab_ver_less_than('8.1')) 67 [observable_pos_requested_vars,index_subset,index_observables]=intersect_stable(var_list,options_.varobs); 68else 69 [observable_pos_requested_vars,index_subset,index_observables]=intersect(var_list,options_.varobs,'stable'); 70end 71matrix_pos=strmatch(endo, var_list(index_subset),'exact'); 72name_1 = endo; 73name_2 = exo; 74name = [ name_1 '.' name_2 ]; 75 76if isfield(oo_, [ TYPE 'TheoreticalMoments' ]) 77 temporary_structure = oo_.([TYPE 'TheoreticalMoments']); 78 if isfield(temporary_structure,'dsge') 79 temporary_structure = oo_.([TYPE 'TheoreticalMoments']).dsge; 80 if isfield(temporary_structure,'ConditionalVarianceDecompositionME') 81 temporary_structure = oo_.([TYPE 'TheoreticalMoments']).dsge.ConditionalVarianceDecompositionME.Mean; 82 if isfield(temporary_structure,name) 83 if sum(Steps-temporary_structure.(name)(1,:)) == 0 84 % Nothing (new) to do here... 85 return 86 end 87 end 88 end 89 end 90end 91 92ListOfFiles = dir([ PATH fname '_' TYPE 'ConditionalVarianceDecompME*.mat']); 93i1 = 1; tmp = zeros(NumberOfSimulations,length(Steps)); 94for file = 1:length(ListOfFiles) 95 load([ PATH ListOfFiles(file).name ]); 96 % 4D-array (endovar,time,exovar,simul) 97 i2 = i1 + size(Conditional_decomposition_array_ME,4) - 1; 98 tmp(i1:i2,:) = transpose(dynare_squeeze(Conditional_decomposition_array_ME(matrix_pos,:,exogenous_variable_index,:))); 99 i1 = i2+1; 100end 101 102p_mean = NaN(1,length(Steps)); 103p_median = NaN(1,length(Steps)); 104p_variance = NaN(1,length(Steps)); 105p_deciles = NaN(9,length(Steps)); 106if options_.estimation.moments_posterior_density.indicator 107 p_density = NaN(2^9,2,length(Steps)); 108end 109p_hpdinf = NaN(1,length(Steps)); 110p_hpdsup = NaN(1,length(Steps)); 111for i=1:length(Steps) 112 if options_.estimation.moments_posterior_density.indicator 113 [pp_mean, pp_median, pp_var, hpd_interval, pp_deciles, pp_density] = ... 114 posterior_moments(tmp(:,i),1,mh_conf_sig); 115 p_density(:,:,i) = pp_density; 116 else 117 [pp_mean, pp_median, pp_var, hpd_interval, pp_deciles] = ... 118 posterior_moments(tmp(:,i),0,mh_conf_sig); 119 end 120 p_mean(i) = pp_mean; 121 p_median(i) = pp_median; 122 p_variance(i) = pp_var; 123 p_deciles(:,i) = pp_deciles; 124 p_hpdinf(i) = hpd_interval(1); 125 p_hpdsup(i) = hpd_interval(2); 126end 127 128FirstField = sprintf('%sTheoreticalMoments', TYPE); 129 130oo_.(FirstField).dsge.ConditionalVarianceDecompositionME.Steps = Steps; 131oo_.(FirstField).dsge.ConditionalVarianceDecompositionME.Mean.(name_1).(name_2) = p_mean; 132oo_.(FirstField).dsge.ConditionalVarianceDecompositionME.Median.(name_1).(name_2) = p_median; 133oo_.(FirstField).dsge.ConditionalVarianceDecompositionME.Variance.(name_1).(name_2) = p_variance; 134oo_.(FirstField).dsge.ConditionalVarianceDecompositionME.HPDinf.(name_1).(name_2) = p_hpdinf; 135oo_.(FirstField).dsge.ConditionalVarianceDecompositionME.HPDsup.(name_1).(name_2) = p_hpdsup; 136oo_.(FirstField).dsge.ConditionalVarianceDecompositionME.deciles.(name_1).(name_2) = p_deciles; 137if options_.estimation.moments_posterior_density.indicator 138 oo_.(FirstField).dsge.ConditionalVarianceDecompositionME.density.(name_1).(name_2) = p_density; 139end 140