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function oo_ = disp_th_moments(dr, var_list, M_, options_, oo_)
% Display theoretical moments of variables
% Copyright (C) 2001-2018 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 <http://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)
if (isoctave && octave_ver_less_than('6')) || (~isoctave && matlab_ver_less_than('8.1'))
[observable_pos_requested_vars,index_subset,index_observables]=intersect_stable(ivar,options_.varobs_id);
else
[observable_pos_requested_vars,index_subset,index_observables]=intersect(ivar,options_.varobs_id,'stable');
end
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 = M_.endo_names(ivar);
lh = cellofchararraymaxlength(labels)+2;
dyntable(options_, title, headers, labels, z, lh, 11, 4);
if options_.TeX
labels = M_.endo_names_tex(ivar);
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);
lh = cellofchararraymaxlength(M_.endo_names(ivar(stationary_vars)))+2;
dyntable(options_, title, headers, M_.endo_names(ivar(stationary_vars)), 100*oo_.gamma_y{options_.ar+2}(stationary_vars,:), lh, 8, 2);
if ME_present
if (isoctave && octave_ver_less_than('6')) || (~isoctave && matlab_ver_less_than('8.1'))
[stationary_observables, pos_index_subset] = intersect_stable(index_subset, stationary_vars);
else
[stationary_observables, pos_index_subset] = intersect(index_subset, stationary_vars, 'stable');
end
headers_ME = vertcat(headers, 'ME');
dyntable(options_, [title,' WITH MEASUREMENT ERROR'], headers_ME, M_.endo_names(ivar(stationary_observables)), ...
oo_.variance_decomposition_ME(pos_index_subset,:), lh, 8, 2);
end
if options_.TeX
headers = M_.exo_names_tex;
headers = vertcat(' ', headers);
labels = M_.endo_names_tex(ivar(stationary_vars));
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');
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 length(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 length(i1) == 0
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 = M_.endo_names(ivar(i1));
headers = vertcat('Variables', labels);
lh = cellofchararraymaxlength(labels)+2;
dyntable(options_, title, headers, labels, corr(i1,i1), lh, 8, 4);
if options_.TeX
labels = M_.endo_names_tex(ivar(i1));
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=[];
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 = M_.endo_names(ivar(i1));
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 = M_.endo_names_tex(ivar(i1));
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
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