292 lines
11 KiB
Matlab
292 lines
11 KiB
Matlab
function disp_identification(pdraws, idemodel, idemoments, name, advanced)
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% Copyright (C) 2008-2012 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 options_
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if nargin < 5 || isempty(advanced),
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advanced=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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% 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 ',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 ',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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disp([' ']),
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if any(idemodel.ino),
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disp('WARNING !!!')
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if SampleSize>1,
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disp(['The rank of H (model) is deficient for ', num2str(length(find(idemodel.ino))),' out of ',int2str(SampleSize),' MC runs!' ]),
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else
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disp(['The rank of H (model) is deficient!' ]),
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end
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disp(' ')
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for j=1:npar,
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if any(idemodel.ind0(:,j)==0),
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pno = 100*length(find(idemodel.ind0(:,j)==0))/SampleSize;
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if SampleSize>1
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disp([' ',name{j},' is not identified in the model for ',num2str(pno),'% of MC runs!' ])
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else
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disp([' ',name{j},' is not identified in the model!' ])
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end
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disp([' [dJ/d(',name{j},')=0 for all tau elements in the model solution!]' ])
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end
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end
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npairs=size(idemodel.jweak_pair,2);
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jmap_pair=dyn_unvech(1:npairs);
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jstore=[];
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disp(' ')
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for j=1:npairs,
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iweak = length(find(idemodel.jweak_pair(:,j)));
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if iweak,
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[jx,jy]=find(jmap_pair==j);
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jstore=[jstore jx(1) jy(1)];
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if SampleSize > 1
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disp([' [',name{jx(1)},',',name{jy(1)},'] are PAIRWISE collinear (with tol = 1.e-10) for ',num2str((iweak)/SampleSize*100),'% of MC runs!' ])
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else
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disp([' [',name{jx(1)},',',name{jy(1)},'] are PAIRWISE collinear (with tol = 1.e-10) !' ])
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end
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end
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end
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disp(' ')
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for j=1:npar,
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iweak = length(find(idemodel.jweak(:,j)));
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if iweak && ~ismember(j,jstore),
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% disp('WARNING !!!')
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% disp(['Model derivatives of parameter ',name{j},' are multi-collinear (with tol = 1.e-10) for ',num2str(iweak/SampleSize*100),'% of MC runs!' ])
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if SampleSize>1
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disp([name{j},' is collinear w.r.t. all other params ',num2str(iweak/SampleSize*100),'% of MC runs!' ])
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else
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disp([name{j},' is collinear w.r.t. all other params!' ])
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end
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end
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end
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% if npar>(j+1),
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% [ipair, jpair] = find(squeeze(idemodel.Pco(j,j+1:end,:))'>(1-1.e-10));
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% else
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% [ipair, jpair] = find(squeeze(idemodel.Pco(j,j+1:end,:))>(1-1.e-10));
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% end
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% if ~isempty(jpair),
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% for jx=j+1:npar,
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% ixp = find(jx==(jpair+j));
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% if ~isempty(ixp)
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% if SampleSize > 1,
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% disp([' [',name{j},',',name{jx},'] are PAIRWISE collinear (with tol = 1.e-10) for ',num2str(length(ixp)/SampleSize*100),'% of MC runs!' ])
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% else
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% disp([' [',name{j},',',name{jx},'] are PAIRWISE collinear (with tol = 1.e-10)!' ])
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% end
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% end
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% end
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% end
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end
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if ~any(idemodel.ino) && ~any(any(idemodel.ind0==0))
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disp(['All parameters are identified in the model (rank of H).' ]),
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disp(' ')
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end
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if any(idemoments.ino),
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disp(' ')
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disp('WARNING !!!')
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if SampleSize > 1,
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disp(['The rank of J (moments) is deficient for ', num2str(length(find(idemoments.ino))),' out of ',int2str(SampleSize),' MC runs!' ]),
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else
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disp(['The rank of J (moments) is deficient!' ]),
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end
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% disp('WARNING !!!')
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% disp(['The rank of J (moments) is deficient for ', num2str(length(find(idemoments.ino))/SampleSize*100),'% of MC runs!' ]),
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% indno=[];
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% for j=1:SampleSize, indno=[indno;idemoments.indno{j}]; end
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% freqno = mean(indno)*100;
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% ifreq=find(freqno);
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% disp('MOMENT RANK FAILURE DUE TO COLLINEARITY OF PARAMETERS:');
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disp(' ')
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for j=1:npar,
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if any(idemoments.ind0(:,j)==0),
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pno = 100*length(find(idemoments.ind0(:,j)==0))/SampleSize;
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if SampleSize > 1
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disp([' ',name{j},' is not identified by J moments for ',num2str(pno),'% of MC runs!' ])
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else
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disp([' ',name{j},' is not identified by J moments!' ])
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end
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disp([' [dJ/d(',name{j},')=0 for all J moments!]' ])
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end
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end
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disp(' ')
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npairs=size(idemoments.jweak_pair,2);
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jmap_pair=dyn_unvech(1:npairs);
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jstore=[];
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for j=1:npairs,
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iweak = length(find(idemoments.jweak_pair(:,j)));
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if iweak,
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[jx,jy]=find(jmap_pair==j);
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jstore=[jstore' jx(1) jy(1)]';
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if SampleSize > 1
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disp([' [',name{jx(1)},',',name{jy(1)},'] are PAIRWISE collinear (with tol = 1.e-10) for ',num2str((iweak)/SampleSize*100),'% of MC runs!' ])
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else
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disp([' [',name{jx(1)},',',name{jy(1)},'] are PAIRWISE collinear (with tol = 1.e-10) !' ])
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end
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end
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end
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disp(' ')
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for j=1:npar,
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iweak = length(find(idemoments.jweak(:,j)));
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if iweak && ~ismember(j,jstore),
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% disp('WARNING !!!')
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% disp(['Moment derivatives of parameter ',name{j},' are multi-collinear (with tol = 1.e-10) for ',num2str(iweak/SampleSize*100),'% of MC runs!' ])
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if SampleSize > 1,
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disp([name{j},' is collinear w.r.t. all other params ',num2str(iweak/SampleSize*100),'% of MC runs!' ])
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else
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disp([name{j},' is collinear w.r.t. all other params!' ])
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end
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end
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end
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% if npar>(j+1),
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% [ipair, jpair] = find(squeeze(idemoments.Pco(j,j+1:end,:))'>(1-1.e-10));
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% else
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% [ipair, jpair] = find(squeeze(idemoments.Pco(j,j+1:end,:))>(1-1.e-10));
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% end
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% if ~isempty(jpair),
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% for jx=j+1:npar,
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% ixp = find(jx==(jpair+j));
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% if ~isempty(ixp)
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% if SampleSize > 1
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% disp([' [',name{j},',',name{jx},'] are PAIRWISE collinear (with tol = 1.e-10) for ',num2str(length(ixp)/SampleSize*100),'% of MC runs!' ])
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% else
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% disp([' [',name{j},',',name{jx},'] are PAIRWISE collinear (with tol = 1.e-10) !' ])
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% end
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% end
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% end
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% end
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% end
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% end
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end
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if ~any(idemoments.ino) && ~any(any(idemoments.ind0==0))
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disp(' ')
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disp(['All parameters are identified by J moments (rank of J)' ]),
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disp(' ')
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end
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% if ~ options_.noprint && advanced,
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% disp('Press KEY to continue with identification analysis')
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% pause;
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% dyntable('Multi collinearity in the model:',char('param','min','mean','max'), ...
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% char(name(kok)),[mmin, mmean, mmax],10,10,6);
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% disp(' ')
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% dyntable('Multi collinearity for moments in J:',char('param','min','mean','max'), ...
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% char(name(kokJ)),[mminJ, mmeanJ, mmaxJ],10,10,6);
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% disp(' ')
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% end
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% if advanced && (~options_.noprint),
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% for j=1:length(kokP),
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% dyntable([name{kokP(j)},' pairwise correlations in the model'],char(' ','min','mean','max'), ...
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% char(name{jpM{j}}),[pminM{j}' pmeanM{j}' pmaxM{j}'],10,10,3);
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% end
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%
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% for j=1:length(kokPJ),
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% dyntable([name{kokPJ(j)},' pairwise correlations in J moments'],char(' ','min','mean','max'), ...
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% char(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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% disp(' ')
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% identificaton patterns
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if SampleSize==1 && advanced,
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disp(' ')
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disp('Press ENTER to print advanced diagnostics'), pause(5),
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for j=1:size(idemoments.cosnJ,2),
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pax=NaN(npar,npar);
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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:npar,
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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.cosnJ(i,j);
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end
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end
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fprintf('%-15s [%s] %14.7f\n',name{i},namx,idemoments.cosnJ(i,j))
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end
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end
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end
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