/usr/share/dynare/matlab/disp_identification.m is in dynare-common 4.4.1-1build1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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% Copyright (C) 2008-2012 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
global options_
if nargin < 5 || isempty(advanced),
advanced=0;
end
[SampleSize, npar] = size(pdraws);
% jok = 0;
% jokP = 0;
% jokJ = 0;
% jokPJ = 0;
% for j=1:npar,
% % if any(idemodel.ind(j,:)==0),
% % pno = 100*length(find(idemodel.ind(j,:)==0))/SampleSize;
% % disp(['Parameter ',name{j},' is not identified in the model for ',num2str(pno),'% of MC runs!' ])
% % disp(' ')
% % end
% % if any(idemoments.ind(j,:)==0),
% % pno = 100*length(find(idemoments.ind(j,:)==0))/SampleSize;
% % disp(['Parameter ',name{j},' is not identified by J moments for ',num2str(pno),'% of MC runs!' ])
% % disp(' ')
% % end
% if any(idemodel.ind(j,:)==1),
% iok = find(idemodel.ind(j,:)==1);
% jok = jok+1;
% kok(jok) = j;
% mmin(jok,1) = min(idemodel.Mco(j,iok));
% mmean(jok,1) = mean(idemodel.Mco(j,iok));
% mmax(jok,1) = max(idemodel.Mco(j,iok));
% [ipmax, jpmax] = find(abs(squeeze(idemodel.Pco(j,[1:j-1,j+1:end],iok)))>0.95);
% if ~isempty(ipmax)
% jokP = jokP+1;
% kokP(jokP) = j;
% ipmax(find(ipmax>=j))=ipmax(find(ipmax>=j))+1;
% [N,X]=hist(ipmax,[1:npar]);
% jpM(jokP)={find(N)};
% NPM(jokP)={N(find(N))./SampleSize.*100};
% pmeanM(jokP)={mean(squeeze(idemodel.Pco(j,find(N),iok))')};
% pminM(jokP)={min(squeeze(idemodel.Pco(j,find(N),iok))')};
% pmaxM(jokP)={max(squeeze(idemodel.Pco(j,find(N),iok))')};
% end
% end
% if any(idemoments.ind(j,:)==1),
% iok = find(idemoments.ind(j,:)==1);
% jokJ = jokJ+1;
% kokJ(jokJ) = j;
% mminJ(jokJ,1) = min(idemoments.Mco(j,iok));
% mmeanJ(jokJ,1) = mean(idemoments.Mco(j,iok));
% mmaxJ(jokJ,1) = max(idemoments.Mco(j,iok));
% [ipmax, jpmax] = find(abs(squeeze(idemoments.Pco(j,[1:j-1,j+1:end],iok)))>0.95);
% if ~isempty(ipmax)
% jokPJ = jokPJ+1;
% kokPJ(jokPJ) = j;
% ipmax(find(ipmax>=j))=ipmax(find(ipmax>=j))+1;
% [N,X]=hist(ipmax,[1:npar]);
% jpJ(jokPJ)={find(N)};
% NPJ(jokPJ)={N(find(N))./SampleSize.*100};
% pmeanJ(jokPJ)={mean(squeeze(idemoments.Pco(j,find(N),iok))')};
% pminJ(jokPJ)={min(squeeze(idemoments.Pco(j,find(N),iok))')};
% pmaxJ(jokPJ)={max(squeeze(idemoments.Pco(j,find(N),iok))')};
% end
% end
% end
disp([' ']),
if any(idemodel.ino),
disp('WARNING !!!')
if SampleSize>1,
disp(['The rank of H (model) is deficient for ', num2str(length(find(idemodel.ino))),' out of ',int2str(SampleSize),' MC runs!' ]),
else
disp(['The rank of H (model) is deficient!' ]),
end
skipline()
for j=1:npar,
if any(idemodel.ind0(:,j)==0),
pno = 100*length(find(idemodel.ind0(:,j)==0))/SampleSize;
if SampleSize>1
disp([' ',name{j},' is not identified in the model for ',num2str(pno),'% of MC runs!' ])
else
disp([' ',name{j},' is not identified in the model!' ])
end
disp([' [dJ/d(',name{j},')=0 for all tau elements in the model solution!]' ])
end
end
npairs=size(idemodel.jweak_pair,2);
jmap_pair=dyn_unvech(1:npairs);
jstore=[];
skipline()
for j=1:npairs,
iweak = length(find(idemodel.jweak_pair(:,j)));
if iweak,
[jx,jy]=find(jmap_pair==j);
jstore=[jstore jx(1) jy(1)];
if SampleSize > 1
disp([' [',name{jx(1)},',',name{jy(1)},'] are PAIRWISE collinear (with tol = 1.e-10) for ',num2str((iweak)/SampleSize*100),'% of MC runs!' ])
else
disp([' [',name{jx(1)},',',name{jy(1)},'] are PAIRWISE collinear (with tol = 1.e-10) !' ])
end
end
end
skipline()
for j=1:npar,
iweak = length(find(idemodel.jweak(:,j)));
if iweak && ~ismember(j,jstore),
% disp('WARNING !!!')
% disp(['Model derivatives of parameter ',name{j},' are multi-collinear (with tol = 1.e-10) for ',num2str(iweak/SampleSize*100),'% of MC runs!' ])
if SampleSize>1
disp([name{j},' is collinear w.r.t. all other params ',num2str(iweak/SampleSize*100),'% of MC runs!' ])
else
disp([name{j},' is collinear w.r.t. all other params!' ])
end
end
end
% if npar>(j+1),
% [ipair, jpair] = find(squeeze(idemodel.Pco(j,j+1:end,:))'>(1-1.e-10));
% else
% [ipair, jpair] = find(squeeze(idemodel.Pco(j,j+1:end,:))>(1-1.e-10));
% end
% if ~isempty(jpair),
% for jx=j+1:npar,
% ixp = find(jx==(jpair+j));
% if ~isempty(ixp)
% if SampleSize > 1,
% disp([' [',name{j},',',name{jx},'] are PAIRWISE collinear (with tol = 1.e-10) for ',num2str(length(ixp)/SampleSize*100),'% of MC runs!' ])
% else
% disp([' [',name{j},',',name{jx},'] are PAIRWISE collinear (with tol = 1.e-10)!' ])
% end
% end
% end
% end
end
if ~any(idemodel.ino) && ~any(any(idemodel.ind0==0))
disp(['All parameters are identified in the model (rank of H).' ]),
skipline()
end
if any(idemoments.ino),
skipline()
disp('WARNING !!!')
if SampleSize > 1,
disp(['The rank of J (moments) is deficient for ', num2str(length(find(idemoments.ino))),' out of ',int2str(SampleSize),' MC runs!' ]),
else
disp(['The rank of J (moments) is deficient!' ]),
end
% disp('WARNING !!!')
% disp(['The rank of J (moments) is deficient for ', num2str(length(find(idemoments.ino))/SampleSize*100),'% of MC runs!' ]),
% indno=[];
% for j=1:SampleSize, indno=[indno;idemoments.indno{j}]; end
% freqno = mean(indno)*100;
% ifreq=find(freqno);
% disp('MOMENT RANK FAILURE DUE TO COLLINEARITY OF PARAMETERS:');
skipline()
for j=1:npar,
if any(idemoments.ind0(:,j)==0),
pno = 100*length(find(idemoments.ind0(:,j)==0))/SampleSize;
if SampleSize > 1
disp([' ',name{j},' is not identified by J moments for ',num2str(pno),'% of MC runs!' ])
else
disp([' ',name{j},' is not identified by J moments!' ])
end
disp([' [dJ/d(',name{j},')=0 for all J moments!]' ])
end
end
skipline()
npairs=size(idemoments.jweak_pair,2);
jmap_pair=dyn_unvech(1:npairs);
jstore=[];
for j=1:npairs,
iweak = length(find(idemoments.jweak_pair(:,j)));
if iweak,
[jx,jy]=find(jmap_pair==j);
jstore=[jstore' jx(1) jy(1)]';
if SampleSize > 1
disp([' [',name{jx(1)},',',name{jy(1)},'] are PAIRWISE collinear (with tol = 1.e-10) for ',num2str((iweak)/SampleSize*100),'% of MC runs!' ])
else
disp([' [',name{jx(1)},',',name{jy(1)},'] are PAIRWISE collinear (with tol = 1.e-10) !' ])
end
end
end
skipline()
for j=1:npar,
iweak = length(find(idemoments.jweak(:,j)));
if iweak && ~ismember(j,jstore),
% disp('WARNING !!!')
% disp(['Moment derivatives of parameter ',name{j},' are multi-collinear (with tol = 1.e-10) for ',num2str(iweak/SampleSize*100),'% of MC runs!' ])
if SampleSize > 1,
disp([name{j},' is collinear w.r.t. all other params ',num2str(iweak/SampleSize*100),'% of MC runs!' ])
else
disp([name{j},' is collinear w.r.t. all other params!' ])
end
end
end
% if npar>(j+1),
% [ipair, jpair] = find(squeeze(idemoments.Pco(j,j+1:end,:))'>(1-1.e-10));
% else
% [ipair, jpair] = find(squeeze(idemoments.Pco(j,j+1:end,:))>(1-1.e-10));
% end
% if ~isempty(jpair),
% for jx=j+1:npar,
% ixp = find(jx==(jpair+j));
% if ~isempty(ixp)
% if SampleSize > 1
% disp([' [',name{j},',',name{jx},'] are PAIRWISE collinear (with tol = 1.e-10) for ',num2str(length(ixp)/SampleSize*100),'% of MC runs!' ])
% else
% disp([' [',name{j},',',name{jx},'] are PAIRWISE collinear (with tol = 1.e-10) !' ])
% end
% end
% end
% end
% end
% end
end
if ~any(idemoments.ino) && ~any(any(idemoments.ind0==0))
skipline()
disp(['All parameters are identified by J moments (rank of J)' ]),
skipline()
end
% if ~ options_.noprint && advanced,
% disp('Press KEY to continue with identification analysis')
% pause;
% dyntable('Multi collinearity in the model:',char('param','min','mean','max'), ...
% char(name(kok)),[mmin, mmean, mmax],10,10,6);
% disp(' ')
% dyntable('Multi collinearity for moments in J:',char('param','min','mean','max'), ...
% char(name(kokJ)),[mminJ, mmeanJ, mmaxJ],10,10,6);
% disp(' ')
% end
% if advanced && (~options_.noprint),
% for j=1:length(kokP),
% dyntable([name{kokP(j)},' pairwise correlations in the model'],char(' ','min','mean','max'), ...
% char(name{jpM{j}}),[pminM{j}' pmeanM{j}' pmaxM{j}'],10,10,3);
% end
%
% for j=1:length(kokPJ),
% dyntable([name{kokPJ(j)},' pairwise correlations in J moments'],char(' ','min','mean','max'), ...
% char(name{jpJ{j}}),[pminJ{j}' pmeanJ{j}' pmaxJ{j}'],10,10,3);
% end
% end
% disp(' ')
% identificaton patterns
if SampleSize==1 && advanced,
skipline()
disp('Press ENTER to print advanced diagnostics'), pause(5),
for j=1:size(idemoments.cosnJ,2),
pax=NaN(npar,npar);
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:npar,
namx='';
for in=1:j,
dumpindx = idemoments.pars{i,j}(in);
if isnan(dumpindx),
namx=[namx ' ' sprintf('%-15s','--')];
else
namx=[namx ' ' sprintf('%-15s',name{dumpindx})];
pax(i,dumpindx)=idemoments.cosnJ(i,j);
end
end
fprintf('%-15s [%s] %14.7f\n',name{i},namx,idemoments.cosnJ(i,j))
end
end
end
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