compute_moments_varendo: skip variance decomposition at higher order
Johannes Pfeifer
parent
9f903db283
commit
dedfd0c08f
GPG Key ID:
295C1FE89E17EB3C
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@ -112,177 +112,180 @@ else
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end
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% VARIANCE DECOMPOSITION.
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if M_.exo_nbr > 1
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if ~NoDecomposition
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temp=NaN(NumberOfEndogenousVariables,NumberOfExogenousVariables);
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if posterior
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for i=1:NumberOfEndogenousVariables
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for j=1:NumberOfExogenousVariables
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oo_ = posterior_analysis('decomposition', var_list_{i}, M_.exo_names{j}, [], options_, M_, oo_);
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temp(i,j) = oo_.PosteriorTheoreticalMoments.dsge.VarianceDecomposition.Mean.(var_list_{i}).(M_.exo_names{j});
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end
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end
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title='Posterior mean variance decomposition (in percent)';
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save_name_string='dsge_post_mean_var_decomp_uncond';
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else
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for i=1:NumberOfEndogenousVariables
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for j=1:NumberOfExogenousVariables
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oo_ = prior_analysis('decomposition', var_list_{i}, M_.exo_names{j}, [], options_, M_, oo_);
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temp(i,j)=oo_.PriorTheoreticalMoments.dsge.VarianceDecomposition.Mean.(var_list_{i}).(M_.exo_names{j});
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end
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end
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title='Prior mean variance decomposition (in percent)';
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save_name_string='dsge_prior_mean_var_decomp_uncond';
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end
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title=add_filter_subtitle(title, options_);
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headers = M_.exo_names;
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headers(M_.exo_names_orig_ord) = headers;
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headers = vertcat(' ', headers);
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lh = cellofchararraymaxlength(var_list_)+2;
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dyntable(options_, title, headers, var_list_, 100*temp, lh, 8, 2);
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if options_.TeX
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headers = M_.exo_names_tex;
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headers = vertcat(' ', headers);
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labels = var_list_tex;
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lh = size(labels,2)+2;
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dyn_latex_table(M_, options_, title, save_name_string, headers, labels, 100*temp, lh, 8, 2);
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end
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skipline();
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end
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skipline();
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if ~all(diag(M_.H)==0)
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if isoctave && octave_ver_less_than('6')
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[observable_name_requested_vars, varlist_pos] = intersect_stable(var_list_, options_.varobs);
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else
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[observable_name_requested_vars, varlist_pos] = intersect(var_list_, options_.varobs, 'stable');
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end
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if ~isempty(observable_name_requested_vars)
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NumberOfObservedEndogenousVariables = length(observable_name_requested_vars);
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temp = NaN(NumberOfObservedEndogenousVariables, NumberOfExogenousVariables+1);
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if options_.order==1
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if M_.exo_nbr > 1
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if ~NoDecomposition
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temp=NaN(NumberOfEndogenousVariables,NumberOfExogenousVariables);
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if posterior
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for i=1:NumberOfObservedEndogenousVariables
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for i=1:NumberOfEndogenousVariables
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for j=1:NumberOfExogenousVariables
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temp(i,j,:) = oo_.PosteriorTheoreticalMoments.dsge.VarianceDecompositionME.Mean.(observable_name_requested_vars{i}).(M_.exo_names{j});
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oo_ = posterior_analysis('decomposition', var_list_{i}, M_.exo_names{j}, [], options_, M_, oo_);
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temp(i,j) = oo_.PosteriorTheoreticalMoments.dsge.VarianceDecomposition.Mean.(var_list_{i}).(M_.exo_names{j});
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end
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endo_index_varlist = strmatch(observable_name_requested_vars{i}, var_list_, 'exact');
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oo_ = posterior_analysis('decomposition', var_list_{endo_index_varlist}, 'ME', [], options_, M_, oo_);
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temp(i,j+1,:) = oo_.PosteriorTheoreticalMoments.dsge.VarianceDecompositionME.Mean.(observable_name_requested_vars{i}).('ME');
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end
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title='Posterior mean variance decomposition (in percent) with measurement error';
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save_name_string='dsge_post_mean_var_decomp_uncond_ME';
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title='Posterior mean variance decomposition (in percent)';
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save_name_string='dsge_post_mean_var_decomp_uncond';
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else
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for i=1:NumberOfObservedEndogenousVariables
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for i=1:NumberOfEndogenousVariables
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for j=1:NumberOfExogenousVariables
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temp(i,j,:) = oo_.PriorTheoreticalMoments.dsge.VarianceDecompositionME.Mean.(observable_name_requested_vars{i}).(M_.exo_names{j});
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oo_ = prior_analysis('decomposition', var_list_{i}, M_.exo_names{j}, [], options_, M_, oo_);
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temp(i,j)=oo_.PriorTheoreticalMoments.dsge.VarianceDecomposition.Mean.(var_list_{i}).(M_.exo_names{j});
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end
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endo_index_varlist = strmatch(observable_name_requested_vars{i}, var_list_, 'exact');
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oo_ = prior_analysis('decomposition', var_list_{endo_index_varlist}, 'ME', [], options_, M_, oo_);
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temp(i,j+1,:) = oo_.PriorTheoreticalMoments.dsge.VarianceDecompositionME.Mean.(observable_name_requested_vars{i}).('ME');
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end
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title='Prior mean variance decomposition (in percent) with measurement error';
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save_name_string='dsge_prior_mean_var_decomp_uncond_ME';
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title='Prior mean variance decomposition (in percent)';
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save_name_string='dsge_prior_mean_var_decomp_uncond';
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end
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title=add_filter_subtitle(title, options_);
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headers = M_.exo_names;
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headers(M_.exo_names_orig_ord) = headers;
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headers = vertcat(' ', headers, 'ME');
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lh = cellofchararraymaxlength(var_list_)+2;
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dyntable(options_, title, headers, observable_name_requested_vars,100*temp,lh,8,2);
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if options_.TeX
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headers = M_.exo_names_tex;
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headers = vertcat(' ', headers, 'ME');
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labels = var_list_tex(varlist_pos);
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lh = cellofchararraymaxlength(labels)+2;
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dyn_latex_table(M_, options_, title, save_name_string, headers, labels, 100*temp, lh, 8, 2);
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end
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skipline();
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end
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end
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% CONDITIONAL VARIANCE DECOMPOSITION.
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if Steps
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temp = NaN(NumberOfEndogenousVariables, NumberOfExogenousVariables, length(Steps));
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if posterior
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for i=1:NumberOfEndogenousVariables
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for j=1:NumberOfExogenousVariables
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oo_ = posterior_analysis('conditional decomposition', var_list_{i}, M_.exo_names{j}, Steps, options_, M_, oo_);
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temp(i,j,:) = oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecomposition.Mean.(var_list_{i}).(M_.exo_names{j});
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end
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end
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title = 'Posterior mean conditional variance decomposition (in percent)';
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save_name_string = 'dsge_post_mean_var_decomp_cond_h';
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else
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for i=1:NumberOfEndogenousVariables
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for j=1:NumberOfExogenousVariables
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oo_ = prior_analysis('conditional decomposition', var_list_{i}, M_.exo_names{j}, Steps, options_, M_, oo_);
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temp(i,j,:) = oo_.PriorTheoreticalMoments.dsge.ConditionalVarianceDecomposition.Mean.(var_list_{i}).(M_.exo_names{j});
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end
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end
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title = 'Prior mean conditional variance decomposition (in percent)';
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save_name_string = 'dsge_prior_mean_var_decomp_cond_h';
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end
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for step_iter=1:length(Steps)
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title_print=[title, ' Period ' int2str(Steps(step_iter))];
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headers = M_.exo_names;
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headers(M_.exo_names_orig_ord) = headers;
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headers = vertcat(' ', headers);
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lh = cellofchararraymaxlength(var_list_)+2;
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dyntable(options_,title_print,headers, var_list_,100* ...
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temp(:,:,step_iter),lh,8,2);
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dyntable(options_, title, headers, var_list_, 100*temp, lh, 8, 2);
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if options_.TeX
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headers = M_.exo_names_tex;
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headers = vertcat(' ', headers);
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labels = var_list_tex;
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lh = cellofchararraymaxlength(labels)+2;
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dyn_latex_table(M_, options_, title_print, [save_name_string, int2str(Steps(step_iter))], headers, labels, 100*temp(:,:,step_iter), lh, 8, 2);
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lh = size(labels,2)+2;
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dyn_latex_table(M_, options_, title, save_name_string, headers, labels, 100*temp, lh, 8, 2);
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end
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skipline();
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end
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skipline();
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if ~all(diag(M_.H)==0)
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if isoctave && octave_ver_less_than('6')
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[observable_name_requested_vars, varlist_pos] = intersect_stable(var_list_, options_.varobs);
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else
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[observable_name_requested_vars, varlist_pos] = intersect(var_list_, options_.varobs, 'stable');
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end
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if ~isempty(observable_name_requested_vars)
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NumberOfObservedEndogenousVariables = length(observable_name_requested_vars);
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temp=NaN(NumberOfObservedEndogenousVariables,NumberOfExogenousVariables+1,length(Steps));
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temp = NaN(NumberOfObservedEndogenousVariables, NumberOfExogenousVariables+1);
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if posterior
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for i=1:NumberOfObservedEndogenousVariables
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for j=1:NumberOfExogenousVariables
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temp(i,j,:) = oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecompositionME.Mean.(observable_name_requested_vars{i}).(M_.exo_names{j});
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temp(i,j,:) = oo_.PosteriorTheoreticalMoments.dsge.VarianceDecompositionME.Mean.(observable_name_requested_vars{i}).(M_.exo_names{j});
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end
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endo_index_varlist = strmatch(observable_name_requested_vars{i}, var_list_, 'exact');
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oo_ = posterior_analysis('conditional decomposition', var_list_{endo_index_varlist}, 'ME', Steps, options_, M_, oo_);
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temp(i,j+1,:) = oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecompositionME.Mean.(observable_name_requested_vars{i}).('ME');
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oo_ = posterior_analysis('decomposition', var_list_{endo_index_varlist}, 'ME', [], options_, M_, oo_);
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temp(i,j+1,:) = oo_.PosteriorTheoreticalMoments.dsge.VarianceDecompositionME.Mean.(observable_name_requested_vars{i}).('ME');
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end
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title = 'Posterior mean conditional variance decomposition (in percent) with measurement error';
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save_name_string = 'dsge_post_mean_var_decomp_ME_cond_h';
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title='Posterior mean variance decomposition (in percent) with measurement error';
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save_name_string='dsge_post_mean_var_decomp_uncond_ME';
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else
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for i=1:NumberOfObservedEndogenousVariables
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for j=1:NumberOfExogenousVariables
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temp(i,j,:) = oo_.PriorTheoreticalMoments.dsge.ConditionalVarianceDecompositionME.Mean.(observable_name_requested_vars{i}).(M_.exo_names{j});
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temp(i,j,:) = oo_.PriorTheoreticalMoments.dsge.VarianceDecompositionME.Mean.(observable_name_requested_vars{i}).(M_.exo_names{j});
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end
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endo_index_varlist = strmatch(observable_name_requested_vars{i}, var_list_, 'exact');
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oo_ = prior_analysis('conditional decomposition', var_list_{endo_index_varlist}, 'ME', Steps, options_, M_, oo_);
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temp(i,j+1,:) = oo_.PriorTheoreticalMoments.dsge.ConditionalVarianceDecompositionME.Mean.(observable_name_requested_vars{i}).('ME');
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oo_ = prior_analysis('decomposition', var_list_{endo_index_varlist}, 'ME', [], options_, M_, oo_);
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temp(i,j+1,:) = oo_.PriorTheoreticalMoments.dsge.VarianceDecompositionME.Mean.(observable_name_requested_vars{i}).('ME');
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end
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title = 'Prior mean conditional variance decomposition (in percent) with measurement error';
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save_name_string = 'dsge_prior_mean_var_decomp_ME_cond_h';
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title='Prior mean variance decomposition (in percent) with measurement error';
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save_name_string='dsge_prior_mean_var_decomp_uncond_ME';
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end
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for step_iter=1:length(Steps)
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title_print = [title, ' Period ' int2str(Steps(step_iter))];
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headers = M_.exo_names;
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headers(M_.exo_names_orig_ord) = headers;
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title=add_filter_subtitle(title, options_);
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headers = M_.exo_names;
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headers(M_.exo_names_orig_ord) = headers;
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headers = vertcat(' ', headers, 'ME');
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lh = cellofchararraymaxlength(var_list_)+2;
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dyntable(options_, title, headers, observable_name_requested_vars,100*temp,lh,8,2);
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if options_.TeX
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headers = M_.exo_names_tex;
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headers = vertcat(' ', headers, 'ME');
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lh = cellofchararraymaxlength(var_list_)+2;
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dyntable(options_, title_print, headers, observable_name_requested_vars, 100*temp(:,:,step_iter), lh, 8, 2);
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if options_.TeX
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headers = M_.exo_names_tex;
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headers = vertcat(' ', headers, 'ME');
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labels = var_list_tex(varlist_pos);
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lh = cellofchararraymaxlength(labels)+2;
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dyn_latex_table(M_, options_, title_print, [save_name_string, int2str(Steps(step_iter))], headers, labels, 100*temp(:,:,step_iter), lh, 8, 2);
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end
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labels = var_list_tex(varlist_pos);
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lh = cellofchararraymaxlength(labels)+2;
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dyn_latex_table(M_, options_, title, save_name_string, headers, labels, 100*temp, lh, 8, 2);
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end
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skipline();
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end
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end
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% CONDITIONAL VARIANCE DECOMPOSITION.
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if Steps
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temp = NaN(NumberOfEndogenousVariables, NumberOfExogenousVariables, length(Steps));
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if posterior
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for i=1:NumberOfEndogenousVariables
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for j=1:NumberOfExogenousVariables
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oo_ = posterior_analysis('conditional decomposition', var_list_{i}, M_.exo_names{j}, Steps, options_, M_, oo_);
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temp(i,j,:) = oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecomposition.Mean.(var_list_{i}).(M_.exo_names{j});
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end
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end
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title = 'Posterior mean conditional variance decomposition (in percent)';
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save_name_string = 'dsge_post_mean_var_decomp_cond_h';
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else
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for i=1:NumberOfEndogenousVariables
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for j=1:NumberOfExogenousVariables
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oo_ = prior_analysis('conditional decomposition', var_list_{i}, M_.exo_names{j}, Steps, options_, M_, oo_);
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temp(i,j,:) = oo_.PriorTheoreticalMoments.dsge.ConditionalVarianceDecomposition.Mean.(var_list_{i}).(M_.exo_names{j});
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end
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end
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title = 'Prior mean conditional variance decomposition (in percent)';
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save_name_string = 'dsge_prior_mean_var_decomp_cond_h';
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end
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for step_iter=1:length(Steps)
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title_print=[title, ' Period ' int2str(Steps(step_iter))];
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headers = M_.exo_names;
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headers(M_.exo_names_orig_ord) = headers;
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headers = vertcat(' ', headers);
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lh = cellofchararraymaxlength(var_list_)+2;
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dyntable(options_,title_print,headers, var_list_,100* ...
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temp(:,:,step_iter),lh,8,2);
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if options_.TeX
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headers = M_.exo_names_tex;
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headers = vertcat(' ', headers);
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labels = var_list_tex;
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lh = cellofchararraymaxlength(labels)+2;
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dyn_latex_table(M_, options_, title_print, [save_name_string, int2str(Steps(step_iter))], headers, labels, 100*temp(:,:,step_iter), lh, 8, 2);
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end
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end
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skipline();
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if ~all(diag(M_.H)==0)
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if ~isempty(observable_name_requested_vars)
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NumberOfObservedEndogenousVariables = length(observable_name_requested_vars);
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temp=NaN(NumberOfObservedEndogenousVariables,NumberOfExogenousVariables+1,length(Steps));
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if posterior
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for i=1:NumberOfObservedEndogenousVariables
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for j=1:NumberOfExogenousVariables
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temp(i,j,:) = oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecompositionME.Mean.(observable_name_requested_vars{i}).(M_.exo_names{j});
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end
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endo_index_varlist = strmatch(observable_name_requested_vars{i}, var_list_, 'exact');
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oo_ = posterior_analysis('conditional decomposition', var_list_{endo_index_varlist}, 'ME', Steps, options_, M_, oo_);
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temp(i,j+1,:) = oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecompositionME.Mean.(observable_name_requested_vars{i}).('ME');
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end
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title = 'Posterior mean conditional variance decomposition (in percent) with measurement error';
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save_name_string = 'dsge_post_mean_var_decomp_ME_cond_h';
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else
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for i=1:NumberOfObservedEndogenousVariables
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for j=1:NumberOfExogenousVariables
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temp(i,j,:) = oo_.PriorTheoreticalMoments.dsge.ConditionalVarianceDecompositionME.Mean.(observable_name_requested_vars{i}).(M_.exo_names{j});
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end
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endo_index_varlist = strmatch(observable_name_requested_vars{i}, var_list_, 'exact');
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oo_ = prior_analysis('conditional decomposition', var_list_{endo_index_varlist}, 'ME', Steps, options_, M_, oo_);
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temp(i,j+1,:) = oo_.PriorTheoreticalMoments.dsge.ConditionalVarianceDecompositionME.Mean.(observable_name_requested_vars{i}).('ME');
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end
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title = 'Prior mean conditional variance decomposition (in percent) with measurement error';
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save_name_string = 'dsge_prior_mean_var_decomp_ME_cond_h';
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end
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for step_iter=1:length(Steps)
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title_print = [title, ' Period ' int2str(Steps(step_iter))];
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headers = M_.exo_names;
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headers(M_.exo_names_orig_ord) = headers;
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headers = vertcat(' ', headers, 'ME');
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lh = cellofchararraymaxlength(var_list_)+2;
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dyntable(options_, title_print, headers, observable_name_requested_vars, 100*temp(:,:,step_iter), lh, 8, 2);
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if options_.TeX
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headers = M_.exo_names_tex;
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headers = vertcat(' ', headers, 'ME');
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labels = var_list_tex(varlist_pos);
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lh = cellofchararraymaxlength(labels)+2;
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dyn_latex_table(M_, options_, title_print, [save_name_string, int2str(Steps(step_iter))], headers, labels, 100*temp(:,:,step_iter), lh, 8, 2);
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end
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end
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skipline();
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end
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end
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end
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end
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else
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fprintf(['Estimation::compute_moments_varendo: (conditional) variance decomposition only available at order=1. Skipping computations\n'])
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end
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fprintf(' Done!\n');
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