509 lines
26 KiB
Matlab
509 lines
26 KiB
Matlab
function plot_identification(params, idemoments, idehess, idemodel, idelre, advanced, tittxt, name, IdentifDirectoryName, tit_TeX, name_tex)
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% function plot_identification(params,idemoments,idehess,idemodel, idelre, advanced, tittxt, name, IdentifDirectoryName)
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%
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% INPUTS
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% o params [array] parameter values for identification checks
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% o idemoments [structure] identification results for the moments
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% o idehess [structure] identification results for the Hessian
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% o idemodel [structure] identification results for the reduced form solution
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% o idelre [structure] identification results for the LRE model
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% o advanced [integer] flag for advanced identification checks
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% o tittxt [char] name of the results to plot
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% o name [char] list of names
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% o IdentifDirectoryName [char] directory name
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% o tittxt [char] TeX-name of the results to plot
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% o name_tex [char] TeX-names of the parameters
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% OUTPUTS
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% None
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%
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% SPECIAL REQUIREMENTS
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% None
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% Copyright © 2008-2021 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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global M_ options_
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if nargin <10 || isempty(tit_TeX)
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tit_TeX=tittxt;
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end
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if nargin <11
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name_TeX=name;
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end
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[SampleSize, nparam]=size(params);
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si_dMOMENTSnorm = idemoments.si_dMOMENTSnorm;
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si_dTAUnorm = idemodel.si_dREDUCEDFORMnorm;
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si_dLREnorm = idelre.si_dDYNAMICnorm;
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tittxt1=regexprep(tittxt, ' ', '_');
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tittxt1=strrep(tittxt1, '.', '');
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if SampleSize == 1
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si_dMOMENTS = idemoments.si_dMOMENTS;
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hh = dyn_figure(options_.nodisplay,'Name',[tittxt, ' - Identification using info from observables']);
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subplot(211)
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mmm = (idehess.ide_strength_dMOMENTS);
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[ss, is] = sort(mmm);
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if ~all(isnan(idehess.ide_strength_dMOMENTS_prior)) ...
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&& ~(nparam == 1 && ~isoctave && matlab_ver_less_than('9.7')) % MATLAB < R2019b does not accept bar(1, [2 3])
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bar(1:nparam,log([idehess.ide_strength_dMOMENTS(:,is)' idehess.ide_strength_dMOMENTS_prior(:,is)']))
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else
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bar(1:nparam,log([idehess.ide_strength_dMOMENTS(:,is)' ]))
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end
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hold on
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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')
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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')
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if any(isinf(log(idehess.ide_strength_dMOMENTS(idehess.identified_parameter_indices))))
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%-Inf, i.e. 0 strength
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inf_indices=find(isinf(log(idehess.ide_strength_dMOMENTS(idehess.identified_parameter_indices))) & log(idehess.ide_strength_dMOMENTS(idehess.identified_parameter_indices))<0);
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inf_pos=ismember(is,idehess.identified_parameter_indices(inf_indices));
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plot(find(inf_pos)-0.15,zeros(sum(inf_pos),1),'o','MarkerSize',7,'MarkerFaceColor',[1 0 0],'MarkerEdgeColor',[0 0 0])
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%+Inf, i.e. Inf strength
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inf_indices=find(isinf(log(idehess.ide_strength_dMOMENTS(idehess.identified_parameter_indices))) & log(idehess.ide_strength_dMOMENTS(idehess.identified_parameter_indices))>0);
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inf_pos=ismember(is,idehess.identified_parameter_indices(inf_indices));
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plot(find(inf_pos)-0.15,zeros(sum(inf_pos),1),'o','MarkerSize',7,'MarkerFaceColor',[1 1 1],'MarkerEdgeColor',[0 0 0])
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end
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if any(isinf(log(idehess.ide_strength_dMOMENTS_prior(idehess.identified_parameter_indices))))
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%-Inf, i.e. 0 strength
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inf_indices=find(isinf(log(idehess.ide_strength_dMOMENTS_prior(idehess.identified_parameter_indices))) & log(idehess.ide_strength_dMOMENTS_prior(idehess.identified_parameter_indices))<0);
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inf_pos=ismember(is,idehess.identified_parameter_indices(inf_indices));
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plot(find(inf_pos)+0.15,zeros(sum(inf_pos),1),'o','MarkerSize',7,'MarkerFaceColor',[1 0 0],'MarkerEdgeColor',[0 0 0])
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%+Inf, i.e. 0 strength
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inf_indices=find(isinf(log(idehess.ide_strength_dMOMENTS_prior(idehess.identified_parameter_indices))) & log(idehess.ide_strength_dMOMENTS_prior(idehess.identified_parameter_indices))>0);
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inf_pos=ismember(is,idehess.identified_parameter_indices(inf_indices));
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plot(find(inf_pos)+0.15,zeros(sum(inf_pos),1),'o','MarkerSize',7,'MarkerFaceColor',[1 1 1],'MarkerEdgeColor',[0 0 0])
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end
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set(gca,'xlim',[0 nparam+1])
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set(gca,'xticklabel','')
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dy = get(gca,'ylim');
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for ip=1:nparam
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text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
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end
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if ~all(isnan(idehess.ide_strength_dMOMENTS_prior))
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legend('relative to param value','relative to prior std','Location','Best')
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else
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legend('relative to param value','Location','Best')
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end
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if idehess.flag_score
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title('Identification strength with asymptotic Information matrix (log-scale)')
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else
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title('Identification strength with moments Information matrix (log-scale)')
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end
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subplot(212)
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if ~all(isnan(idehess.deltaM_prior)) ...
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&& ~(nparam == 1 && ~isoctave && matlab_ver_less_than('9.7')) % MATLAB < R2019b does not accept bar(1, [2 3])
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bar(1:nparam, log([idehess.deltaM(is) idehess.deltaM_prior(is)]))
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else
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bar(1:nparam, log([idehess.deltaM(is)]))
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end
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hold on
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plot((1:length(idehess.deltaM(is)))-0.15,log([idehess.deltaM(is)']),'o','MarkerSize',7,'MarkerFaceColor',[0 0 0],'MarkerEdgeColor','none')
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plot((1:length(idehess.deltaM_prior(is)))+0.15,log([idehess.deltaM_prior(is)']),'o','MarkerSize',7,'MarkerFaceColor',[0 0 0],'MarkerEdgeColor','none')
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inf_pos=find(isinf(log(idehess.deltaM)));
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if ~isempty(inf_pos)
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inf_indices=~ismember(inf_pos,idehess.sensitivity_zero_pos);
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inf_pos=ismember(is,inf_pos(inf_indices));
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plot(find(inf_pos)-0.15,zeros(sum(inf_pos),1),'o','MarkerSize',7,'MarkerFaceColor',[1 1 1],'MarkerEdgeColor',[0 0 0])
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end
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inf_pos=find(isinf(log(idehess.deltaM_prior)));
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if ~isempty(inf_pos)
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inf_indices=~ismember(inf_pos,idehess.sensitivity_zero_pos);
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inf_pos=ismember(is,inf_pos(inf_indices));
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plot(find(inf_pos)+0.15,zeros(sum(inf_pos),1),'o','MarkerSize',7,'MarkerFaceColor',[1 1 1],'MarkerEdgeColor',[0 0 0])
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end
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set(gca,'xlim',[0 nparam+1])
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set(gca,'xticklabel','')
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dy = get(gca,'ylim');
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for ip=1:nparam
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text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
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end
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if ~all(isnan(idehess.deltaM_prior))
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legend('relative to param value','relative to prior std','Location','Best')
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else
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legend('relative to param value','Location','Best')
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end
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if idehess.flag_score
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title('Sensitivity component with asymptotic Information matrix (log-scale)')
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else
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title('Sensitivity component with moments Information matrix (log-scale)')
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end
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if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
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fidTeX = fopen([IdentifDirectoryName '/' M_.fname '_ident_strength_' tittxt1,'.tex'],'w');
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fprintf(fidTeX,'%% TeX eps-loader file generated by plot_identification.m (Dynare).\n');
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fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
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fprintf(fidTeX,'\\begin{figure}[H]\n');
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fprintf(fidTeX,'\\centering \n');
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fprintf(fidTeX,'\\includegraphics[width=0.8\\textwidth]{%s_ident_strength_%s}\n',[IdentifDirectoryName '/' M_.fname],tittxt1);
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fprintf(fidTeX,'\\caption{%s - Identification using info from observables.}',tit_TeX);
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fprintf(fidTeX,'\\label{Fig:ident:%s}\n',deblank(tittxt));
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fprintf(fidTeX,'\\end{figure}\n\n');
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fprintf(fidTeX,'%% End Of TeX file. \n');
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fclose(fidTeX);
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end
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dyn_saveas(hh,[IdentifDirectoryName '/' M_.fname '_ident_strength_' tittxt1],options_.nodisplay,options_.graph_format);
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if advanced
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if ~options_.nodisplay
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skipline()
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disp('Plotting advanced diagnostics')
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end
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if all(isnan([si_dMOMENTSnorm';si_dTAUnorm';si_dLREnorm']))
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fprintf('\nIDENTIFICATION: Skipping sensitivity plot, because standard deviation of parameters is NaN, possibly due to the use of ML.\n')
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else
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hh = dyn_figure(options_.nodisplay,'Name',[tittxt, ' - Sensitivity plot']);
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subplot(211)
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mmm = (si_dMOMENTSnorm)'./max(si_dMOMENTSnorm);
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mmm1 = (si_dTAUnorm)'./max(si_dTAUnorm);
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mmm=[mmm mmm1];
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mmm1 = (si_dLREnorm)'./max(si_dLREnorm);
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offset=length(si_dTAUnorm)-length(si_dLREnorm);
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mmm1 = [NaN(offset,1); mmm1];
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mmm=[mmm mmm1];
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bar(log(mmm(is,:).*100))
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set(gca,'xlim',[0 nparam+1])
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set(gca,'xticklabel','')
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dy = get(gca,'ylim');
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for ip=1:nparam
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text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
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end
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legend('Moments','Model','LRE model','Location','Best')
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title('Sensitivity bars using derivatives (log-scale)')
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dyn_saveas(hh,[IdentifDirectoryName '/' M_.fname '_sensitivity_' tittxt1 ],options_.nodisplay,options_.graph_format);
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if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
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fidTeX = fopen([IdentifDirectoryName '/' M_.fname '_sensitivity_' tittxt1,'.tex'],'w');
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fprintf(fidTeX,'%% TeX eps-loader file generated by plot_identification.m (Dynare).\n');
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fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
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fprintf(fidTeX,'\\begin{figure}[H]\n');
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fprintf(fidTeX,'\\centering \n');
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fprintf(fidTeX,'\\includegraphics[width=0.8\\textwidth]{%s_sensitivity_%s}\n',[IdentifDirectoryName '/' M_.fname],tittxt1);
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fprintf(fidTeX,'\\caption{%s - Sensitivity plot.}',tit_TeX);
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fprintf(fidTeX,'\\label{Fig:sensitivity:%s}\n',deblank(tittxt));
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fprintf(fidTeX,'\\end{figure}\n\n');
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fprintf(fidTeX,'%% End Of TeX file. \n');
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fclose(fidTeX);
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end
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end
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% identificaton patterns
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for j=1:size(idemoments.cosndMOMENTS,2)
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pax=NaN(nparam,nparam);
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% fprintf('\n')
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% disp(['Collinearity patterns with ', int2str(j) ,' parameter(s)'])
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% fprintf('%-15s [%-*s] %10s\n','Parameter',(15+1)*j,' Expl. params ','cosn')
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for i=1:nparam
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namx='';
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for in=1:j
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dumpindx = idemoments.pars{i,j}(in);
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if isnan(dumpindx)
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namx=[namx ' ' sprintf('%-15s','--')];
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else
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namx=[namx ' ' sprintf('%-15s',name{dumpindx})];
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pax(i,dumpindx)=idemoments.cosndMOMENTS(i,j);
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end
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end
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% fprintf('%-15s [%s] %10.3f\n',name{i},namx,idemoments.cosndMOMENTS(i,j))
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end
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hh = dyn_figure(options_.nodisplay,'Name',[tittxt,' - Collinearity patterns with ', int2str(j) ,' parameter(s)']);
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imagesc(pax,[0 1]);
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set(gca,'xticklabel','')
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set(gca,'yticklabel','')
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for ip=1:nparam
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text(ip,(0.5),name{ip},'rotation',90,'HorizontalAlignment','left','interpreter','none')
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text(0.5,ip,name{ip},'rotation',0,'HorizontalAlignment','right','interpreter','none')
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end
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colorbar;
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colormap('jet');
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ax=colormap;
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ax(1,:)=[0.9 0.9 0.9];
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colormap(ax);
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if nparam>10
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set(gca,'xtick',(5:5:nparam))
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set(gca,'ytick',(5:5:nparam))
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end
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set(gca,'xgrid','on')
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set(gca,'ygrid','on')
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xlabel([tittxt,' - Collinearity patterns with ', int2str(j) ,' parameter(s)'],'interpreter','none')
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dyn_saveas(hh,[ IdentifDirectoryName '/' M_.fname '_ident_collinearity_' tittxt1 '_' int2str(j) ],options_.nodisplay,options_.graph_format);
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if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
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fidTeX = fopen([ IdentifDirectoryName '/' M_.fname '_ident_collinearity_' tittxt1 '_' int2str(j),'.tex'],'w');
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fprintf(fidTeX,'%% TeX eps-loader file generated by plot_identification.m (Dynare).\n');
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fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
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fprintf(fidTeX,'\\begin{figure}[H]\n');
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fprintf(fidTeX,'\\centering \n');
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fprintf(fidTeX,'\\includegraphics[width=0.8\\textwidth]{%s_ident_collinearity_%s_%u}\n',[IdentifDirectoryName '/' M_.fname],tittxt1,j);
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fprintf(fidTeX,'\\caption{%s - Collinearity patterns with %u parameter(s).}',tit_TeX,j);
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fprintf(fidTeX,'\\label{Fig:collinearity:%s:%u_pars}\n',deblank(tittxt),j);
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fprintf(fidTeX,'\\end{figure}\n\n');
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fprintf(fidTeX,'%% End Of TeX file. \n');
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fclose(fidTeX);
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end
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end
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skipline()
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[U,S,V]=svd(idehess.AHess,0);
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S=diag(S);
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if idehess.flag_score
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if nparam<5
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f1 = dyn_figure(options_.nodisplay,'Name',[tittxt,' - Identification patterns (Information matrix)']);
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tex_tit_1=[tittxt,' - Identification patterns (Information matrix)'];
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else
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f1 = dyn_figure(options_.nodisplay,'Name',[tittxt,' - Identification patterns (Information matrix): SMALLEST SV']);
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tex_tit_1=[tittxt,' - Identification patterns (Information matrix): SMALLEST SV'];
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f2 = dyn_figure(options_.nodisplay,'Name',[tittxt,' - Identification patterns (Information matrix): HIGHEST SV']);
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tex_tit_2=[tittxt,' - Identification patterns (Information matrix): HIGHEST SV'];
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end
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else
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% S = idemoments.S;
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% V = idemoments.V;
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if nparam<5
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f1 = dyn_figure(options_.nodisplay,'Name',[tittxt,' - Identification patterns (moments Information matrix)']);
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tex_tit_1=[tittxt,' - Identification patterns (moments Information matrix)'];
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else
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f1 = dyn_figure(options_.nodisplay,'Name',[tittxt,' - Identification patterns (moments Information matrix): SMALLEST SV']);
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tex_tit_1=[tittxt,' - Identification patterns (moments Information matrix): SMALLEST SV'];
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f2 = dyn_figure(options_.nodisplay,'Name',[tittxt,' - Identification patterns (moments Information matrix): HIGHEST SV']);
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tex_tit_2=[tittxt,' - Identification patterns (moments Information matrix): HIGHEST SV'];
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end
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end
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for j=1:min(nparam,8)
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if j<5
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set(0,'CurrentFigure',f1),
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jj=j;
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else
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set(0,'CurrentFigure',f2),
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jj=j-4;
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end
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subplot(4,1,jj)
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if j<5
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bar(abs(V(:,end-j+1)))
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Stit = S(end-j+1);
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else
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bar(abs(V(:,jj))),
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Stit = S(jj);
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end
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set(gca,'xticklabel','')
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if j==4 || j==nparam || j==8
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for ip=1:nparam
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text(ip,-0.02,name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
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end
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end
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title(['Singular value ',num2str(Stit)])
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end
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dyn_saveas(f1,[ IdentifDirectoryName '/' M_.fname '_ident_pattern_' tittxt1 '_1' ],options_.nodisplay,options_.graph_format);
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if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
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fidTeX = fopen([ IdentifDirectoryName '/' M_.fname '_ident_pattern_' tittxt1 '_1','.tex'],'w');
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fprintf(fidTeX,'%% TeX eps-loader file generated by plot_identification.m (Dynare).\n');
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fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
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fprintf(fidTeX,'\\begin{figure}[H]\n');
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fprintf(fidTeX,'\\centering \n');
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fprintf(fidTeX,'\\includegraphics[width=0.8\\textwidth]{%s_ident_pattern_%s_1}\n',[IdentifDirectoryName '/' M_.fname],tittxt1);
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fprintf(fidTeX,'\\caption{%s.}',tex_tit_1);
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fprintf(fidTeX,'\\label{Fig:ident_pattern:%s:1}\n',tittxt1);
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fprintf(fidTeX,'\\end{figure}\n\n');
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fprintf(fidTeX,'%% End Of TeX file. \n');
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fclose(fidTeX);
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end
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if nparam>4
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dyn_saveas(f2,[ IdentifDirectoryName '/' M_.fname '_ident_pattern_' tittxt1 '_2' ],options_.nodisplay,options_.graph_format);
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if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
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fidTeX = fopen([ IdentifDirectoryName '/' M_.fname '_ident_pattern_' tittxt1 '_2.tex'],'w');
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fprintf(fidTeX,'%% TeX eps-loader file generated by plot_identification.m (Dynare).\n');
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fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
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fprintf(fidTeX,'\\begin{figure}[H]\n');
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fprintf(fidTeX,'\\centering \n');
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fprintf(fidTeX,'\\includegraphics[width=0.8\\textwidth]{%s_ident_pattern_%s_2}\n',[IdentifDirectoryName '/' M_.fname],tittxt1);
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fprintf(fidTeX,'\\caption{%s.}',tex_tit_2);
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fprintf(fidTeX,'\\label{Fig:ident_pattern:%s:2}\n',tittxt1);
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fprintf(fidTeX,'\\end{figure}\n\n');
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fprintf(fidTeX,'%% End Of TeX file. \n');
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fclose(fidTeX);
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end
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end
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end
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else
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hh = dyn_figure(options_.nodisplay,'Name',['MC sensitivities']);
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subplot(211)
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mmm = (idehess.ide_strength_dMOMENTS);
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[ss, is] = sort(mmm);
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mmm = mean(si_dMOMENTSnorm)';
|
|
mmm = mmm./max(mmm);
|
|
if advanced
|
|
mmm1 = mean(si_dTAUnorm)';
|
|
mmm=[mmm mmm1./max(mmm1)];
|
|
mmm1 = mean(si_dLREnorm)';
|
|
offset=size(si_dTAUnorm,2)-size(si_dLREnorm,2);
|
|
mmm1 = [NaN(offset,1); mmm1./max(mmm1)];
|
|
mmm=[mmm mmm1];
|
|
end
|
|
|
|
bar(mmm(is,:))
|
|
set(gca,'xlim',[0 nparam+1])
|
|
set(gca,'xticklabel','')
|
|
dy = get(gca,'ylim');
|
|
for ip=1:nparam
|
|
text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
|
end
|
|
if advanced
|
|
legend('Moments','Model','LRE model','Location','Best')
|
|
end
|
|
title('MC mean of sensitivity measures')
|
|
dyn_saveas(hh,[ IdentifDirectoryName '/' M_.fname '_MC_sensitivity' ],options_.nodisplay,options_.graph_format);
|
|
if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
|
|
fidTeX = fopen([ IdentifDirectoryName '/' M_.fname '_MC_sensitivity.tex'],'w');
|
|
fprintf(fidTeX,'%% TeX eps-loader file generated by plot_identification.m (Dynare).\n');
|
|
fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
|
|
fprintf(fidTeX,'\\begin{figure}[H]\n');
|
|
fprintf(fidTeX,'\\centering \n');
|
|
fprintf(fidTeX,'\\includegraphics[width=0.8\\textwidth]{%s_MC_sensitivity}\n',[IdentifDirectoryName '/' M_.fname]);
|
|
fprintf(fidTeX,'\\caption{MC mean of sensitivity measures}');
|
|
fprintf(fidTeX,'\\label{Fig:_MC_sensitivity}\n');
|
|
fprintf(fidTeX,'\\end{figure}\n\n');
|
|
fprintf(fidTeX,'%% End Of TeX file. \n');
|
|
fclose(fidTeX);
|
|
end
|
|
|
|
if advanced
|
|
if ~options_.nodisplay,
|
|
skipline()
|
|
disp('Displaying advanced diagnostics')
|
|
end
|
|
% options_.nograph=1;
|
|
hh = dyn_figure(options_.nodisplay,'Name','MC Condition Number');
|
|
subplot(221)
|
|
hist(log10(idemodel.cond))
|
|
title('log10 of Condition number in the model')
|
|
subplot(222)
|
|
hist(log10(idemoments.cond))
|
|
title('log10 of Condition number in the moments')
|
|
subplot(223)
|
|
hist(log10(idelre.cond))
|
|
title('log10 of Condition number in the LRE model')
|
|
dyn_saveas(hh,[IdentifDirectoryName '/' M_.fname '_ident_COND' ],options_.nodisplay,options_.graph_format);
|
|
options_mcf.pvalue_ks = 0.1;
|
|
options_mcf.pvalue_corr = 0.001;
|
|
options_mcf.alpha2 = 0;
|
|
options_mcf.param_names = name;
|
|
options_mcf.param_names_tex = name_tex;
|
|
options_mcf.fname_ = M_.fname;
|
|
options_mcf.OutputDirectoryName = IdentifDirectoryName;
|
|
options_mcf.beha_title = 'LOW condition nbr';
|
|
options_mcf.nobeha_title = 'HIGH condition nbr';
|
|
options_mcf.amcf_name = 'MC_HighestCondNumberLRE';
|
|
options_mcf.amcf_title = 'MC Highest Condition Number LRE Model';
|
|
options_mcf.title = 'MC Highest Condition Number LRE Model';
|
|
ncut=floor(SampleSize/10*9);
|
|
[dum,is]=sort(idelre.cond);
|
|
mcf_analysis(params, is(1:ncut), is(ncut+1:end), options_mcf, options_);
|
|
options_mcf.amcf_name = 'MC_HighestCondNumberModel';
|
|
options_mcf.amcf_title = 'MC Highest Condition Number Model Solution';
|
|
options_mcf.title = 'MC Highest Condition Number Model Solution';
|
|
[dum,is]=sort(idemodel.cond);
|
|
mcf_analysis(params, is(1:ncut), is(ncut+1:end), options_mcf, options_);
|
|
options_mcf.amcf_name = 'MC_HighestCondNumberMoments';
|
|
options_mcf.amcf_title = 'MC Highest Condition Number Model Moments';
|
|
options_mcf.title = 'MC Highest Condition Number Model Moments';
|
|
[dum,is]=sort(idemoments.cond);
|
|
mcf_analysis(params, is(1:ncut), is(ncut+1:end), options_mcf, options_);
|
|
% [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']);
|
|
tex_tit_1=[tittxt,' - MC Identification patterns (moments): HIGHEST SV'];
|
|
else
|
|
f1 = dyn_figure(options_.nodisplay,'Name',[tittxt,' - MC Identification patterns (moments): SMALLEST SV']);
|
|
tex_tit_1=[tittxt,' - MC Identification patterns (moments): SMALLEST SV'];
|
|
f2 = dyn_figure(options_.nodisplay,'Name',[tittxt,' - MC Identification patterns (moments): HIGHEST SV']);
|
|
tex_tit_2=[tittxt,' - MC Identification patterns (moments): HIGHEST SV'];
|
|
end
|
|
nplots=min(nparam,8);
|
|
if nplots>4
|
|
nsubplo=ceil(nplots/2);
|
|
else
|
|
nsubplo=nplots;
|
|
end
|
|
for j=1:nplots
|
|
if (nparam>4 && j<=ceil(nplots/2)) || nparam<5
|
|
set(0,'CurrentFigure',f1),
|
|
jj=j;
|
|
VVV=squeeze(abs(idemoments.V(:,:,end-j+1)));
|
|
SSS = idemoments.S(:,end-j+1);
|
|
else
|
|
set(0,'CurrentFigure',f2),
|
|
jj=j-ceil(nplots/2);
|
|
VVV=squeeze(abs(idemoments.V(:,:,jj)));
|
|
SSS = idemoments.S(:,jj);
|
|
end
|
|
subplot(nsubplo,1,jj)
|
|
for i=1:nparam
|
|
[post_mean, post_median(:,i), post_var, hpd_interval(i,:), post_deciles] = posterior_moments(VVV(:,i),0,0.9);
|
|
end
|
|
bar(post_median)
|
|
hold on, plot(hpd_interval,'--*r'),
|
|
Stit=mean(SSS);
|
|
|
|
set(gca,'xticklabel','')
|
|
if j==4 || j==nparam || j==8
|
|
for ip=1:nparam
|
|
text(ip,-0.02,name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
|
end
|
|
end
|
|
title(['MEAN Singular value ',num2str(Stit)])
|
|
end
|
|
dyn_saveas(f1,[IdentifDirectoryName '/' M_.fname '_MC_ident_pattern_1' ],options_.nodisplay,options_.graph_format);
|
|
if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
|
|
fidTeX = fopen([IdentifDirectoryName '/' M_.fname '_MC_ident_pattern_1.tex'],'w');
|
|
fprintf(fidTeX,'%% TeX eps-loader file generated by plot_identification.m (Dynare).\n');
|
|
fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
|
|
fprintf(fidTeX,'\\begin{figure}[H]\n');
|
|
fprintf(fidTeX,'\\centering \n');
|
|
fprintf(fidTeX,'\\includegraphics[width=0.8\\textwidth]{%s_MC_ident_pattern_1}\n',[IdentifDirectoryName '/' M_.fname]);
|
|
fprintf(fidTeX,'\\caption{%s.}',tex_tit_1);
|
|
fprintf(fidTeX,'\\label{Fig:MC_ident_pattern:1}\n');
|
|
fprintf(fidTeX,'\\end{figure}\n\n');
|
|
fprintf(fidTeX,'%% End Of TeX file. \n');
|
|
fclose(fidTeX);
|
|
end
|
|
if nparam>4
|
|
dyn_saveas(f2,[ IdentifDirectoryName '/' M_.fname '_MC_ident_pattern_2' ],options_.nodisplay,options_.graph_format);
|
|
if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
|
|
fidTeX = fopen([ IdentifDirectoryName '/' M_.fname '_MC_ident_pattern_2.tex'],'w');
|
|
fprintf(fidTeX,'%% TeX eps-loader file generated by plot_identification.m (Dynare).\n');
|
|
fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
|
|
fprintf(fidTeX,'\\begin{figure}[H]\n');
|
|
fprintf(fidTeX,'\\centering \n');
|
|
fprintf(fidTeX,'\\includegraphics[width=0.8\\textwidth]{%s_MC_ident_pattern_2}\n',[IdentifDirectoryName '/' M_.fname]);
|
|
fprintf(fidTeX,'\\caption{%s.}',tex_tit_2);
|
|
fprintf(fidTeX,'\\label{Fig:MC_ident_pattern:2}\n');
|
|
fprintf(fidTeX,'\\end{figure}\n\n');
|
|
fprintf(fidTeX,'%% End Of TeX file. \n');
|
|
fclose(fidTeX);
|
|
end
|
|
end
|
|
end
|
|
end
|