304 lines
15 KiB
Matlab
304 lines
15 KiB
Matlab
function oo_ = compute_moments_varendo(type, options_, M_, oo_, var_list_)
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% Computes the second order moments (autocorrelation function, covariance
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% matrix and variance decomposition) distributions for all the endogenous variables selected in
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% var_list_. The results are saved in oo_
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%
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% INPUTS:
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% type [string] 'posterior' or 'prior'
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% options_ [structure] Dynare structure.
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% M_ [structure] Dynare structure (related to model definition).
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% oo_ [structure] Dynare structure (results).
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% var_list_ [cell of char arrays] Endogenous variable names.
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%
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% OUTPUTS
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% oo_ [structure] Dynare structure (results).
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%
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% SPECIAL REQUIREMENTS
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% none
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% Copyright (C) 2008-2021 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 <http://www.gnu.org/licenses/>.
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fprintf('Estimation::compute_moments_varendo: I''m computing endogenous moments (this may take a while)... \n');
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if options_.order==1
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if options_.one_sided_hp_filter
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fprintf('Estimation::compute_moments_varendo: theoretical moments incompatible with one-sided HP filter. Skipping computations.\n')
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return
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end
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else
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if ~options_.pruning
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fprintf('Estimation::compute_moments_varendo: theoretical moments at order>1 require pruning. Skipping computations.\n')
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return
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else
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if options_.one_sided_hp_filter || options_.hp_filter || options_.bandpass.indicator
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fprintf(['Estimation::compute_moments_varendo: theoretical pruned moments incompatible with filtering. Skipping computations\n'])
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end
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end
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end
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if strcmpi(type,'posterior')
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posterior = 1;
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if nargin==4
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var_list_ = options_.varobs;
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end
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if isfield(oo_,'PosteriorTheoreticalMoments')
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oo_=rmfield(oo_,'PosteriorTheoreticalMoments');
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end
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elseif strcmpi(type,'prior')
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posterior = 0;
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if nargin==4
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var_list_ = options_.prior_analysis_endo_var_list;
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if isempty(var_list_)
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options_.prior_analysis_var_list = options_.varobs;
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end
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end
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if isfield(oo_,'PriorTheoreticalMoments')
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oo_=rmfield(oo_,'PriorTheoreticalMoments');
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end
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else
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error('compute_moments_varendo:: Unknown type!')
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end
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NumberOfEndogenousVariables = length(var_list_);
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NumberOfExogenousVariables = M_.exo_nbr;
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NumberOfLags = options_.ar;
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NoDecomposition = options_.nodecomposition;
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if isfield(options_,'conditional_variance_decomposition')
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Steps = options_.conditional_variance_decomposition;
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else
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Steps = 0;
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end
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if options_.TeX
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var_list_tex={};
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for var_iter = 1:length(var_list_)
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var_list_tex = vertcat(var_list_tex, M_.endo_names_tex{strmatch(var_list_{var_iter}, M_.endo_names, 'exact')});
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end
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end
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% COVARIANCE MATRIX.
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if posterior
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for i=1:NumberOfEndogenousVariables
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for j=i:NumberOfEndogenousVariables
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oo_ = posterior_analysis('variance', var_list_{i}, var_list_{j}, [], options_, M_, oo_);
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end
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end
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else
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for i=1:NumberOfEndogenousVariables
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for j=i:NumberOfEndogenousVariables
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oo_ = prior_analysis('variance', var_list_{i}, var_list_{j}, [], options_, M_, oo_);
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end
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end
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end
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% CORRELATION FUNCTION.
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if posterior
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for h=NumberOfLags:-1:1
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for i=1:NumberOfEndogenousVariables
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for j=1:NumberOfEndogenousVariables
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oo_ = posterior_analysis('correlation', var_list_{i}, var_list_{j}, h, options_, M_, oo_);
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end
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end
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end
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else
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for h=NumberOfLags:-1:1
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for i=1:NumberOfEndogenousVariables
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for j=1:NumberOfEndogenousVariables
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oo_ = prior_analysis('correlation', var_list_{i}, var_list_{j}, h, options_, M_, oo_);
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end
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end
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end
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end
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% VARIANCE DECOMPOSITION.
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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: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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[observable_name_requested_vars, varlist_pos] = intersect(var_list_, options_.varobs, 'stable');
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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 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.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('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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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.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('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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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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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\n');
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