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dynare/matlab/disp_moments.m

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function oo_=disp_moments(y,var_list,M_,options_,oo_)
% function disp_moments(y,var_list,M_,options_,oo_)
% Displays moments of simulated variables
% INPUTS
% y [double] nvar*nperiods vector of simulated variables.
% var_list [char] nvar character array with names of variables.
% M_ [structure] Dynare's model structure
% oo_ [structure] Dynare's results structure
% options_ [structure] Dynare's options structure
%
% OUTPUTS
% oo_ [structure] Dynare's results structure,
% 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/>.
warning_old_state = warning;
warning off
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
y = y(ivar,options_.drop+1:end)';
ME_present=0;
if ~all(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;
i_ME = setdiff([1:size(M_.H,1)],find(diag(M_.H) == 0)); % find ME with 0 variance
chol_S = chol(M_.H(i_ME,i_ME)); %decompose rest
shock_mat=zeros(options_.periods,size(M_.H,1)); %initialize
shock_mat(:,i_ME)=randn(length(i_ME),options_.periods)'*chol_S;
y_ME = y(:,index_subset)+shock_mat(options_.drop+1:end,index_observables);
y_ME_only = shock_mat(options_.drop+1:end,index_observables);
m_ME = mean(y_ME);
y_ME=get_filtered_time_series(y_ME,m_ME,options_);
y_ME_only_filtered=get_filtered_time_series(y_ME_only,mean(y_ME_only),options_);
s2_ME = mean(y_ME.*y_ME);
end
end
m = mean(y);
% filter series
y=get_filtered_time_series(y,m,options_);
s2 = mean(y.*y);
s = sqrt(s2);
oo_.mean = transpose(m);
oo_.var = y'*y/size(y,1);
oo_.skewness = (mean(y.^3)./s2.^1.5)';
oo_.kurtosis = (mean(y.^4)./(s2.*s2)-3)';
labels = M_.endo_names(ivar);
labels_TeX = M_.endo_names_tex(ivar);
if options_.nomoments == 0
z = [ m' s' s2' (mean(y.^3)./s2.^1.5)' (mean(y.^4)./(s2.*s2)-3)' ];
title='MOMENTS OF SIMULATED VARIABLES';
title=add_filter_subtitle(title, options_);
headers = {'VARIABLE'; 'MEAN'; 'STD. DEV.'; 'VARIANCE'; 'SKEWNESS'; 'KURTOSIS'};
dyntable(options_, title, headers, labels, z, cellofchararraymaxlength(labels)+2, 16, 6);
if options_.TeX
dyn_latex_table(M_, options_, title, 'sim_moments', headers, labels_TeX, z, cellofchararraymaxlength(labels)+2, 16, 6);
end
end
if options_.nocorr == 0
corr = (y'*y/size(y,1))./(s'*s);
if options_.contemporaneous_correlation
oo_.contemporaneous_correlation = corr;
end
if options_.noprint == 0
title = 'CORRELATION OF SIMULATED VARIABLES';
title=add_filter_subtitle(title,options_);
headers = vertcat('VARIABLE', M_.endo_names(ivar));
dyntable(options_, title, headers, labels, corr, cellofchararraymaxlength(labels)+2, 8, 4);
if options_.TeX
headers = vertcat('VARIABLE', M_.endo_names_tex(ivar));
lh = cellofchararraymaxlength(labels)+2;
dyn_latex_table(M_, options_, title, 'sim_corr_matrix', headers, labels_TeX, corr, lh, 8,4);
end
end
end
if options_.noprint == 0 && length(options_.conditional_variance_decomposition)
fprintf('\nSTOCH_SIMUL: conditional_variance_decomposition requires theoretical moments, i.e. periods=0.\n')
end
ar = options_.ar;
if ar > 0
autocorr = [];
for i=1:ar
oo_.autocorr{i} = y(ar+1:end,:)'*y(ar+1-i:end-i,:)./((size(y,1)-ar)*std(y(ar+1:end,:))'*std(y(ar+1-i:end-i,:)));
autocorr = [ autocorr diag(oo_.autocorr{i}) ];
end
if options_.noprint == 0
title = 'AUTOCORRELATION OF SIMULATED VARIABLES';
title=add_filter_subtitle(title,options_);
headers = vertcat('VARIABLE', cellstr(int2str([1:ar]')));
dyntable(options_, title, headers, labels, autocorr, cellofchararraymaxlength(labels)+2, 8, 4);
if options_.TeX
headers = vertcat('VARIABLE', cellstr(int2str([1:ar]')));
lh = cellofchararraymaxlength(labels)+2;
dyn_latex_table(M_, options_, title, 'sim_autocorr_matrix', headers, labels_TeX, autocorr, cellofchararraymaxlength(labels_TeX)+2, 8, 4);
end
end
end
if ~options_.nodecomposition
if M_.exo_nbr == 1
oo_.variance_decomposition = 100*ones(nvar,1);
else
oo_.variance_decomposition=zeros(nvar,M_.exo_nbr);
%get starting values
if isempty(M_.endo_histval)
y0 = oo_.dr.ys;
else
if options_.loglinear
y0 = log_variable(1:M_.endo_nbr,M_.endo_histval,M_);
else
y0 = M_.endo_histval;
end
end
%back out shock matrix used for generating y
i_exo_var = setdiff([1:M_.exo_nbr],find(diag(M_.Sigma_e) == 0)); % find shocks with 0 variance
chol_S = chol(M_.Sigma_e(i_exo_var,i_exo_var)); %decompose rest
shock_mat=zeros(options_.periods,M_.exo_nbr); %initialize
shock_mat(:,i_exo_var)=oo_.exo_simul(:,i_exo_var)/chol_S; %invert construction of oo_.exo_simul from simult.m
for shock_iter=1:length(i_exo_var)
temp_shock_mat=zeros(size(shock_mat));
temp_shock_mat(:,i_exo_var(shock_iter))=shock_mat(:,i_exo_var(shock_iter));
temp_shock_mat(:,i_exo_var) = temp_shock_mat(:,i_exo_var)*chol_S;
y_sim_one_shock = simult_(y0,oo_.dr,temp_shock_mat,options_.order);
y_sim_one_shock=y_sim_one_shock(ivar,1+options_.drop+1:end)';
y_sim_one_shock=get_filtered_time_series(y_sim_one_shock,mean(y_sim_one_shock),options_);
oo_.variance_decomposition(:,i_exo_var(shock_iter))=var(y_sim_one_shock)./s2*100;
end
if ME_present
oo_.variance_decomposition_ME=oo_.variance_decomposition(index_subset,:)...
.*repmat((s2(index_subset)./s2_ME)',1,length(i_exo_var));
oo_.variance_decomposition_ME(:,end+1)=var(y_ME_only_filtered)./s2_ME*100;
end
if ~options_.noprint %options_.nomoments == 0
skipline()
title='VARIANCE DECOMPOSITION SIMULATING ONE SHOCK AT A TIME (in percent)';
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))+2;
dyntable(options_, title, vertcat(headers, 'Tot. lin. contr.'), ...
M_.endo_names(ivar), [oo_.variance_decomposition sum(oo_.variance_decomposition,2)], lh, 8, 2);
if ME_present
headers_ME = vertcat(headers, 'ME');
dyntable(options_, [title,' WITH MEASUREMENT ERROR'], vertcat(headers_ME, 'Tot. lin. contr.'), M_.endo_names(ivar(index_subset)), ...
[oo_.variance_decomposition_ME sum(oo_.variance_decomposition_ME, 2)], lh, 8, 2);
end
if options_.TeX
headers = M_.exo_names_tex;
headers = vertcat(' ', headers);
labels = M_.endo_names_tex(ivar);
lh = cellofchararraymaxlength(labels)+2;
dyn_latex_table(M_, options_, title, 'sim_var_decomp', vertcat(headers, 'Tot. lin. contr.'), ...
labels_TeX, [oo_.variance_decomposition sum(oo_.variance_decomposition, 2)], lh, 8, 2);
if ME_present
headers_ME = vertcat(headers, 'ME');
dyn_latex_table(M_, options_, [title, ' WITH MEASUREMENT ERROR'], 'sim_var_decomp_ME', ...
vertcat(headers_ME, 'Tot. lin. contr.'), ...
labels_TeX(ivar(index_subset)), ...
[oo_.variance_decomposition_ME sum(oo_.variance_decomposition_ME, 2)], lh, 8, 2);
end
end
if options_.order == 1
fprintf('Note: numbers do not add up to 100 due to non-zero correlation of simulated shocks in small samples\n\n')
else
fprintf('Note: numbers do not add up to 100 due to i) non-zero correlation of simulated shocks in small samples and ii) nonlinearity\n\n')
end
end
end
end
warning(warning_old_state);
end
function y = get_filtered_time_series(y, m, options_)
if options_.hp_filter && ~options_.one_sided_hp_filter && ~options_.bandpass.indicator
[hptrend,y] = sample_hp_filter(y,options_.hp_filter);
elseif ~options_.hp_filter && options_.one_sided_hp_filter && ~options_.bandpass.indicator
[hptrend,y] = one_sided_hp_filter(y,options_.one_sided_hp_filter);
elseif ~options_.hp_filter && ~options_.one_sided_hp_filter && options_.bandpass.indicator
data_temp=dseries(y,'0q1');
data_temp=baxter_king_filter(data_temp,options_.bandpass.passband(1),options_.bandpass.passband(2),options_.bandpass.K);
y=data_temp.data;
elseif ~options_.hp_filter && ~options_.one_sided_hp_filter && ~options_.bandpass.indicator
y = bsxfun(@minus, y, m);
else
error('disp_moments:: You cannot use more than one filter at the same time')
end
end
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