function oo_ = disp_th_moments(dr, var_list, M_, options_, oo_)

% Display theoretical moments of variables

% Copyright (C) 2001-2021 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/>.

nodecomposition = options_.nodecomposition;
if options_.one_sided_hp_filter
    error('disp_th_moments:: theoretical moments incompatible with one-sided HP filter. Use simulated moments instead')
end
if isempty(var_list)
    var_list = M_.endo_names(1:M_.orig_endo_nbr);
end
nvar = length(var_list);
ivar=zeros(nvar,1);
for i=1:nvar
    i_tmp = strmatch(var_list{i}, M_.endo_names, 'exact');
    if isempty(i_tmp)
        error ('One of the variable specified does not exist');
    else
        ivar(i) = i_tmp;
    end
end

[oo_.gamma_y,stationary_vars] = th_autocovariances(dr, ivar, M_, options_, nodecomposition);
m = dr.ys(ivar);
non_stationary_vars = setdiff(1:length(ivar),stationary_vars);
m(non_stationary_vars) = NaN;

i1 = find(abs(diag(oo_.gamma_y{1})) > 1e-12);
s2 = diag(oo_.gamma_y{1});
sd = sqrt(s2);
if options_.order == 2 && ~M_.hessian_eq_zero
    m = m+oo_.gamma_y{options_.ar+3};
end

z = [ m sd s2 ];
oo_.mean = m;
oo_.var = oo_.gamma_y{1};

ME_present=0;
if ~all(diag(M_.H)==0)
    [observable_pos_requested_vars,index_subset,index_observables]=intersect(ivar,options_.varobs_id,'stable');
    if ~isempty(observable_pos_requested_vars)
        ME_present=1;
    end
end

if size(stationary_vars, 1) > 0
    if ~nodecomposition
        oo_.variance_decomposition=100*oo_.gamma_y{options_.ar+2};
        if ME_present
            ME_Variance=diag(M_.H);
            oo_.variance_decomposition_ME=oo_.variance_decomposition(index_subset,:).*repmat(diag(oo_.var(index_subset,index_subset))./(diag(oo_.var(index_subset,index_subset))+ME_Variance(index_observables)),1,M_.exo_nbr);
            oo_.variance_decomposition_ME(:,end+1)=100-sum(oo_.variance_decomposition_ME,2);
        end
    end
    if ~options_.noprint %options_.nomoments == 0
        if options_.order == 2
            title = 'APPROXIMATED THEORETICAL MOMENTS';
        else
            title = 'THEORETICAL MOMENTS';
        end
        title = add_filter_subtitle(title, options_);
        headers = {'VARIABLE';'MEAN';'STD. DEV.';'VARIANCE'};
        labels=get_labels_transformed_vars(M_.endo_names,ivar,options_,false);
        lh = cellofchararraymaxlength(labels)+2;
        dyntable(options_, title, headers, labels, z, lh, 11, 4);
        if options_.TeX
            labels=get_labels_transformed_vars(M_.endo_names_tex,ivar,options_,true);
            lh = cellofchararraymaxlength(labels)+2;
            dyn_latex_table(M_, options_, title, 'th_moments', headers, labels, z, lh, 11, 4);
        end

        if M_.exo_nbr > 1 && ~nodecomposition
            skipline()
            if options_.order == 2
                title = 'APPROXIMATED VARIANCE DECOMPOSITION (in percent)';
            else
                title = 'VARIANCE DECOMPOSITION (in percent)';
            end
            title = add_filter_subtitle(title, options_);
            headers = M_.exo_names;
            headers(M_.exo_names_orig_ord) = headers;
            headers = vertcat(' ', headers);
            labels=get_labels_transformed_vars(M_.endo_names,ivar(stationary_vars),options_,false);
            lh = cellofchararraymaxlength(labels)+2;
            dyntable(options_, title, headers, labels, 100*oo_.gamma_y{options_.ar+2}(stationary_vars,:), lh, 8, 2);
            if ME_present
                [stationary_observables, pos_index_subset] = intersect(index_subset, stationary_vars, 'stable');
                headers_ME = vertcat(headers, 'ME');
                labels=get_labels_transformed_vars(M_.endo_names,ivar(stationary_observables),options_,false);
                dyntable(options_, [title,' WITH MEASUREMENT ERROR'], headers_ME, labels, ...
                         oo_.variance_decomposition_ME(pos_index_subset,:), lh, 8, 2);
            end
            if options_.TeX
                headers = M_.exo_names_tex;
                headers = vertcat(' ', headers);
                labels=get_labels_transformed_vars(M_.endo_names_tex,ivar(stationary_vars),options_,true);
                lh = cellofchararraymaxlength(labels)+2;
                dyn_latex_table(M_, options_, title, 'th_var_decomp_uncond', headers, labels, 100*oo_.gamma_y{options_.ar+2}(stationary_vars,:), lh, 8, 2);
                if ME_present
                    headers_ME = vertcat(headers, 'ME');
                    labels=get_labels_transformed_vars(M_.endo_names_tex,ivar(stationary_observables),options_,true);
                    dyn_latex_table(M_, options_, [title,' WITH MEASUREMENT ERROR'], ...
                                    'th_var_decomp_uncond_ME', headers_ME, labels, oo_.variance_decomposition_ME(pos_index_subset,:), lh, 8, 2);
                end
            end
        end
    end
    conditional_variance_steps = options_.conditional_variance_decomposition;
    if ~isempty(conditional_variance_steps)
        StateSpaceModel.number_of_state_equations = M_.endo_nbr;
        StateSpaceModel.number_of_state_innovations = M_.exo_nbr;
        StateSpaceModel.sigma_e_is_diagonal = M_.sigma_e_is_diagonal;
        [StateSpaceModel.transition_matrix, StateSpaceModel.impulse_matrix] = ...
            kalman_transition_matrix(dr,(1:M_.endo_nbr)',M_.nstatic+(1:M_.nspred)',M_.exo_nbr);
        StateSpaceModel.state_innovations_covariance_matrix = M_.Sigma_e;
        StateSpaceModel.order_var = dr.order_var;
        StateSpaceModel.measurement_error = M_.H;
        StateSpaceModel.observable_pos = options_.varobs_id;
        [oo_.conditional_variance_decomposition, oo_.conditional_variance_decomposition_ME] = ...
            conditional_variance_decomposition(StateSpaceModel, conditional_variance_steps, ivar);
        if ~options_.noprint
            display_conditional_variance_decomposition(oo_.conditional_variance_decomposition, conditional_variance_steps, ivar, M_, options_);
            if ME_present
                display_conditional_variance_decomposition(oo_.conditional_variance_decomposition_ME, conditional_variance_steps, ...
                                                           observable_pos_requested_vars, M_, options_);
            end
        end
    end
end

if isempty(i1)
    skipline()
    disp('All endogenous are constant or non stationary, not displaying correlations and auto-correlations')
    skipline()
    return
end

if ~options_.nocorr && size(stationary_vars, 1)>0
    corr = NaN(size(oo_.gamma_y{1}));
    corr(i1,i1) = oo_.gamma_y{1}(i1,i1)./(sd(i1)*sd(i1)');
    if options_.contemporaneous_correlation
        oo_.contemporaneous_correlation = corr;
    end
    if ~options_.noprint
        skipline()
        if options_.order==2
            title = 'APPROXIMATED MATRIX OF CORRELATIONS';
        else
            title = 'MATRIX OF CORRELATIONS';
        end
        title = add_filter_subtitle(title, options_);
        labels=get_labels_transformed_vars(M_.endo_names,ivar(i1),options_,false);
        headers = vertcat('Variables', labels);
        lh = cellofchararraymaxlength(labels)+2;
        dyntable(options_, title, headers, labels, corr(i1,i1), lh, 8, 4);
        if options_.TeX
            labels=get_labels_transformed_vars(M_.endo_names_tex,ivar(i1),options_,true);
            headers = vertcat('Variables', labels);
            lh = cellofchararraymaxlength(labels)+2;
            dyn_latex_table(M_, options_, title, 'th_corr_matrix', headers, labels, corr(i1,i1), lh, 8, 4);
        end
    end
end

if options_.ar > 0 && size(stationary_vars, 1) > 0
    z=NaN(length(i1),options_.ar);
    for i=1:options_.ar
        oo_.autocorr{i} = oo_.gamma_y{i+1};
        z(:,i) = diag(oo_.gamma_y{i+1}(i1,i1));
    end
    if ~options_.noprint
        skipline()
        if options_.order == 2
            title = 'APPROXIMATED COEFFICIENTS OF AUTOCORRELATION';
        else
            title = 'COEFFICIENTS OF AUTOCORRELATION';
        end
        title = add_filter_subtitle(title, options_);
            labels=get_labels_transformed_vars(M_.endo_names,ivar(i1),options_,false);
        headers = vertcat('Order ', cellstr(int2str([1:options_.ar]')));
        lh = cellofchararraymaxlength(labels)+2;
        dyntable(options_, title, headers, labels, z, lh, 8, 4);
        if options_.TeX
            labels=get_labels_transformed_vars(M_.endo_names_tex,ivar(i1),options_,true);
            headers = vertcat('Order ', cellstr(int2str([1:options_.ar]')));
            lh = cellofchararraymaxlength(labels)+2;
            dyn_latex_table(M_, options_, title, 'th_autocorr_matrix', headers, labels, z, lh, 8, 4);
        end
    end
end
