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Verified Commit dedfd0c0 authored by Johannes Pfeifer 's avatar Johannes Pfeifer Committed by Stéphane Adjemian
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compute_moments_varendo: skip variance decomposition at higher order

parent 9f903db2
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matlab/compute_moments_varendo.m
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......@@ -112,177 +112,180 @@ else
end
% VARIANCE DECOMPOSITION.
if M_.exo_nbr > 1
if ~NoDecomposition
temp=NaN(NumberOfEndogenousVariables,NumberOfExogenousVariables);
if posterior
for i=1:NumberOfEndogenousVariables
for j=1:NumberOfExogenousVariables
oo_ = posterior_analysis('decomposition', var_list_{i}, M_.exo_names{j}, [], options_, M_, oo_);
temp(i,j) = oo_.PosteriorTheoreticalMoments.dsge.VarianceDecomposition.Mean.(var_list_{i}).(M_.exo_names{j});
end
end
title='Posterior mean variance decomposition (in percent)';
save_name_string='dsge_post_mean_var_decomp_uncond';
else
for i=1:NumberOfEndogenousVariables
for j=1:NumberOfExogenousVariables
oo_ = prior_analysis('decomposition', var_list_{i}, M_.exo_names{j}, [], options_, M_, oo_);
temp(i,j)=oo_.PriorTheoreticalMoments.dsge.VarianceDecomposition.Mean.(var_list_{i}).(M_.exo_names{j});
end
end
title='Prior mean variance decomposition (in percent)';
save_name_string='dsge_prior_mean_var_decomp_uncond';
end
title=add_filter_subtitle(title, options_);
headers = M_.exo_names;
headers(M_.exo_names_orig_ord) = headers;
headers = vertcat(' ', headers);
lh = cellofchararraymaxlength(var_list_)+2;
dyntable(options_, title, headers, var_list_, 100*temp, lh, 8, 2);
if options_.TeX
headers = M_.exo_names_tex;
headers = vertcat(' ', headers);
labels = var_list_tex;
lh = size(labels,2)+2;
dyn_latex_table(M_, options_, title, save_name_string, headers, labels, 100*temp, lh, 8, 2);
end
skipline();
end
skipline();
if ~all(diag(M_.H)==0)
if isoctave && octave_ver_less_than('6')
[observable_name_requested_vars, varlist_pos] = intersect_stable(var_list_, options_.varobs);
else
[observable_name_requested_vars, varlist_pos] = intersect(var_list_, options_.varobs, 'stable');
end
if ~isempty(observable_name_requested_vars)
NumberOfObservedEndogenousVariables = length(observable_name_requested_vars);
temp = NaN(NumberOfObservedEndogenousVariables, NumberOfExogenousVariables+1);
if options_.order==1
if M_.exo_nbr > 1
if ~NoDecomposition
temp=NaN(NumberOfEndogenousVariables,NumberOfExogenousVariables);
if posterior
for i=1:NumberOfObservedEndogenousVariables
for i=1:NumberOfEndogenousVariables
for j=1:NumberOfExogenousVariables
temp(i,j,:) = oo_.PosteriorTheoreticalMoments.dsge.VarianceDecompositionME.Mean.(observable_name_requested_vars{i}).(M_.exo_names{j});
oo_ = posterior_analysis('decomposition', var_list_{i}, M_.exo_names{j}, [], options_, M_, oo_);
temp(i,j) = oo_.PosteriorTheoreticalMoments.dsge.VarianceDecomposition.Mean.(var_list_{i}).(M_.exo_names{j});
end
endo_index_varlist = strmatch(observable_name_requested_vars{i}, var_list_, 'exact');
oo_ = posterior_analysis('decomposition', var_list_{endo_index_varlist}, 'ME', [], options_, M_, oo_);
temp(i,j+1,:) = oo_.PosteriorTheoreticalMoments.dsge.VarianceDecompositionME.Mean.(observable_name_requested_vars{i}).('ME');
end
title='Posterior mean variance decomposition (in percent) with measurement error';
save_name_string='dsge_post_mean_var_decomp_uncond_ME';
title='Posterior mean variance decomposition (in percent)';
save_name_string='dsge_post_mean_var_decomp_uncond';
else
for i=1:NumberOfObservedEndogenousVariables
for i=1:NumberOfEndogenousVariables
for j=1:NumberOfExogenousVariables
temp(i,j,:) = oo_.PriorTheoreticalMoments.dsge.VarianceDecompositionME.Mean.(observable_name_requested_vars{i}).(M_.exo_names{j});
oo_ = prior_analysis('decomposition', var_list_{i}, M_.exo_names{j}, [], options_, M_, oo_);
temp(i,j)=oo_.PriorTheoreticalMoments.dsge.VarianceDecomposition.Mean.(var_list_{i}).(M_.exo_names{j});
end
endo_index_varlist = strmatch(observable_name_requested_vars{i}, var_list_, 'exact');
oo_ = prior_analysis('decomposition', var_list_{endo_index_varlist}, 'ME', [], options_, M_, oo_);
temp(i,j+1,:) = oo_.PriorTheoreticalMoments.dsge.VarianceDecompositionME.Mean.(observable_name_requested_vars{i}).('ME');
end
title='Prior mean variance decomposition (in percent) with measurement error';
save_name_string='dsge_prior_mean_var_decomp_uncond_ME';
title='Prior mean variance decomposition (in percent)';
save_name_string='dsge_prior_mean_var_decomp_uncond';
end
title=add_filter_subtitle(title, options_);
headers = M_.exo_names;
headers(M_.exo_names_orig_ord) = headers;
headers = vertcat(' ', headers, 'ME');
lh = cellofchararraymaxlength(var_list_)+2;
dyntable(options_, title, headers, observable_name_requested_vars,100*temp,lh,8,2);
if options_.TeX
headers = M_.exo_names_tex;
headers = vertcat(' ', headers, 'ME');
labels = var_list_tex(varlist_pos);
lh = cellofchararraymaxlength(labels)+2;
dyn_latex_table(M_, options_, title, save_name_string, headers, labels, 100*temp, lh, 8, 2);
end
skipline();
end
end
% CONDITIONAL VARIANCE DECOMPOSITION.
if Steps
temp = NaN(NumberOfEndogenousVariables, NumberOfExogenousVariables, length(Steps));
if posterior
for i=1:NumberOfEndogenousVariables
for j=1:NumberOfExogenousVariables
oo_ = posterior_analysis('conditional decomposition', var_list_{i}, M_.exo_names{j}, Steps, options_, M_, oo_);
temp(i,j,:) = oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecomposition.Mean.(var_list_{i}).(M_.exo_names{j});
end
end
title = 'Posterior mean conditional variance decomposition (in percent)';
save_name_string = 'dsge_post_mean_var_decomp_cond_h';
else
for i=1:NumberOfEndogenousVariables
for j=1:NumberOfExogenousVariables
oo_ = prior_analysis('conditional decomposition', var_list_{i}, M_.exo_names{j}, Steps, options_, M_, oo_);
temp(i,j,:) = oo_.PriorTheoreticalMoments.dsge.ConditionalVarianceDecomposition.Mean.(var_list_{i}).(M_.exo_names{j});
end
end
title = 'Prior mean conditional variance decomposition (in percent)';
save_name_string = 'dsge_prior_mean_var_decomp_cond_h';
end
for step_iter=1:length(Steps)
title_print=[title, ' Period ' int2str(Steps(step_iter))];
headers = M_.exo_names;
headers(M_.exo_names_orig_ord) = headers;
headers = vertcat(' ', headers);
lh = cellofchararraymaxlength(var_list_)+2;
dyntable(options_,title_print,headers, var_list_,100* ...
temp(:,:,step_iter),lh,8,2);
dyntable(options_, title, headers, var_list_, 100*temp, lh, 8, 2);
if options_.TeX
headers = M_.exo_names_tex;
headers = vertcat(' ', headers);
labels = var_list_tex;
lh = cellofchararraymaxlength(labels)+2;
dyn_latex_table(M_, options_, title_print, [save_name_string, int2str(Steps(step_iter))], headers, labels, 100*temp(:,:,step_iter), lh, 8, 2);
lh = size(labels,2)+2;
dyn_latex_table(M_, options_, title, save_name_string, headers, labels, 100*temp, lh, 8, 2);
end
skipline();
end
skipline();
if ~all(diag(M_.H)==0)
if isoctave && octave_ver_less_than('6')
[observable_name_requested_vars, varlist_pos] = intersect_stable(var_list_, options_.varobs);
else
[observable_name_requested_vars, varlist_pos] = intersect(var_list_, options_.varobs, 'stable');
end
if ~isempty(observable_name_requested_vars)
NumberOfObservedEndogenousVariables = length(observable_name_requested_vars);
temp=NaN(NumberOfObservedEndogenousVariables,NumberOfExogenousVariables+1,length(Steps));
temp = NaN(NumberOfObservedEndogenousVariables, NumberOfExogenousVariables+1);
if posterior
for i=1:NumberOfObservedEndogenousVariables
for j=1:NumberOfExogenousVariables
temp(i,j,:) = oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecompositionME.Mean.(observable_name_requested_vars{i}).(M_.exo_names{j});
temp(i,j,:) = oo_.PosteriorTheoreticalMoments.dsge.VarianceDecompositionME.Mean.(observable_name_requested_vars{i}).(M_.exo_names{j});
end
endo_index_varlist = strmatch(observable_name_requested_vars{i}, var_list_, 'exact');
oo_ = posterior_analysis('conditional decomposition', var_list_{endo_index_varlist}, 'ME', Steps, options_, M_, oo_);
temp(i,j+1,:) = oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecompositionME.Mean.(observable_name_requested_vars{i}).('ME');
oo_ = posterior_analysis('decomposition', var_list_{endo_index_varlist}, 'ME', [], options_, M_, oo_);
temp(i,j+1,:) = oo_.PosteriorTheoreticalMoments.dsge.VarianceDecompositionME.Mean.(observable_name_requested_vars{i}).('ME');
end
title = 'Posterior mean conditional variance decomposition (in percent) with measurement error';
save_name_string = 'dsge_post_mean_var_decomp_ME_cond_h';
title='Posterior mean variance decomposition (in percent) with measurement error';
save_name_string='dsge_post_mean_var_decomp_uncond_ME';
else
for i=1:NumberOfObservedEndogenousVariables
for j=1:NumberOfExogenousVariables
temp(i,j,:) = oo_.PriorTheoreticalMoments.dsge.ConditionalVarianceDecompositionME.Mean.(observable_name_requested_vars{i}).(M_.exo_names{j});
temp(i,j,:) = oo_.PriorTheoreticalMoments.dsge.VarianceDecompositionME.Mean.(observable_name_requested_vars{i}).(M_.exo_names{j});
end
endo_index_varlist = strmatch(observable_name_requested_vars{i}, var_list_, 'exact');
oo_ = prior_analysis('conditional decomposition', var_list_{endo_index_varlist}, 'ME', Steps, options_, M_, oo_);
temp(i,j+1,:) = oo_.PriorTheoreticalMoments.dsge.ConditionalVarianceDecompositionME.Mean.(observable_name_requested_vars{i}).('ME');
oo_ = prior_analysis('decomposition', var_list_{endo_index_varlist}, 'ME', [], options_, M_, oo_);
temp(i,j+1,:) = oo_.PriorTheoreticalMoments.dsge.VarianceDecompositionME.Mean.(observable_name_requested_vars{i}).('ME');
end
title = 'Prior mean conditional variance decomposition (in percent) with measurement error';
save_name_string = 'dsge_prior_mean_var_decomp_ME_cond_h';
title='Prior mean variance decomposition (in percent) with measurement error';
save_name_string='dsge_prior_mean_var_decomp_uncond_ME';
end
for step_iter=1:length(Steps)
title_print = [title, ' Period ' int2str(Steps(step_iter))];
headers = M_.exo_names;
headers(M_.exo_names_orig_ord) = headers;
title=add_filter_subtitle(title, options_);
headers = M_.exo_names;
headers(M_.exo_names_orig_ord) = headers;
headers = vertcat(' ', headers, 'ME');
lh = cellofchararraymaxlength(var_list_)+2;
dyntable(options_, title, headers, observable_name_requested_vars,100*temp,lh,8,2);
if options_.TeX
headers = M_.exo_names_tex;
headers = vertcat(' ', headers, 'ME');
lh = cellofchararraymaxlength(var_list_)+2;
dyntable(options_, title_print, headers, observable_name_requested_vars, 100*temp(:,:,step_iter), lh, 8, 2);
if options_.TeX
headers = M_.exo_names_tex;
labels = var_list_tex(varlist_pos);
lh = cellofchararraymaxlength(labels)+2;
dyn_latex_table(M_, options_, title, save_name_string, headers, labels, 100*temp, lh, 8, 2);
end
skipline();
end
end
% CONDITIONAL VARIANCE DECOMPOSITION.
if Steps
temp = NaN(NumberOfEndogenousVariables, NumberOfExogenousVariables, length(Steps));
if posterior
for i=1:NumberOfEndogenousVariables
for j=1:NumberOfExogenousVariables
oo_ = posterior_analysis('conditional decomposition', var_list_{i}, M_.exo_names{j}, Steps, options_, M_, oo_);
temp(i,j,:) = oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecomposition.Mean.(var_list_{i}).(M_.exo_names{j});
end
end
title = 'Posterior mean conditional variance decomposition (in percent)';
save_name_string = 'dsge_post_mean_var_decomp_cond_h';
else
for i=1:NumberOfEndogenousVariables
for j=1:NumberOfExogenousVariables
oo_ = prior_analysis('conditional decomposition', var_list_{i}, M_.exo_names{j}, Steps, options_, M_, oo_);
temp(i,j,:) = oo_.PriorTheoreticalMoments.dsge.ConditionalVarianceDecomposition.Mean.(var_list_{i}).(M_.exo_names{j});
end
end
title = 'Prior mean conditional variance decomposition (in percent)';
save_name_string = 'dsge_prior_mean_var_decomp_cond_h';
end
for step_iter=1:length(Steps)
title_print=[title, ' Period ' int2str(Steps(step_iter))];
headers = M_.exo_names;
headers(M_.exo_names_orig_ord) = headers;
headers = vertcat(' ', headers);
lh = cellofchararraymaxlength(var_list_)+2;
dyntable(options_,title_print,headers, var_list_,100* ...
temp(:,:,step_iter),lh,8,2);
if options_.TeX
headers = M_.exo_names_tex;
headers = vertcat(' ', headers);
labels = var_list_tex;
lh = cellofchararraymaxlength(labels)+2;
dyn_latex_table(M_, options_, title_print, [save_name_string, int2str(Steps(step_iter))], headers, labels, 100*temp(:,:,step_iter), lh, 8, 2);
end
end
skipline();
if ~all(diag(M_.H)==0)
if ~isempty(observable_name_requested_vars)
NumberOfObservedEndogenousVariables = length(observable_name_requested_vars);
temp=NaN(NumberOfObservedEndogenousVariables,NumberOfExogenousVariables+1,length(Steps));
if posterior
for i=1:NumberOfObservedEndogenousVariables
for j=1:NumberOfExogenousVariables
temp(i,j,:) = oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecompositionME.Mean.(observable_name_requested_vars{i}).(M_.exo_names{j});
end
endo_index_varlist = strmatch(observable_name_requested_vars{i}, var_list_, 'exact');
oo_ = posterior_analysis('conditional decomposition', var_list_{endo_index_varlist}, 'ME', Steps, options_, M_, oo_);
temp(i,j+1,:) = oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecompositionME.Mean.(observable_name_requested_vars{i}).('ME');
end
title = 'Posterior mean conditional variance decomposition (in percent) with measurement error';
save_name_string = 'dsge_post_mean_var_decomp_ME_cond_h';
else
for i=1:NumberOfObservedEndogenousVariables
for j=1:NumberOfExogenousVariables
temp(i,j,:) = oo_.PriorTheoreticalMoments.dsge.ConditionalVarianceDecompositionME.Mean.(observable_name_requested_vars{i}).(M_.exo_names{j});
end
endo_index_varlist = strmatch(observable_name_requested_vars{i}, var_list_, 'exact');
oo_ = prior_analysis('conditional decomposition', var_list_{endo_index_varlist}, 'ME', Steps, options_, M_, oo_);
temp(i,j+1,:) = oo_.PriorTheoreticalMoments.dsge.ConditionalVarianceDecompositionME.Mean.(observable_name_requested_vars{i}).('ME');
end
title = 'Prior mean conditional variance decomposition (in percent) with measurement error';
save_name_string = 'dsge_prior_mean_var_decomp_ME_cond_h';
end
for step_iter=1:length(Steps)
title_print = [title, ' Period ' int2str(Steps(step_iter))];
headers = M_.exo_names;
headers(M_.exo_names_orig_ord) = headers;
headers = vertcat(' ', headers, 'ME');
labels = var_list_tex(varlist_pos);
lh = cellofchararraymaxlength(labels)+2;
dyn_latex_table(M_, options_, title_print, [save_name_string, int2str(Steps(step_iter))], headers, labels, 100*temp(:,:,step_iter), lh, 8, 2);
lh = cellofchararraymaxlength(var_list_)+2;
dyntable(options_, title_print, headers, observable_name_requested_vars, 100*temp(:,:,step_iter), lh, 8, 2);
if options_.TeX
headers = M_.exo_names_tex;
headers = vertcat(' ', headers, 'ME');
labels = var_list_tex(varlist_pos);
lh = cellofchararraymaxlength(labels)+2;
dyn_latex_table(M_, options_, title_print, [save_name_string, int2str(Steps(step_iter))], headers, labels, 100*temp(:,:,step_iter), lh, 8, 2);
end
end
skipline();
end
skipline();
end
end
end
else
fprintf(['Estimation::compute_moments_varendo: (conditional) variance decomposition only available at order=1. Skipping computations\n'])
end
fprintf(' Done!\n');
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