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improved identification checks, using svd in place of eig.

time-shift
Marco Ratto 2010-09-29 16:18:57 +02:00
parent 70400e5e84
commit 1fc3d503ee
1 changed files with 83 additions and 62 deletions
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145
matlab/identification_checks.m
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@ -1,4 +1,4 @@
function [McoH, McoJ, McoGP, PcoH, PcoJ, PcoGP, condH, condJ, condGP, eH, eJ, eGP, ind01, ind02, indnoH, indnoJ] = identification_checks(H,JJ, gp, bayestopt_)
function [McoH, McoJ, McoGP, PcoH, PcoJ, PcoGP, condH, condJ, condGP, eH, eJ, eGP, ind01, ind02, indnoH, indnoJ, ixnoH, ixnoJ] = identification_checks(H,JJ, gp, bayestopt_)
% Copyright (C) 2008 Dynare Team
%
@ -23,36 +23,42 @@ function [McoH, McoJ, McoGP, PcoH, PcoJ, PcoGP, condH, condJ, condGP, eH, eJ, eG
% 1. check rank of H at theta
npar = size(H,2);
npar0 = size(gp,2);
indnoH = {};
indnoJ = {};
indnoLRE = {};
ind1 = find(vnorm(H)~=0);
indnoH = zeros(1,npar);
indnoJ = zeros(1,npar);
indnoLRE = zeros(1,npar0);
ind1 = find(vnorm(H)>=eps);
H1 = H(:,ind1);
covH = H1'*H1;
sdH = sqrt(diag(covH));
sdH = sdH*sdH';
[e1,e2] = eig( (H1'*H1)./sdH );
% [e1,e2] = eig( (H1'*H1)./sdH );
[eu,e2,e1] = svd( H1, 0 );
eH = zeros(npar,npar);
% eH(ind1,:) = e1;
eH(ind1,length(find(vnorm(H)==0))+1:end) = e1;
eH(find(vnorm(H)==0),1:length(find(vnorm(H)==0)))=eye(length(find(vnorm(H)==0)));
condH = cond(H1);
% condH = cond(H1'*H1);
condHH = cond(H1'*H1);
rankH = rank(H);
rankHH = rank(H'*H);
ind2 = find(vnorm(JJ)~=0);
ind2 = find(vnorm(JJ)>=eps);
JJ1 = JJ(:,ind2);
covJJ = JJ1'*JJ1;
sdJJ = sqrt(diag(covJJ));
sdJJ = sdJJ*sdJJ';
[ee1,ee2] = eig( (JJ1'*JJ1)./sdJJ );
% [ee1,ee2] = eig( (JJ1'*JJ1)./sdJJ );
[eu,ee2,ee1] = svd( JJ1, 0 );
% eJ = NaN(npar,length(ind2));
eJ = zeros(npar,npar);
eJ(ind2,length(find(vnorm(JJ)==0))+1:end) = ee1;
eJ(find(vnorm(JJ)==0),1:length(find(vnorm(JJ)==0)))=eye(length(find(vnorm(JJ)==0)));
% condJ = cond(JJ1'*JJ1);
condJJ = cond(JJ1'*JJ1);
condJ = cond(JJ1);
rankJJ = rank(JJ'*JJ);
rankJ = rank(JJ);
ind3 = find(vnorm(gp)~=0);
ind3 = find(vnorm(gp)>=eps);
gp1 = gp(:,ind3);
covgp = gp1'*gp1;
sdgp = sqrt(diag(covgp));
@ -65,54 +71,11 @@ eGP(find(vnorm(gp)==0),1:length(find(vnorm(gp)==0)))=eye(length(find(vnorm(gp)==
% condJ = cond(JJ1'*JJ1);
condGP = cond(gp1);
if rank(H)<npar
ixno = 0;
% - find out which parameters are involved,
% using something like the vnorm and the eigenvalue decomposition of H;
% disp('Some parameters are NOT identified in the model: H rank deficient')
% disp(' ')
if length(ind1)<npar,
ixno = ixno + 1;
indnoH(ixno) = {find(~ismember([1:npar],ind1))};
% disp('Not identified params')
% disp(bayestopt_.name(indnoH{1}))
% disp(' ')
end
e0 = find(abs(diag(e2))<eps);
for j=1:length(e0),
ixno = ixno + 1;
indnoH(ixno) = {ind1(find(e1(:,e0(j))))};
% disp('Perfectly collinear parameters')
% disp(bayestopt_.name(indnoH{ixno}))
% disp(' ')
end
else % rank(H)==length(theta), go to 2
% 2. check rank of J
% disp('All parameters are identified at theta in the model (rank of H)')
% disp(' ')
end
if rank(JJ)<npar
ixno = 0;
% - find out which parameters are involved
% disp('Some parameters are NOT identified by the moments included in J')
% disp(' ')
if length(ind2)<npar,
ixno = ixno + 1;
indnoJ(ixno) = {find(~ismember([1:npar],ind2))};
end
ee0 = find(abs(diag(ee2))<eps);
for j=1:length(ee0),
ixno = ixno + 1;
indnoJ(ixno) = {ind2(find(ee1(:,ee0(j))))};
% disp('Perfectly collinear parameters in moments J')
% disp(bayestopt_.name(indnoJ{ixno}))
% disp(' ')
end
else %rank(J)==length(theta) =>
% disp('All parameters are identified at theta by the moments included in J')
end
ind01 = zeros(npar,1);
ind02 = zeros(npar,1);
ind01(ind1) = 1;
ind02(ind2) = 1;
% rank(H1)==size(H1,2)
% rank(JJ1)==size(JJ1,2)
@ -135,6 +98,64 @@ end
% format long % some are nearly 1
% McoJ
ixno = 0;
if rankH<npar | rankHH<npar | min(1-McoH)<1.e-10
% - find out which parameters are involved,
% using something like the vnorm and the eigenvalue decomposition of H;
% disp('Some parameters are NOT identified in the model: H rank deficient')
% disp(' ')
if length(ind1)<npar,
ixno = ixno + 1;
% indnoH(ixno) = {find(~ismember([1:npar],ind1))};
indnoH(ixno,:) = (~ismember([1:npar],ind1));
% disp('Not identified params')
% disp(bayestopt_.name(indnoH{1}))
% disp(' ')
end
e0 = [rankHH+1:length(ind1)];
for j=1:length(e0),
ixno = ixno + 1;
% indnoH(ixno) = {ind1(find(abs(e1(:,e0(j)))) > 1.e-6 )};
indnoH(ixno,:) = (abs(e1(:,e0(j))) > 1.e-6 )';
% disp('Perfectly collinear parameters')
% disp(bayestopt_.name(indnoH{ixno}))
% disp(' ')
% ind01(indnoH{ixno})=0;
end
else % rank(H)==length(theta), go to 2
% 2. check rank of J
% disp('All parameters are identified at theta in the model (rank of H)')
% disp(' ')
end
ixnoH=ixno;
ixno = 0;
if rankJ<npar | rankJJ<npar | min(1-McoJ)<1.e-10
% - find out which parameters are involved
% disp('Some parameters are NOT identified by the moments included in J')
% disp(' ')
if length(ind2)<npar,
ixno = ixno + 1;
% indnoJ(ixno) = {find(~ismember([1:npar],ind2))};
indnoJ(ixno,:) = (~ismember([1:npar],ind2));
end
ee0 = [rankJJ+1:length(ind2)];
if isempty(ee0),
cccc=0';
end
for j=1:length(ee0),
ixno = ixno + 1;
% indnoJ(ixno) = {ind2( find(abs(ee1(:,ee0(j))) > 1.e-6) )};
indnoJ(ixno,:) = (abs(ee1(:,ee0(j))) > 1.e-6)';
% disp('Perfectly collinear parameters in moments J')
% disp(bayestopt_.name(indnoJ{ixno}))
% disp(' ')
% ind02(indnoJ{ixno})=0;
end
else %rank(J)==length(theta) =>
% disp('All parameters are identified at theta by the moments included in J')
end
ixnoJ=ixno;
% here there is no exact linear dependence, but there are several
% near-dependencies, mostly due to strong pairwise colliniearities, which can
@ -168,10 +189,10 @@ for ii = 1:size(gp1,2);
end
ind01 = zeros(npar,1);
ind02 = zeros(npar,1);
ind01(ind1) = 1;
ind02(ind2) = 1;
% ind01 = zeros(npar,1);
% ind02 = zeros(npar,1);
% ind01(ind1) = 1;
% ind02(ind2) = 1;