function oo_ = compute_moments_varendo(type, options_, M_, oo_, estim_params_, var_list_)
% oo_ = compute_moments_varendo(type, options_, M_, oo_, estim_params_, var_list_)
% Computes the second order moments (autocorrelation function, covariance
% matrix and variance decomposition) distributions for all the endogenous variables selected in
% var_list_. The results are saved in oo_
%
% INPUTS:
%   type            [string]                    'posterior' or 'prior'
%   options_        [structure]                 Dynare structure.
%   M_              [structure]                 Dynare structure (related to model definition).
%   oo_             [structure]                 Dynare structure (results).
%   var_list_       [cell of char arrays]       Endogenous variable names.
%
% OUTPUTS
%   oo_             [structure]    Dynare structure (results).
%
% SPECIAL REQUIREMENTS
%   none

% Copyright © 2008-2025 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare.  If not, see <https://www.gnu.org/licenses/>.


fprintf('Estimation::compute_moments_varendo: I''m computing endogenous moments (this may take a while)... \n');

if options_.order==1
    if options_.one_sided_hp_filter
        fprintf('Estimation::compute_moments_varendo: theoretical moments incompatible with one-sided HP filter. Skipping computations.\n')
        return
    end
else
    if ~options_.pruning
        fprintf('Estimation::compute_moments_varendo: theoretical moments at order>1 require pruning. Skipping computations.\n')
        return
    else
        if options_.one_sided_hp_filter || options_.hp_filter || options_.bandpass.indicator
            fprintf('Estimation::compute_moments_varendo: theoretical pruned moments incompatible with filtering. Skipping computations\n')
        end
    end
end

if strcmpi(type,'posterior')
    posterior = 1;
    if nargin==5
        var_list_ = options_.varobs;
    end
    if isfield(oo_,'PosteriorTheoreticalMoments')
        oo_=rmfield(oo_,'PosteriorTheoreticalMoments');
    end
elseif strcmpi(type,'prior')
    posterior = 0;
    if nargin==5
        var_list_ = options_.varobs;
    end
    if isfield(oo_,'PriorTheoreticalMoments')
        oo_=rmfield(oo_,'PriorTheoreticalMoments');
    end
else
    error('compute_moments_varendo:: Unknown type!')
end

NumberOfEndogenousVariables = length(var_list_);
NumberOfExogenousVariables = M_.exo_nbr;
NumberOfLags = options_.ar;
NoDecomposition = options_.nodecomposition;
if isfield(options_,'conditional_variance_decomposition')
    Steps = options_.conditional_variance_decomposition;
else
    Steps = 0;
end

if options_.TeX
    var_list_tex={};
    for var_iter = 1:length(var_list_)
        var_list_tex = vertcat(var_list_tex, M_.endo_names_tex{strmatch(var_list_{var_iter}, M_.endo_names, 'exact')});
    end
end

% COVARIANCE MATRIX.
[hh_fig, length_of_old_string] = wait_bar.run(0, [], 'Endogenous moments: variance/covariance', options_.console_mode, 0, 'Endogenous moments: variance/covariance.');
if posterior
    for i=1:NumberOfEndogenousVariables
        for j=i:NumberOfEndogenousVariables
            oo_ = posterior_analysis('variance', var_list_{i}, var_list_{j}, NumberOfLags, options_, M_, oo_, estim_params_);
            [hh_fig, length_of_old_string] = wait_bar.run(((i-1)*NumberOfEndogenousVariables+j)/(NumberOfEndogenousVariables^2), hh_fig, 'Endogenous moments: variance/covariance', options_.console_mode, length_of_old_string);
        end
    end
else
    for i=1:NumberOfEndogenousVariables
        for j=i:NumberOfEndogenousVariables
            oo_ = prior_analysis('variance', var_list_{i}, var_list_{j}, NumberOfLags, options_, M_, oo_, estim_params_);
            [hh_fig, length_of_old_string] = wait_bar.run(((i-1)*NumberOfEndogenousVariables+j)/(NumberOfEndogenousVariables^2), hh_fig, 'Endogenous moments: variance/covariance', options_.console_mode, length_of_old_string);
        end
    end
end
wait_bar.close(hh_fig,options_.console_mode)

% CORRELATION FUNCTION.
if posterior
    oo_ = posterior_analysis('correlation', var_list_, NumberOfEndogenousVariables, NumberOfLags, options_, M_, oo_, estim_params_);
else
    oo_ = prior_analysis('correlation', var_list_, NumberOfEndogenousVariables, NumberOfLags, options_, M_, oo_, estim_params_);
end


% VARIANCE DECOMPOSITION.
if options_.order==1
    if M_.exo_nbr > 1
        if ~NoDecomposition
            [hh_fig, length_of_old_string] = wait_bar.run(0, [], 'Endogenous moments: variance decomposition', options_.console_mode, 0, 'Endogenous moments: variance decomposition.');
            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_, estim_params_);
                        temp(i,j) = oo_.PosteriorTheoreticalMoments.dsge.VarianceDecomposition.Mean.(var_list_{i}).(M_.exo_names{j});
                        [hh_fig, length_of_old_string] = wait_bar.run(((i-1)*NumberOfExogenousVariables+j)/(NumberOfEndogenousVariables*NumberOfExogenousVariables),...
                            hh_fig, 'Endogenous moments: variance decomposition', options_.console_mode, length_of_old_string);
                    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_, estim_params_);
                        temp(i,j)=oo_.PriorTheoreticalMoments.dsge.VarianceDecomposition.Mean.(var_list_{i}).(M_.exo_names{j});
                        [hh_fig, length_of_old_string] = wait_bar.run(((i-1)*NumberOfExogenousVariables+j)/(NumberOfEndogenousVariables*NumberOfExogenousVariables), hh_fig, 'Endogenous moments: variance decomposition', options_.console_mode, length_of_old_string);
                    end
                end
                title='Prior mean variance decomposition (in percent)';
                save_name_string='dsge_prior_mean_var_decomp_uncond';
            end
            wait_bar.close(hh_fig,options_.console_mode)
            title=add_filter_subtitle(title, options_);
            headers = M_.exo_names;
            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
            if ~options_.noprint
                skipline();
            end
            if ~options_.noprint
                skipline();
            end
            if ~all(diag(M_.H)==0)
                [observable_name_requested_vars, varlist_pos] = intersect(var_list_, options_.varobs, 'stable');
                if ~isempty(observable_name_requested_vars)
                    [hh_fig, length_of_old_string] = wait_bar.run(0, [], 'Endogenous moments: variance decomposition with measurement error.', options_.console_mode, 0, 'Endogenous moments: variance decomposition with measurement error.');
                    NumberOfObservedEndogenousVariables = length(observable_name_requested_vars);
                    temp = NaN(NumberOfObservedEndogenousVariables, NumberOfExogenousVariables+1);
                    if posterior
                        for i=1:NumberOfObservedEndogenousVariables
                            for j=1:NumberOfExogenousVariables
                                temp(i,j,:) = oo_.PosteriorTheoreticalMoments.dsge.VarianceDecompositionME.Mean.(observable_name_requested_vars{i}).(M_.exo_names{j});
                                [hh_fig, length_of_old_string] = wait_bar.run(((i-1)*NumberOfExogenousVariables+j)/(NumberOfObservedEndogenousVariables*NumberOfExogenousVariables), hh_fig, 'Endogenous moments: variance decomposition with measurement error', options_.console_mode, length_of_old_string);
                            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_, estim_params_);
                            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';
                    else
                        for i=1:NumberOfObservedEndogenousVariables
                            for j=1:NumberOfExogenousVariables
                                temp(i,j,:) = oo_.PriorTheoreticalMoments.dsge.VarianceDecompositionME.Mean.(observable_name_requested_vars{i}).(M_.exo_names{j});
                                [hh_fig, length_of_old_string] = wait_bar.run(((i-1)*NumberOfExogenousVariables+j)/(NumberOfObservedEndogenousVariables*NumberOfExogenousVariables), hh_fig, 'Endogenous moments: variance decomposition with measurement error', options_.console_mode, length_of_old_string);
                            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';
                    end
                    wait_bar.close(hh_fig,options_.console_mode)
                    title=add_filter_subtitle(title, options_);
                    headers = M_.exo_names;
                    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
        end
        % CONDITIONAL VARIANCE DECOMPOSITION.
        if Steps
            [hh_fig, length_of_old_string] = wait_bar.run(0, [], 'Endogenous moments: conditional variance decomposition.', options_.console_mode, 0, 'Endogenous moments: variance decomposition with measurement error.');
            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_, estim_params_);
                        temp(i,j,:) = oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecomposition.Mean.(var_list_{i}).(M_.exo_names{j});
                        [hh_fig, length_of_old_string] = wait_bar.run(((i-1)*NumberOfExogenousVariables+j)/(NumberOfEndogenousVariables*NumberOfExogenousVariables), hh_fig,...
                            'Endogenous moments: conditional variance decomposition', options_.console_mode, length_of_old_string);
                    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});
                        [hh_fig, length_of_old_string] = wait_bar.run(((i-1)*NumberOfExogenousVariables+j)/(NumberOfEndogenousVariables*NumberOfExogenousVariables), hh_fig,...
                            'Endogenous moments: conditional variance decomposition', options_.console_mode, length_of_old_string);
                    end
                end
                title = 'Prior mean conditional variance decomposition (in percent)';
                save_name_string = 'dsge_prior_mean_var_decomp_cond_h';
            end
            wait_bar.close(hh_fig,options_.console_mode)
            for step_iter=1:length(Steps)
                title_print=[title, ' Period ' int2str(Steps(step_iter))];
                headers = M_.exo_names;
                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)
                    [hh_fig, length_of_old_string] = wait_bar.run(0, [], 'Endogenous moments: conditional variance decomposition with measurement error.', options_.console_mode, 0, 'Endogenous moments: conditional variance decomposition with measurement error.');
                    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});
                                [hh_fig, length_of_old_string] = wait_bar.run(((i-1)*NumberOfExogenousVariables+j)/(NumberOfObservedEndogenousVariables*NumberOfExogenousVariables), hh_fig,...
                                    'Endogenous moments: conditional variance decomposition with measurement error', options_.console_mode, length_of_old_string);
                            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_, estim_params_);
                            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});
                                [hh_fig, length_of_old_string] = wait_bar.run(((i-1)*NumberOfExogenousVariables+j)/(NumberOfObservedEndogenousVariables*NumberOfExogenousVariables), hh_fig,...
                                    'Endogenous moments: conditional variance decomposition with measurement error', options_.console_mode, length_of_old_string);
                            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
                    wait_bar.close(hh_fig,options_.console_mode)
                    for step_iter=1:length(Steps)
                        title_print = [title, ' Period ' int2str(Steps(step_iter))];
                        headers = M_.exo_names;
                        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;
                            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
            end
        end
    end
else
    fprintf('Estimation::compute_moments_varendo: (conditional) variance decomposition only available at order=1. Skipping computations\n')
end
fprintf('Done!\n\n');
