164 lines
7.7 KiB
Matlab
164 lines
7.7 KiB
Matlab
function oo_=disp_th_moments_pruned_state_space(dr,M_,options_,i_var,oo_)
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% oo_=disp_th_moments_pruned_state_space(dr,M_,options_,i_var,oo_)
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% Display theoretical moments of variables based on (second or third order)
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% pruned state-space
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%
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% INPUTS:
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% dr : [struct] Dynare decision rules structure
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% M_ [struct] structure describing the Model
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% options_ [struct] structure describing the options
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% i_var [double] Index of requested variables in declaration order
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% oo_ [struct] structure describing the Model
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%
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% OUTPUTS:
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% gamma_y [cell] Matlab cell of nar+1 arrays, where nar is the order of the autocorrelation function.
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% gamma_y{1} [double] Covariance matrix.
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% gamma_y{i+1} [double] Autocorrelation function (for i=1,...,options_.ar).
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% mean [vector] Unconditional mean
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% var [matrix] Unconditional covariance matrix
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% autocorr [cell] Cell storing the theoretical autocorrelation
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% contemporaneous_correlation [matrix] matrix of contemporaneous correlations
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% autocorr [cell] Cell storing the theoretical autocorrelation
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% variance_decomposition [matrix] Unconditional variance decomposition matrix
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% variance_decomposition_ME [matrix] Unconditional variance decomposition matrix with measurement error
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% conditional_variance_decomposition [array] Conditional variance decomposition array
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% conditional_variance_decomposition_ME [array] Conditional variance decomposition array with measurement error
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% Copyright © 2020-2023 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 <https://www.gnu.org/licenses/>.
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if options_.one_sided_hp_filter || options_.hp_filter || options_.bandpass.indicator
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error(['disp_th_moments:: theoretical moments incompatible with filtering. Use simulated moments instead'])
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end
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nvars=length(i_var);
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obs_var=NaN(nvars,1);
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for i=1:nvars
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obs_var(i,1) = find(strcmp(M_.endo_names(i_var(i),:), M_.endo_names(dr.order_var)));
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end
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pruned_state_space = pruned_SS.pruned_state_space_system(M_, options_, dr, obs_var, options_.ar, 1, 0);
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m = pruned_state_space.E_y;
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oo_.gamma_y{1} = pruned_state_space.Var_y;
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i1 = find(abs(diag(oo_.gamma_y{1})) > 1e-12);
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s2 = diag(oo_.gamma_y{1});
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sd = sqrt(s2);
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z = [ m sd s2 ];
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oo_.mean = m;
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oo_.var = oo_.gamma_y{1};
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if ~options_.noprint %options_.nomoments == 0
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title='THEORETICAL MOMENTS BASED ON PRUNED STATE SPACE';
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headers={'VARIABLE','MEAN','STD. DEV.','VARIANCE'};
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labels = M_.endo_names(i_var,:);
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lh = cellofchararraymaxlength(labels)+2;
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dyntable(options_,title,headers,labels,z,lh,11,4);
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if options_.TeX
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labels = M_.endo_names_tex(i_var,:);
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lh = cellofchararraymaxlength(labels)+2;
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dyn_latex_table(M_,options_,title,'th_moments',headers,labels,z,lh,11,4);
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end
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end
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if isempty(i1)
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disp_verbose(' ',~options_.noprint)
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disp_verbose('All endogenous are constant or non stationary, not displaying correlations and auto-correlations',~options_.noprint)
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disp_verbose(' ',~options_.noprint)
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return;
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end
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if options_.nocorr == 0 % && size(stationary_vars, 1) > 0
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corr=pruned_state_space.Corr_y;
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if options_.contemporaneous_correlation
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oo_.contemporaneous_correlation = corr;
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end
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if ~options_.noprint
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skipline()
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title='MATRIX OF CORRELATIONS BASED ON PRUNED STATE SPACE';
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labels = M_.endo_names(i_var,:);
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headers = ['Variables';labels];
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lh = cellofchararraymaxlength(labels)+2;
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dyntable(options_,title,headers,labels,corr,lh,8,4);
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if options_.TeX
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labels = M_.endo_names_tex(i_var,:);
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headers=['Variables';labels];
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lh = cellofchararraymaxlength(labels)+2;
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dyn_latex_table(M_,options_,title,'th_corr_matrix',headers,labels,corr,lh,8,4);
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end
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end
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end
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if options_.ar > 0 %&& size(stationary_vars, 1) > 0
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z=NaN(length(i1),options_.ar);
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for i=1:options_.ar
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oo_.gamma_y{i+1} = pruned_state_space.Corr_yi(:,:,i);
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oo_.autocorr{i} = oo_.gamma_y{i+1};
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z(:,i) = diag(oo_.gamma_y{i+1}(i1,i1));
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end
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if ~options_.noprint
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skipline()
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title='COEFFICIENTS OF AUTOCORRELATION BASED ON PRUNED STATE SPACE';
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labels = M_.endo_names(i_var(i1),:);
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headers = ['Order ';cellstr(int2str([1:options_.ar]'))];
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lh = cellofchararraymaxlength(labels)+2;
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dyntable(options_,title,headers,labels,z,lh,8,4);
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if options_.TeX
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labels = M_.endo_names_tex(i_var(i1),:);
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lh = cellofchararraymaxlength(labels)+2;
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dyn_latex_table(M_,options_,title,'th_autocorr_matrix',headers,labels,z,lh,8,4);
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end
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end
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end
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if options_.order==2 && ~options_.nodecomposition && M_.exo_nbr > 1% do variance decomposition
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index_stationary_vars = dr.inv_order_var(i_var); %no nonstationary variables in pruning
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ghu_states_only = zeros(M_.nspred,M_.exo_nbr);
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ghu_states_only(1:M_.nspred,:) = dr.ghu(M_.nstatic+(1:M_.nspred),:); %get shock impact on states only
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[A] = kalman_transition_matrix(dr,M_.nstatic+(1:M_.nspred)',1:M_.nspred);
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oo_.gamma_y{options_.ar+2}=compute_variance_decomposition(M_,options_,s2,A,dr.ghx(index_stationary_vars,:),dr.ghu,ghu_states_only,1:length(i_var),index_stationary_vars,nvars);
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[ME_present,observable_pos_requested_vars,index_subset,index_observables]=check_measurement_error_requested_vars(M_,options_,i_var);
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%store unconditional variance decomposition
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oo_.variance_decomposition=100*oo_.gamma_y{options_.ar+2};
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if ME_present
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ME_Variance=diag(M_.H);
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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);
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oo_.variance_decomposition_ME(:,end+1)=100-sum(oo_.variance_decomposition_ME,2);
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end
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if ~options_.noprint %options_.nomoments == 0
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display_unconditional_variance_decomposition(M_,options_,oo_,i_var,1:length(i_var),index_subset,ME_present)
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end
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%% Conditional variance decomposition
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conditional_variance_steps = options_.conditional_variance_decomposition;
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if ~isempty(conditional_variance_steps)
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[oo_.conditional_variance_decomposition, oo_.conditional_variance_decomposition_ME] = ...
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conditional_variance_decomposition(M_,options_,dr, conditional_variance_steps, i_var);
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if ~options_.noprint
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display_conditional_variance_decomposition(oo_.conditional_variance_decomposition, conditional_variance_steps, i_var, M_, options_);
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if ME_present
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display_conditional_variance_decomposition(oo_.conditional_variance_decomposition_ME, conditional_variance_steps, ...
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observable_pos_requested_vars, M_, options_);
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end
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end
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end
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end |