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Identification: various cosmetic fixes

new-samplers
Johannes Pfeifer 2023-11-30 12:26:58 +01:00 committed by Sébastien Villemot
parent e68793030c
commit 392721097c
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GPG Key ID: 2CECE9350ECEBE4A
3 changed files with 58 additions and 51 deletions
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22
matlab/dynare_identification.m
View File

@ -1,5 +1,5 @@
function [pdraws, STO_REDUCEDFORM, STO_MOMENTS, STO_DYNAMIC, STO_si_dDYNAMIC, STO_si_dREDUCEDFORM, STO_si_dMOMENTS, STO_dSPECTRUM, STO_dMINIMAL] = dynare_identification(M_,oo_,options_,bayestopt_,estim_params_,options_ident, pdraws0)
%function [pdraws, STO_REDUCEDFORM, STO_MOMENTS, STO_DYNAMIC, STO_si_dDYNAMIC, STO_si_dREDUCEDFORM, STO_si_dMOMENTS, STO_dSPECTRUM, STO_dMINIMAL] = dynare_identification(options_ident, pdraws0)
% [pdraws, STO_REDUCEDFORM, STO_MOMENTS, STO_DYNAMIC, STO_si_dDYNAMIC, STO_si_dREDUCEDFORM, STO_si_dMOMENTS, STO_dSPECTRUM, STO_dMINIMAL] = dynare_identification(options_ident, pdraws0)
% -------------------------------------------------------------------------
% This function is called, when the user specifies identification(...); in the mod file. It prepares all identification analysis:
% (1) set options, local and persistent variables for a new identification
@ -11,6 +11,11 @@ function [pdraws, STO_REDUCEDFORM, STO_MOMENTS, STO_DYNAMIC, STO_si_dDYNAMIC, ST
% to put identification in your mod file, otherwise the preprocessor won't provide all necessary objects
% =========================================================================
% INPUTS
% * M_ [structure] Matlab's structure describing the model
% * oo_ [structure] Matlab's structure describing the results
% * options_ [structure] Matlab's structure describing the current options
% * bayestopt_ [structure] describing the priors
% * estim_params_ [structure] characterizing parameters to be estimated
% * options_ident [structure] identification options
% * pdraws0 [SampleSize by totparam_nbr] optional: matrix of MC sample of model parameters
% -------------------------------------------------------------------------
@ -781,6 +786,19 @@ if iload <=0
else
maxrun_dMINIMAL = 0;
end
si_dDYNAMICnorm=NaN(max([maxrun_dDYNAMIC, maxrun_dREDUCEDFORM, maxrun_dMOMENTS, maxrun_dSPECTRUM, maxrun_dMINIMAL]),size(STO_si_dDYNAMIC,2));
if ~options_MC.no_identification_reducedform
si_dREDUCEDFORMnorm=NaN(max([maxrun_dDYNAMIC, maxrun_dREDUCEDFORM, maxrun_dMOMENTS, maxrun_dSPECTRUM, maxrun_dMINIMAL]),size(STO_si_dREDUCEDFORM,2));
end
if ~options_MC.no_identification_moments
si_dMOMENTSnorm=NaN(max([maxrun_dDYNAMIC, maxrun_dREDUCEDFORM, maxrun_dMOMENTS, maxrun_dSPECTRUM, maxrun_dMINIMAL]),size(STO_si_dMOMENTS,2));
end
if ~options_MC.no_identification_spectrum
dSPECTRUMnorm=NaN(max([maxrun_dDYNAMIC, maxrun_dREDUCEDFORM, maxrun_dMOMENTS, maxrun_dSPECTRUM, maxrun_dMINIMAL]),size(STO_dSPECTRUM,2));
end
if ~options_MC.no_identification_minimal
dMINIMALnorm=NaN(max([maxrun_dDYNAMIC, maxrun_dREDUCEDFORM, maxrun_dMOMENTS, maxrun_dSPECTRUM, maxrun_dMINIMAL]),size(STO_dMINIMAL,2));
end
for irun=1:max([maxrun_dDYNAMIC, maxrun_dREDUCEDFORM, maxrun_dMOMENTS, maxrun_dSPECTRUM, maxrun_dMINIMAL])
iter=iter+1;
% note that this is not the same si_dDYNAMICnorm as computed in identification_analysis
@ -912,7 +930,7 @@ if SampleSize > 1
fprintf('Testing %s.\n',tittxt);
if ~iload
options_ident.tittxt = tittxt; %title text for graphs and figures
[ide_moments_min, ide_spectrum_min, ide_minimal_min, ide_hess_min, ide_reducedform_min, ide_dynamic_min, derivatives_info_min, info_min, error_indicator_min] = ...
[ide_moments_min, ide_spectrum_min, ide_minimal_min, ide_hess_min, ide_reducedform_min, ide_dynamic_min, ~, ~, error_indicator_min] = ...
identification_analysis(M_,options_,oo_,bayestopt_,estim_params_,pdraws(jmin,:), indpmodel, indpstderr, indpcorr, options_ident, dataset_info, prior_exist, 1); %the 1 at the end initializes persistent variables
save([IdentifDirectoryName '/' fname '_identif.mat'], 'ide_hess_min', 'ide_moments_min','ide_spectrum_min','ide_minimal_min','ide_reducedform_min', 'ide_dynamic_min', 'jmin', '-append');
end

4
matlab/identification_analysis.m
View File

@ -142,7 +142,7 @@ error_indicator.identification_spectrum=0;
if info(1) == 0 %no errors in solution
% Compute parameter Jacobians for identification analysis
[MEAN, dMEAN, REDUCEDFORM, dREDUCEDFORM, DYNAMIC, dDYNAMIC, MOMENTS, dMOMENTS, dSPECTRUM, dSPECTRUM_NO_MEAN, dMINIMAL, derivatives_info] = get_identification_jacobians(estim_params_, M_, options_, options_ident, indpmodel, indpstderr, indpcorr, indvobs, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
[~, ~, REDUCEDFORM, dREDUCEDFORM, DYNAMIC, dDYNAMIC, MOMENTS, dMOMENTS, dSPECTRUM, dSPECTRUM_NO_MEAN, dMINIMAL, derivatives_info] = get_identification_jacobians(estim_params_, M_, options_, options_ident, indpmodel, indpstderr, indpcorr, indvobs, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
if isempty(dMINIMAL)
% Komunjer and Ng is not computed if (1) minimality conditions are not fullfilled or (2) there are more shocks and measurement errors than observables, so we need to reset options
error_indicator.identification_minimal = 1;
@ -300,7 +300,7 @@ if info(1) == 0 %no errors in solution
derivatives_info.no_DLIK = 1;
bounds = prior_bounds(bayestopt_, options_.prior_trunc); %reset bounds as lb and ub must only be operational during mode-finding
%note that for order>1 we do not provide any information on DT,DYss,DOM in derivatives_info, such that dsge_likelihood creates an error. Therefore the computation will be based on simulated_moment_uncertainty for order>1.
[fval, info, cost_flag, DLIK, AHess, ys, trend_coeff, M_, options_, bayestopt_, oo_.dr] = dsge_likelihood(params', dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, bounds, oo_.dr, oo_.steady_state,oo_.exo_steady_state, oo_.exo_det_steady_state. derivatives_info); %non-used output variables need to be set for octave for some reason
[~, info, ~, ~, AHess, ~, ~, M_, options_, ~, oo_.dr] = dsge_likelihood(params', dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, bounds, oo_.dr, oo_.steady_state,oo_.exo_steady_state, oo_.exo_det_steady_state. derivatives_info); %non-used output variables need to be set for octave for some reason
%note that for the order of parameters in AHess we have: stderr parameters come first, corr parameters second, model parameters third. the order within these blocks corresponds to the order specified in the estimated_params block
options_.analytic_derivation = analytic_derivation; %reset option
AHess = -AHess; %take negative of hessian

83
matlab/plot_identification.m
View File

@ -1,5 +1,5 @@
function plot_identification(M_, params, idemoments, idehess, idemodel, idelre, advanced, tittxt, name, IdentifDirectoryName, fname, options_, estim_params_, bayestopt_, tit_TeX, name_tex)
% function plot_identification(params,idemoments,idehess,idemodel, idelre, advanced, tittxt, name, IdentifDirectoryName, fname, options_, estim_params_, bayestopt_, tit_TeX, name_tex)
% plot_identification(M_, params,idemoments,idehess,idemodel, idelre, advanced, tittxt, name, IdentifDirectoryName, fname, options_, estim_params_, bayestopt_, tit_TeX, name_tex)
%
% INPUTS
% o M_ [structure] model
@ -10,10 +10,15 @@ function plot_identification(M_, params, idemoments, idehess, idemodel, idelre,
% o idelre [structure] identification results for the LRE model
% o advanced [integer] flag for advanced identification checks
% o tittxt [char] name of the results to plot
% o name [char] list of names
% o name [char] list of parameter names
% o IdentifDirectoryName [char] directory name
% o fname [char] file name
% o options_ [structure] structure describing the current options
% o estim_params_ [structure] characterizing parameters to be estimated
% o bayestopt_ [structure] describing the priors
% o tittxt [char] TeX-name of the results to plot
% o name_tex [char] TeX-names of the parameters
%
% OUTPUTS
% None
%
@ -53,20 +58,19 @@ si_dLREnorm = idelre.si_dDYNAMICnorm;
tittxt1=regexprep(tittxt, ' ', '_');
tittxt1=strrep(tittxt1, '.', '');
if SampleSize == 1
si_dMOMENTS = idemoments.si_dMOMENTS;
hh_fig = dyn_figure(options_.nodisplay,'Name',[tittxt, ' - Identification using info from observables']);
subplot(211)
mmm = (idehess.ide_strength_dMOMENTS);
[ss, is] = sort(mmm);
[~, is] = sort(mmm);
if ~all(isnan(idehess.ide_strength_dMOMENTS_prior)) ...
&& ~(nparam == 1 && ~isoctave && matlab_ver_less_than('9.7')) % MATLAB < R2019b does not accept bar(1, [2 3])
bar(1:nparam,log([idehess.ide_strength_dMOMENTS(:,is)' idehess.ide_strength_dMOMENTS_prior(:,is)']))
else
bar(1:nparam,log([idehess.ide_strength_dMOMENTS(:,is)' ]))
bar(1:nparam,log(idehess.ide_strength_dMOMENTS(:,is)'))
end
hold on
plot((1:length(idehess.ide_strength_dMOMENTS(:,is)))-0.15,log([idehess.ide_strength_dMOMENTS(:,is)']),'o','MarkerSize',7,'MarkerFaceColor',[0 0 0],'MarkerEdgeColor','none')
plot((1:length(idehess.ide_strength_dMOMENTS_prior(:,is)))+0.15,log([idehess.ide_strength_dMOMENTS_prior(:,is)']),'o','MarkerSize',7,'MarkerFaceColor',[0 0 0],'MarkerEdgeColor','none')
plot((1:length(idehess.ide_strength_dMOMENTS(:,is)))-0.15,log(idehess.ide_strength_dMOMENTS(:,is)'),'o','MarkerSize',7,'MarkerFaceColor',[0 0 0],'MarkerEdgeColor','none')
plot((1:length(idehess.ide_strength_dMOMENTS_prior(:,is)))+0.15,log(idehess.ide_strength_dMOMENTS_prior(:,is)'),'o','MarkerSize',7,'MarkerFaceColor',[0 0 0],'MarkerEdgeColor','none')
if any(isinf(log(idehess.ide_strength_dMOMENTS(idehess.identified_parameter_indices))))
%-Inf, i.e. 0 strength
inf_indices=find(isinf(log(idehess.ide_strength_dMOMENTS(idehess.identified_parameter_indices))) & log(idehess.ide_strength_dMOMENTS(idehess.identified_parameter_indices))<0);
@ -94,8 +98,8 @@ if SampleSize == 1
if options_.TeX
text(ip,dy(1),name_tex{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','latex')
else
text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
end
if ~all(isnan(idehess.ide_strength_dMOMENTS_prior))
legend('relative to param value','relative to prior std','Location','Best')
@ -116,8 +120,8 @@ if SampleSize == 1
bar(1:nparam, log([idehess.deltaM(is)]))
end
hold on
plot((1:length(idehess.deltaM(is)))-0.15,log([idehess.deltaM(is)']),'o','MarkerSize',7,'MarkerFaceColor',[0 0 0],'MarkerEdgeColor','none')
plot((1:length(idehess.deltaM_prior(is)))+0.15,log([idehess.deltaM_prior(is)']),'o','MarkerSize',7,'MarkerFaceColor',[0 0 0],'MarkerEdgeColor','none')
plot((1:length(idehess.deltaM(is)))-0.15,log(idehess.deltaM(is)'),'o','MarkerSize',7,'MarkerFaceColor',[0 0 0],'MarkerEdgeColor','none')
plot((1:length(idehess.deltaM_prior(is)))+0.15,log(idehess.deltaM_prior(is)'),'o','MarkerSize',7,'MarkerFaceColor',[0 0 0],'MarkerEdgeColor','none')
inf_pos=find(isinf(log(idehess.deltaM)));
if ~isempty(inf_pos)
inf_indices=~ismember(inf_pos,idehess.sensitivity_zero_pos);
@ -137,8 +141,8 @@ if SampleSize == 1
if options_.TeX
text(ip,dy(1),name_tex{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','latex')
else
text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
end
if ~all(isnan(idehess.deltaM_prior))
legend('relative to param value','relative to prior std','Location','Best')
@ -191,8 +195,8 @@ if SampleSize == 1
if options_.TeX
text(ip,dy(1),name_tex{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','latex')
else
text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
end
legend('Moments','Model','LRE model','Location','Best')
title('Sensitivity bars using derivatives (log-scale)')
@ -214,9 +218,6 @@ if SampleSize == 1
% identificaton patterns
for j=1:size(idemoments.cosndMOMENTS,2)
pax=NaN(nparam,nparam);
% fprintf('\n')
% disp(['Collinearity patterns with ', int2str(j) ,' parameter(s)'])
% fprintf('%-15s [%-*s] %10s\n','Parameter',(15+1)*j,' Expl. params ','cosn')
for i=1:nparam
namx='';
for in=1:j
@ -227,12 +228,11 @@ if SampleSize == 1
if options_.TeX
namx=[namx ' ' sprintf('%-15s',name_tex{dumpindx})];
else
namx=[namx ' ' sprintf('%-15s',name{dumpindx})];
namx=[namx ' ' sprintf('%-15s',name{dumpindx})];
end
pax(i,dumpindx)=idemoments.cosndMOMENTS(i,j);
end
end
% fprintf('%-15s [%s] %10.3f\n',name{i},namx,idemoments.cosndMOMENTS(i,j))
end
hh_fig = dyn_figure(options_.nodisplay,'Name',[tittxt,' - Collinearity patterns with ', int2str(j) ,' parameter(s)']);
imagesc(pax,[0 1]);
@ -243,9 +243,9 @@ if SampleSize == 1
text(ip,(0.5),name_tex{ip},'rotation',90,'HorizontalAlignment','left','interpreter','latex')
text(0.5,ip,name_tex{ip},'rotation',0,'HorizontalAlignment','right','interpreter','latex')
else
text(ip,(0.5),name{ip},'rotation',90,'HorizontalAlignment','left','interpreter','none')
text(0.5,ip,name{ip},'rotation',0,'HorizontalAlignment','right','interpreter','none')
end
text(ip,(0.5),name{ip},'rotation',90,'HorizontalAlignment','left','interpreter','none')
text(0.5,ip,name{ip},'rotation',0,'HorizontalAlignment','right','interpreter','none')
end
end
colorbar;
colormap('jet');
@ -275,7 +275,7 @@ if SampleSize == 1
end
end
skipline()
[U,S,V]=svd(idehess.AHess,0);
[~,S,V]=svd(idehess.AHess,0);
S=diag(S);
if idehess.flag_score
if nparam<5
@ -288,8 +288,6 @@ if SampleSize == 1
tex_tit_2=[tittxt,' - Identification patterns (Information matrix): HIGHEST SV'];
end
else
% S = idemoments.S;
% V = idemoments.V;
if nparam<5
f1 = dyn_figure(options_.nodisplay,'Name',[tittxt,' - Identification patterns (moments Information matrix)']);
tex_tit_1=[tittxt,' - Identification patterns (moments Information matrix)'];
@ -322,10 +320,10 @@ if SampleSize == 1
if options_.TeX
text(ip,-0.02,name_tex{ip},'rotation',90,'HorizontalAlignment','right','interpreter','latex')
else
text(ip,-0.02,name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
text(ip,-0.02,name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
end
end
end
title(['Singular value ',num2str(Stit)])
end
dyn_saveas(f1,[ IdentifDirectoryName '/' fname '_ident_pattern_' tittxt1 '_1' ],options_.nodisplay,options_.graph_format);
@ -361,10 +359,10 @@ if SampleSize == 1
end
else
hh_fig = dyn_figure(options_.nodisplay,'Name',['MC sensitivities']);
hh_fig = dyn_figure(options_.nodisplay,'Name','MC sensitivities');
subplot(211)
mmm = (idehess.ide_strength_dMOMENTS);
[ss, is] = sort(mmm);
[~, is] = sort(mmm);
mmm = mean(si_dMOMENTSnorm)';
mmm = mmm./max(mmm);
if advanced
@ -384,8 +382,8 @@ else
if options_.TeX
text(ip,dy(1),name_tex{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','latex')
else
text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
end
if advanced
legend('Moments','Model','LRE model','Location','Best')
@ -407,11 +405,10 @@ else
end
if advanced
if ~options_.nodisplay,
if ~options_.nodisplay
skipline()
disp('Displaying advanced diagnostics')
end
% options_.nograph=1;
hh_fig = dyn_figure(options_.nodisplay,'Name','MC Condition Number');
subplot(221)
hist(log10(idemodel.cond))
@ -448,16 +445,6 @@ else
options_mcf.title = 'MC Highest Condition Number Model Moments';
[~,is]=sort(idemoments.cond);
mcf_analysis(params, is(1:ncut), is(ncut+1:end), options_mcf, M_, options_, bayestopt_, estim_params_);
% [proba, dproba] = stab_map_1(idemoments.Mco', is(1:ncut), is(ncut+1:end), 'HighestCondNumberMoments_vs_Mco', 1, [], IdentifDirectoryName);
% for j=1:nparam,
% % ibeh=find(idemoments.Mco(j,:)<0.9);
% % inonbeh=find(idemoments.Mco(j,:)>=0.9);
% % if ~isempty(ibeh) && ~isempty(inonbeh)
% % [proba, dproba] = stab_map_1(params, ibeh, inonbeh, ['HighestMultiCollinearity_',name{j}], 1, [], IdentifDirectoryName);
% % end
% [~,is]=sort(idemoments.Mco(:,j));
% [proba, dproba] = stab_map_1(params, is(1:ncut), is(ncut+1:end), ['MC_HighestMultiCollinearity_',name{j}], 1, [], IdentifDirectoryName, 0.15);
% end
if nparam<5
f1 = dyn_figure(options_.nodisplay,'Name',[tittxt,' - MC Identification patterns (moments): HIGHEST SV']);
@ -487,8 +474,10 @@ else
SSS = idemoments.S(:,jj);
end
subplot(nsubplo,1,jj)
post_median=NaN(1,nparam);
hpd_interval=NaN(nparam,2);
for i=1:nparam
[post_mean, post_median(:,i), post_var, hpd_interval(i,:), post_deciles] = posterior_moments(VVV(:,i),0,0.9);
[~, post_median(:,i), ~, hpd_interval(i,:)] = posterior_moments(VVV(:,i),0,0.9);
end
bar(post_median)
hold on, plot(hpd_interval,'--*r'),
@ -500,10 +489,10 @@ else
if options_.TeX
text(ip,-0.02,name_tex{ip},'rotation',90,'HorizontalAlignment','right','interpreter','latex')
else
text(ip,-0.02,name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
text(ip,-0.02,name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
end
end
end
title(['MEAN Singular value ',num2str(Stit)])
end
dyn_saveas(f1,[IdentifDirectoryName '/' fname '_MC_ident_pattern_1' ],options_.nodisplay,options_.graph_format);