function oo_=disp_th_moments(dr,var_list,M_,options_,oo_) % Display theoretical moments of variables % Copyright (C) 2001-2017 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 . 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 size(var_list,1) == 0 var_list = M_.endo_names(1:M_.orig_endo_nbr, :); end nvar = size(var_list,1); 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}; if size(stationary_vars, 1) > 0 if ~nodecomposition oo_.variance_decomposition=100*oo_.gamma_y{options_.ar+2}; 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=char('VARIABLE','MEAN','STD. DEV.','VARIANCE'); labels = deblank(M_.endo_names(ivar,:)); lh = size(labels,2)+2; dyntable(options_,title,headers,labels,z,lh,11,4); if options_.TeX labels = deblank(M_.endo_names_tex(ivar,:)); lh = size(labels,2)+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 = char(' ',headers); lh = size(deblank(M_.endo_names(ivar(stationary_vars),:)),2)+2; dyntable(options_,title,headers,deblank(M_.endo_names(ivar(stationary_vars), ... :)),100* ... oo_.gamma_y{options_.ar+2}(stationary_vars,:),lh,8,2); if options_.TeX headers=M_.exo_names_tex; headers = char(' ',headers); labels = deblank(M_.endo_names_tex(ivar(stationary_vars),:)); lh = size(labels,2)+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); 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; oo_.conditional_variance_decomposition = conditional_variance_decomposition(StateSpaceModel,conditional_variance_steps,ivar); if options_.noprint == 0 display_conditional_variance_decomposition(oo_.conditional_variance_decomposition,conditional_variance_steps,... ivar,M_,options_); 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 == 0 && 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 = deblank(M_.endo_names(ivar(i1),:)); headers = char('Variables',labels); lh = size(labels,2)+2; dyntable(options_,title,headers,labels,corr(i1,i1),lh,8,4); if options_.TeX labels = deblank(M_.endo_names_tex(ivar(i1),:)); headers=char('Variables',labels); lh = size(labels,2)+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 = deblank(M_.endo_names(ivar(i1),:)); headers = char('Order ',int2str([1:options_.ar]')); lh = size(labels,2)+2; dyntable(options_,title,headers,labels,z,lh,8,4); if options_.TeX labels = deblank(M_.endo_names_tex(ivar(i1),:)); headers=char('Order ',int2str([1:options_.ar]')); lh = size(labels,2)+2; dyn_latex_table(M_,options_,title,'th_autocorr_matrix',headers,labels,z,lh,8,4); end end end