108 lines
3.9 KiB
Matlab
108 lines
3.9 KiB
Matlab
function disp_identification(pdraws, idemodel, idemoments, disp_pcorr)
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% Copyright (C) 2008 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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global bayestopt_
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if nargin<4 | isempty(disp_pcorr),
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disp_pcorr=0;
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end
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[SampleSize, npar] = size(pdraws);
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jok = 0;
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jokP = 0;
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jokJ = 0;
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jokPJ = 0;
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if ~any(any(idemodel.ind==0))
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disp(['All parameters are identified in the model in the MC sample (rank of H).' ]),
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disp(' ')
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end
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if ~any(any(idemoments.ind==0))
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disp(['All parameters are identified by J moments in the MC sample (rank of J)' ]),
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end
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for j=1:npar,
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if any(idemodel.ind(j,:)==0),
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pno = 100*length(find(idemodel.ind(j,:)==0))/SampleSize;
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disp(['Parameter ',bayestopt_.name{j},' is not identified in the model for ',num2str(pno),'% of MC runs!' ])
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disp(' ')
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end
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if any(idemoments.ind(j,:)==0),
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pno = 100*length(find(idemoments.ind(j,:)==0))/SampleSize;
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disp(['Parameter ',bayestopt_.name{j},' is not identified by J moments for ',num2str(pno),'% of MC runs!' ])
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disp(' ')
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end
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if any(idemodel.ind(j,:)==1),
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iok = find(idemodel.ind(j,:)==1);
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jok = jok+1;
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kok(jok) = j;
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mmin(jok,1) = min(idemodel.Mco(j,iok));
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mmean(jok,1) = mean(idemodel.Mco(j,iok));
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mmax(jok,1) = max(idemodel.Mco(j,iok));
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[ipmax, jpmax] = find(abs(squeeze(idemodel.Pco(j,[1:j-1,j+1:end],iok)))>0.95);
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if ~isempty(ipmax)
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jokP = jokP+1;
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kokP(jokP) = j;
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ipmax(find(ipmax>=j))=ipmax(find(ipmax>=j))+1;
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[N,X]=hist(ipmax,[1:npar]);
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jpM(jokP)={find(N)};
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NPM(jokP)={N(find(N))./SampleSize.*100};
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pmeanM(jokP)={mean(squeeze(idemodel.Pco(j,find(N),iok))')};
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pminM(jokP)={min(squeeze(idemodel.Pco(j,find(N),iok))')};
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pmaxM(jokP)={max(squeeze(idemodel.Pco(j,find(N),iok))')};
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end
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end
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if any(idemoments.ind(j,:)==1),
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iok = find(idemoments.ind(j,:)==1);
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jokJ = jokJ+1;
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kokJ(jokJ) = j;
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mminJ(jokJ,1) = min(idemoments.Mco(j,iok));
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mmeanJ(jokJ,1) = mean(idemoments.Mco(j,iok));
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mmaxJ(jokJ,1) = max(idemoments.Mco(j,iok));
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[ipmax, jpmax] = find(abs(squeeze(idemoments.Pco(j,[1:j-1,j+1:end],iok)))>0.95);
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if ~isempty(ipmax)
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jokPJ = jokPJ+1;
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kokPJ(jokPJ) = j;
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ipmax(find(ipmax>=j))=ipmax(find(ipmax>=j))+1;
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[N,X]=hist(ipmax,[1:npar]);
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jpJ(jokPJ)={find(N)};
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NPJ(jokPJ)={N(find(N))./SampleSize.*100};
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pmeanJ(jokPJ)={mean(squeeze(idemoments.Pco(j,find(N),iok))')};
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pminJ(jokPJ)={min(squeeze(idemoments.Pco(j,find(N),iok))')};
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pmaxJ(jokPJ)={max(squeeze(idemoments.Pco(j,find(N),iok))')};
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end
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end
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end
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dyntable('Multi collinearity in the model:',strvcat('param','min','mean','max'), ...
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strvcat(bayestopt_.name(kok)),[mmin, mmean, mmax],10,10,6);
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dyntable('Multi collinearity for moments in J:',strvcat('param','min','mean','max'), ...
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strvcat(bayestopt_.name(kokJ)),[mminJ, mmeanJ, mmaxJ],10,10,6);
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if disp_pcorr,
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for j=1:length(kokP),
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dyntable([bayestopt_.name{kokP(j)},' pairwise correlations in the model'],strvcat(' ','min','mean','max'), ...
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strvcat(bayestopt_.name{jpM{j}}),[pminM{j}' pmeanM{j}' pmaxM{j}'],10,10,3);
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end
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for j=1:length(kokPJ),
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dyntable([bayestopt_.name{kokPJ(j)},' pairwise correlations in J moments'],strvcat(' ','min','mean','max'), ...
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strvcat(bayestopt_.name{jpJ{j}}),[pminJ{j}' pmeanJ{j}' pmaxJ{j}'],10,10,3);
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end
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end
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