198 lines
7.5 KiB
Matlab
198 lines
7.5 KiB
Matlab
function oo_=disp_moments(y,var_list,M_,options_,oo_)
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% function disp_moments(y,var_list,M_,options_,oo_)
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% Displays moments of simulated variables
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% INPUTS
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% y [double] nvar*nperiods vector of simulated variables.
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% var_list [char] nvar character array with names of variables.
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% M_ [structure] Dynare's model structure
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% oo_ [structure] Dynare's results structure
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% options_ [structure] Dynare's options structure
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%
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% OUTPUTS
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% oo_ [structure] Dynare's results structure,
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% Copyright (C) 2001-2015 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 <http://www.gnu.org/licenses/>.
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warning_old_state = warning;
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warning off
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if size(var_list,1) == 0
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var_list = M_.endo_names(1:M_.orig_endo_nbr, :);
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end
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nvar = size(var_list,1);
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ivar=zeros(nvar,1);
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for i=1:nvar
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i_tmp = strmatch(var_list(i,:),M_.endo_names,'exact');
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if isempty(i_tmp)
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error (['One of the variable specified does not exist']) ;
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else
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ivar(i) = i_tmp;
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end
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end
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y = y(ivar,options_.drop+1:end)';
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m = mean(y);
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% filter series
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y=get_filtered_time_series(y,m,options_);
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s2 = mean(y.*y);
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s = sqrt(s2);
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oo_.mean = transpose(m);
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oo_.var = y'*y/size(y,1);
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labels = deblank(M_.endo_names(ivar,:));
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labels_TeX = deblank(M_.endo_names_tex(ivar,:));
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if options_.nomoments == 0
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z = [ m' s' s2' (mean(y.^3)./s2.^1.5)' (mean(y.^4)./(s2.*s2)-3)' ];
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title='MOMENTS OF SIMULATED VARIABLES';
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title=add_filter_subtitle(title,options_);
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headers=char('VARIABLE','MEAN','STD. DEV.','VARIANCE','SKEWNESS', ...
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'KURTOSIS');
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dyntable(title,headers,labels,z,size(labels,2)+2,16,6);
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if options_.TeX
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dyn_latex_table(M_,title,'sim_moments',headers,labels_TeX,z,size(labels,2)+2,16,6);
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end
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end
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if options_.nocorr == 0
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corr = (y'*y/size(y,1))./(s'*s);
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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 == 0
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title = 'CORRELATION OF SIMULATED VARIABLES';
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title=add_filter_subtitle(title,options_);
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headers = char('VARIABLE',M_.endo_names(ivar,:));
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dyntable(title,headers,labels,corr,size(labels,2)+2,8,4);
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if options_.TeX
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headers = char('VARIABLE',M_.endo_names_tex(ivar,:));
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lh = size(labels,2)+2;
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dyn_latex_table(M_,title,'sim_corr_matrix',headers,labels_TeX,corr,size(labels,2)+2,8,4);
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end
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end
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end
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if options_.noprint == 0 && length(options_.conditional_variance_decomposition)
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fprintf('\nSTOCH_SIMUL: conditional_variance_decomposition requires theoretical moments, i.e. periods=0.\n')
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end
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ar = options_.ar;
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if ar > 0
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autocorr = [];
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for i=1:ar
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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,:)));
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autocorr = [ autocorr diag(oo_.autocorr{i}) ];
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end
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if options_.noprint == 0
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title = 'AUTOCORRELATION OF SIMULATED VARIABLES';
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title=add_filter_subtitle(title,options_);
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headers = char('VARIABLE',int2str([1:ar]'));
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dyntable(title,headers,labels,autocorr,size(labels,2)+2,8,4);
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if options_.TeX
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headers = char('VARIABLE',int2str([1:ar]'));
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lh = size(labels,2)+2;
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dyn_latex_table(M_,title,'sim_autocorr_matrix',headers,labels_TeX,autocorr,size(labels_TeX,2)+2,8,4);
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end
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end
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end
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if ~options_.nodecomposition
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if M_.exo_nbr == 1
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oo_.variance_decomposition = 100*ones(nvar,1);
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else
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oo_.variance_decomposition=zeros(nvar,M_.exo_nbr);
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%get starting values
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if isempty(M_.endo_histval)
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y0 = oo_.dr.ys;
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else
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y0 = M_.endo_histval;
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end
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%back out shock matrix used for generating y
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i_exo_var = setdiff([1:M_.exo_nbr],find(diag(M_.Sigma_e) == 0)); % find shocks with 0 variance
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chol_S = chol(M_.Sigma_e(i_exo_var,i_exo_var)); %decompose rest
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shock_mat=zeros(options_.periods,M_.exo_nbr); %initialize
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shock_mat(:,i_exo_var)=oo_.exo_simul(:,i_exo_var)/chol_S; %invert construction of oo_.exo_simul from simult.m
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for shock_iter=1:length(i_exo_var)
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temp_shock_mat=zeros(size(shock_mat));
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temp_shock_mat(:,i_exo_var(shock_iter))=shock_mat(:,i_exo_var(shock_iter));
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temp_shock_mat(:,i_exo_var) = temp_shock_mat(:,i_exo_var)*chol_S;
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y_sim_one_shock = simult_(y0,oo_.dr,temp_shock_mat,options_.order);
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y_sim_one_shock=y_sim_one_shock(ivar,1+options_.drop+1:end)';
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y_sim_one_shock=get_filtered_time_series(y_sim_one_shock,mean(y_sim_one_shock),options_);
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oo_.variance_decomposition(:,i_exo_var(shock_iter))=var(y_sim_one_shock)./s2*100;
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end
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if ~options_.noprint %options_.nomoments == 0
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skipline()
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title='VARIANCE DECOMPOSITION SIMULATING ONE SHOCK AT A TIME (in percent)';
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title=add_filter_subtitle(title,options_);
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headers = M_.exo_names;
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headers(M_.exo_names_orig_ord,:) = headers;
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headers = char(' ',headers);
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lh = size(deblank(M_.endo_names(ivar,:)),2)+2;
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dyntable(title,char(headers,'Tot. lin. contr.'),deblank(M_.endo_names(ivar,:)),[oo_.variance_decomposition sum(oo_.variance_decomposition,2)],lh,8,2);
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if options_.TeX
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headers=M_.exo_names_tex;
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headers = char(' ',headers);
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labels = deblank(M_.endo_names_tex(ivar,:));
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lh = size(labels,2)+2;
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dyn_latex_table(M_,title,'sim_var_decomp',char(headers,'Tot. lin. contr.'),labels_TeX,[oo_.variance_decomposition sum(oo_.variance_decomposition,2)],lh,8,2);
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end
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if options_.order == 1
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fprintf('Note: numbers do not add up to 100 due to non-zero correlation of simulated shocks in small samples\n\n')
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else
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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')
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end
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end
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end
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end
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warning(warning_old_state);
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end
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function y=get_filtered_time_series(y,m,options_)
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if options_.hp_filter && ~options_.one_sided_hp_filter && ~options_.bandpass.indicator
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[hptrend,y] = sample_hp_filter(y,options_.hp_filter);
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elseif ~options_.hp_filter && options_.one_sided_hp_filter && ~options_.bandpass.indicator
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[hptrend,y] = one_sided_hp_filter(y,options_.one_sided_hp_filter);
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elseif ~options_.hp_filter && ~options_.one_sided_hp_filter && options_.bandpass.indicator
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data_temp=dseries(y,'0q1');
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data_temp=baxter_king_filter(data_temp,options_.bandpass.passband(1),options_.bandpass.passband(2),200);
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y=data_temp.data;
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elseif ~options_.hp_filter && ~options_.one_sided_hp_filter && ~options_.bandpass.indicator
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y = bsxfun(@minus, y, m);
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else
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error('disp_moments:: You cannot use more than one filter at the same time')
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end
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end |