diff --git a/matlab/dynare_identification.m b/matlab/dynare_identification.m
index 263d347e8893ee98f6652eec479bf8572190656b..ea0047abfe7dfb613306f559defde1191932dbc4 100644
--- a/matlab/dynare_identification.m
+++ b/matlab/dynare_identification.m
@@ -1,4 +1,21 @@
function [pdraws, TAU, GAM, LRE, gp, H, JJ] = dynare_identification(options_ident, pdraws0)
+%function [pdraws, TAU, GAM, LRE, gp, H, JJ] = dynare_identification(options_ident, pdraws0)
+%
+% INPUTS
+% o options_ident [structure] identification options
+% o pdraws0 [matrix] optional: matrix of MC sample of model params.
+%
+% OUTPUTS
+% o pdraws [matrix] matrix of MC sample of model params used
+% o TAU, [matrix] MC sample of entries in the model solution (stacked vertically)
+% o GAM, [matrix] MC sample of entries in the moments (stacked vertically)
+% o LRE, [matrix] MC sample of entries in LRE model (stacked vertically)
+% o gp, [matrix] derivatives of the Jacobian (LRE model)
+% o H, [matrix] derivatives of the model solution
+% o JJ [matrix] derivatives of the moments
+%
+% SPECIAL REQUIREMENTS
+% None
% main
%
@@ -27,6 +44,7 @@ else
warning off MATLAB:dividebyzero
end
+fname_ = M_.fname;
options_ident = set_default_option(options_ident,'load_ident_files',0);
options_ident = set_default_option(options_ident,'useautocorr',0);
options_ident = set_default_option(options_ident,'ar',3);
@@ -44,7 +62,7 @@ if isempty(estim_params_),
prior_exist=0;
else
prior_exist=1;
-end
+end
iload = options_ident.load_ident_files;
advanced = options_ident.advanced;
@@ -59,8 +77,9 @@ options_.Schur_vec_tol = 1.e-8;
options_ = set_default_option(options_,'datafile',[]);
options_.mode_compute = 0;
-[data,rawdata]=dynare_estimation_init([],1);
-% computes a first linear solution to set up various variables
+options_.plot_priors = 0;
+[data,rawdata]=dynare_estimation_init([],fname_,1);
+
@@ -68,10 +87,12 @@ SampleSize = options_ident.prior_mc;
% results = prior_sampler(0,M_,bayestopt_,options_,oo_);
-if options_ident.prior_range
- prior_draw(1,1);
-else
- prior_draw(1);
+if prior_exist
+ if options_ident.prior_range
+ prior_draw(1,1);
+ else
+ prior_draw(1);
+ end
end
if ~(exist('sylvester3mr','file')==2),
@@ -112,7 +133,7 @@ disp(' ')
disp(['==== Identification analysis ====' ]),
disp(' ')
-if iload <=0,
+if iload <=0,
iteration = 0;
burnin_iteration = 0;
@@ -133,14 +154,18 @@ if iload <=0,
while iteration < SampleSize,
loop_indx = loop_indx+1;
if prior_exist,
- if nargin==2,
- if burnin_iteration>=BurninSampleSize,
- params = pdraws0(iteration+1,:);
+ if SampleSize==1,
+ params = set_prior(estim_params_,M_,options_)';
+ else
+ if nargin==2,
+ if burnin_iteration>=BurninSampleSize,
+ params = pdraws0(iteration+1,:);
+ else
+ params = pdraws0(burnin_iteration+1,:);
+ end
else
- params = pdraws0(burnin_iteration+1,:);
+ params = prior_draw();
end
- else
- params = prior_draw();
end
set_all_parameters(params);
else
@@ -151,11 +176,6 @@ if iload <=0,
if info(1)==0,
oo0=oo_;
- % [Aa,Bb] = kalman_transition_matrix(oo0.dr, ...
- % bayestopt_.restrict_var_list, ...
- % bayestopt_.restrict_columns, ...
- % bayestopt_.restrict_aux, M_.exo_nbr);
- % tau=[vec(Aa); dyn_vech(Bb*M_.Sigma_e*Bb')];
tau=[oo_.dr.ys(oo_.dr.order_var); vec(A); dyn_vech(B*M_.Sigma_e*B')];
yy0=oo_.dr.ys(I);
[residual, g1 ] = feval([M_.fname,'_dynamic'],yy0, oo_.exo_steady_state', M_.params,1);
@@ -164,7 +184,7 @@ if iload <=0,
burnin_iteration = burnin_iteration + 1;
pdraws(burnin_iteration,:) = params;
TAU(:,burnin_iteration)=tau;
- LRE(:,burnin_iteration)=[oo_.dr.ys(oo_.dr.order_var); vec(g1)];
+ LRE(:,burnin_iteration)=[oo_.dr.ys(oo_.dr.order_var); vec(g1)];
[gam,stationary_vars] = th_autocovariances(oo0.dr,bayestopt_.mfys,M_,options_);
if exist('OCTAVE_VERSION')
warning('off', 'Octave:divide-by-zero')
@@ -206,7 +226,7 @@ if iload <=0,
end
if iteration,
- [JJ, H, gam, gp] = getJJ(A, B, M_,oo0,options_,0,indx,indexo,bayestopt_.mf2,nlags,useautocorr);
+ [JJ, H, gam, gp] = getJJ(A, B, M_,oo0,options_,0,indx,indexo,bayestopt_.mf2,nlags,useautocorr);
if BurninSampleSize == 0,
indJJ = (find(max(abs(JJ'))>1.e-8));
indH = (find(max(abs(H'))>1.e-8));
@@ -239,24 +259,24 @@ if iload <=0,
% siH = abs(H(indH,:).*(ones(length(indH),1)*bayestopt_.p2'));
% siJ = abs(JJ(indJJ,:).*(1./mGAM'*bayestopt_.p2'));
% siH = abs(H(indH,:).*(1./mTAU'*bayestopt_.p2'));
-
+
siJnorm(iteration,:) = vnorm(siJ./repmat(GAM(:,iteration),1,nparam)).*params;
siHnorm(iteration,:) = vnorm(siH./repmat(TAU(:,iteration),1,nparam)).*params;
siLREnorm(iteration,:) = vnorm(siLRE./repmat(LRE(:,iteration),1,nparam-offset)).*params(offset+1:end);
if iteration ==1,
- siJmean = abs(siJ)./SampleSize;
- siHmean = abs(siH)./SampleSize;
- siLREmean = abs(siLRE)./SampleSize;
- derJmean = (siJ)./SampleSize;
- derHmean = (siH)./SampleSize;
- derLREmean = (siLRE)./SampleSize;
+ siJmean = abs(siJ)./SampleSize;
+ siHmean = abs(siH)./SampleSize;
+ siLREmean = abs(siLRE)./SampleSize;
+ derJmean = (siJ)./SampleSize;
+ derHmean = (siH)./SampleSize;
+ derLREmean = (siLRE)./SampleSize;
else
- siJmean = abs(siJ)./SampleSize+siJmean;
- siHmean = abs(siH)./SampleSize+siHmean;
- siLREmean = abs(siLRE)./SampleSize+siLREmean;
- derJmean = (siJ)./SampleSize+derJmean;
- derHmean = (siH)./SampleSize+derHmean;
- derLREmean = (siLRE)./SampleSize+derLREmean;
+ siJmean = abs(siJ)./SampleSize+siJmean;
+ siHmean = abs(siH)./SampleSize+siHmean;
+ siLREmean = abs(siLRE)./SampleSize+siLREmean;
+ derJmean = (siJ)./SampleSize+derJmean;
+ derHmean = (siH)./SampleSize+derHmean;
+ derLREmean = (siLRE)./SampleSize+derLREmean;
end
pdraws(iteration,:) = params;
normH = max(abs(stoH(:,:,run_index))')';
@@ -265,74 +285,108 @@ if iload <=0,
% normH = TAU(:,iteration);
% normJ = GAM(:,iteration);
% normLRE = LRE(:,iteration);
- [idemodel.Mco(:,iteration), idemoments.Mco(:,iteration), idelre.Mco(:,iteration), ...
- idemodel.Pco(:,:,iteration), idemoments.Pco(:,:,iteration), idelre.Pco(:,:,iteration), ...
- idemodel.cond(iteration), idemoments.cond(iteration), idelre.cond(iteration), ...
- idemodel.ee(:,:,iteration), idemoments.ee(:,:,iteration), idelre.ee(:,:,iteration), ...
- idemodel.ind(:,iteration), idemoments.ind(:,iteration), ...
- idemodel.indno{iteration}, idemoments.indno{iteration}, ...
- idemodel.ino(iteration), idemoments.ino(iteration)] = ...
- identification_checks(H(indH,:)./normH(:,ones(nparam,1)),JJ(indJJ,:)./normJ(:,ones(nparam,1)), gp(indLRE,:)./normLRE(:,ones(size(gp,2),1)), bayestopt_);
- % identification_checks(H(indH,:),JJ(indJJ,:), gp(indLRE,:), bayestopt_);
+ [idemodel.Mco(:,iteration), idemoments.Mco(:,iteration), idelre.Mco(:,iteration), ...
+ idemodel.Pco(:,:,iteration), idemoments.Pco(:,:,iteration), idelre.Pco(:,:,iteration), ...
+ idemodel.cond(iteration), idemoments.cond(iteration), idelre.cond(iteration), ...
+ idemodel.ee(:,:,iteration), idemoments.ee(:,:,iteration), idelre.ee(:,:,iteration), ...
+ idemodel.ind(:,iteration), idemoments.ind(:,iteration), ...
+ idemodel.indno{iteration}, idemoments.indno{iteration}, ...
+ idemodel.ino(iteration), idemoments.ino(iteration)] = ...
+ identification_checks(H(indH,:)./normH(:,ones(nparam,1)),JJ(indJJ,:)./normJ(:,ones(nparam,1)), gp(indLRE,:)./normLRE(:,ones(size(gp,2),1)));
+ % identification_checks(H(indH,:),JJ(indJJ,:), gp(indLRE,:), bayestopt_);
indok = find(max(idemoments.indno{iteration},[],1)==0);
ide_strength_J(iteration,:)=NaN(1,nparam);
- if iteration ==1,
+ if iteration ==1 && ~isempty(indok),
+ normaliz = abs(params);
+ if prior_exist,
+ if ~isempty(estim_params_.var_exo),
+ normaliz1 = estim_params_.var_exo(:,7); % normalize with prior standard deviation
+ else
+ normaliz1=[];
+ end
+ if ~isempty(estim_params_.param_vals),
+ normaliz1 = [normaliz1; estim_params_.param_vals(:,7)]'; % normalize with prior standard deviation
+ end
+ normaliz = max([normaliz; normaliz1]);
+ end
cmm = simulated_moment_uncertainty(indJJ, periods, replic);
+ % Jinv=(siJ(:,indok)'*siJ(:,indok))\siJ(:,indok)';
+ % MIM=inv(Jinv*cmm*Jinv');
+ MIM=siJ(:,indok)'*inv(cmm)*siJ(:,indok);
+ deltaM = sqrt(diag(MIM));
+ tildaM = MIM./((deltaM)*(deltaM'));
+ rhoM=sqrt(1-1./diag(inv(tildaM)));
+ deltaM = deltaM.*normaliz(indok)';
+ ide_strength_J(iteration,indok) = (1./(sqrt(diag(inv(MIM)))./normaliz(indok)'));
+
+ % indok = find(max(idemodel.indno{iteration},[],1)==0);
+ % ide_strength_H(iteration,:)=zeros(1,nparam);
+ % mim=inv(siH(:,indok)'*siH(:,indok))*siH(:,indok)';
+ % % mim=mim*diag(GAM(:,iteration))*mim';
+ % % MIM=inv(mim);
+ % mim=mim.*repmat(TAU(:,iteration),1,length(indok))';
+ % MIM=inv(mim*mim');
+ % deltaM = sqrt(diag(MIM));
+ % tildaM = MIM./((deltaM)*(deltaM'));
+ % rhoM=sqrt(1-1./diag(inv(tildaM)));
+ % deltaM = deltaM.*params(indok)';
+ % ide_strength_H(iteration,indok) = (1./[sqrt(diag(inv(MIM)))./params(indok)']);
+ normaliz(indok) = sqrt(diag(inv(MIM)))';
+ % inok = find((abs(GAM(:,iteration))==0));
+ % isok = find((abs(GAM(:,iteration))));
+ % quant(isok,:) = siJ(isok,:)./repmat(GAM(isok,iteration),1,nparam);
+ % quant(inok,:) = siJ(inok,:)./repmat(mean(abs(GAM(:,iteration))),length(inok),nparam);
+ quant = siJ./repmat(sqrt(diag(cmm)),1,nparam);
+ siJnorm(iteration,:) = vnorm(quant).*normaliz;
+ % siJnorm(iteration,:) = vnorm(siJ(inok,:)).*normaliz;
+ quant=[];
+ inok = find((abs(TAU(:,iteration))==0));
+ isok = find((abs(TAU(:,iteration))));
+ quant(isok,:) = siH(isok,:)./repmat(TAU(isok,iteration),1,nparam);
+ quant(inok,:) = siH(inok,:)./repmat(mean(abs(TAU(:,iteration))),length(inok),nparam);
+ siHnorm(iteration,:) = vnorm(quant).*normaliz;
+ % siHnorm(iteration,:) = vnorm(siH./repmat(TAU(:,iteration),1,nparam)).*normaliz;
+ quant=[];
+ inok = find((abs(LRE(:,iteration))==0));
+ isok = find((abs(LRE(:,iteration))));
+ quant(isok,:) = siLRE(isok,:)./repmat(LRE(isok,iteration),1,np);
+ quant(inok,:) = siLRE(inok,:)./repmat(mean(abs(LRE(:,iteration))),length(inok),np);
+ siLREnorm(iteration,:) = vnorm(quant).*normaliz(offset+1:end);
+ % siLREnorm(iteration,:) = vnorm(siLRE./repmat(LRE(:,iteration),1,nparam-offset)).*normaliz(offset+1:end);
end,
- % Jinv=(siJ(:,indok)'*siJ(:,indok))\siJ(:,indok)';
- % MIM=inv(Jinv*cmm*Jinv');
- MIM=siJ(:,indok)'*inv(cmm)*siJ(:,indok);
- deltaM = sqrt(diag(MIM));
- tildaM = MIM./((deltaM)*(deltaM'));
- rhoM=sqrt(1-1./diag(inv(tildaM)));
- deltaM = deltaM.*params(indok)';
- ide_strength_J(iteration,indok) = (1./[sqrt(diag(inv(MIM)))./params(indok)']);
-
- % indok = find(max(idemodel.indno{iteration},[],1)==0);
- % ide_strength_H(iteration,:)=zeros(1,nparam);
- % mim=inv(siH(:,indok)'*siH(:,indok))*siH(:,indok)';
- % % mim=mim*diag(GAM(:,iteration))*mim';
- % % MIM=inv(mim);
- % mim=mim.*repmat(TAU(:,iteration),1,length(indok))';
- % MIM=inv(mim*mim');
- % deltaM = sqrt(diag(MIM));
- % tildaM = MIM./((deltaM)*(deltaM'));
- % rhoM=sqrt(1-1./diag(inv(tildaM)));
- % deltaM = deltaM.*params(indok)';
- % ide_strength_H(iteration,indok) = (1./[sqrt(diag(inv(MIM)))./params(indok)']);
if run_index==MAX_tau || iteration==SampleSize,
file_index = file_index + 1;
if run_index<MAX_tau,
stoH = stoH(:,:,1:run_index);
stoJJ = stoJJ(:,:,1:run_index);
- stoLRE = stoLRE(:,:,1:run_index);
- end
- save([IdentifDirectoryName '/' M_.fname '_identif_' int2str(file_index)], 'stoH', 'stoJJ', 'stoLRE')
+ stoLRE = stoLRE(:,:,1:run_index);
+ end
+ save([IdentifDirectoryName '/' M_.fname '_identif_' int2str(file_index) '.mat'], 'stoH', 'stoJJ', 'stoLRE')
run_index = 0;
end
-
+
if SampleSize > 1,
waitbar(iteration/SampleSize,h,['MC Identification checks ',int2str(iteration),'/',int2str(SampleSize)])
end
end
end
end
-
-
+
+
if SampleSize > 1,
close(h)
end
-
-
- save([IdentifDirectoryName '/' M_.fname '_identif'], 'pdraws', 'idemodel', 'idemoments', 'idelre', 'indJJ', 'indH', 'indLRE', ...
- 'siHmean', 'siJmean', 'siLREmean', 'derHmean', 'derJmean', 'derLREmean', 'TAU', 'GAM', 'LRE')
+
+
+ save([IdentifDirectoryName '/' M_.fname '_identif.mat'], 'pdraws', 'idemodel', 'idemoments', 'idelre', 'indJJ', 'indH', 'indLRE', ...
+ 'siHmean', 'siJmean', 'siLREmean', 'derHmean', 'derJmean', 'derLREmean', 'TAU', 'GAM', 'LRE')
else
- load([IdentifDirectoryName '/' M_.fname '_identif'], 'pdraws', 'idemodel', 'idemoments', 'idelre', 'indJJ', 'indH', 'indLRE', ...
- 'siHmean', 'siJmean', 'siLREmean', 'derHmean', 'derJmean', 'derLREmean', 'TAU', 'GAM', 'LRE')
+ load([IdentifDirectoryName '/' M_.fname '_identif'], 'pdraws', 'idemodel', 'idemoments', 'idelre', 'indJJ', 'indH', 'indLRE', ...
+ 'siHmean', 'siJmean', 'siLREmean', 'derHmean', 'derJmean', 'derLREmean', 'TAU', 'GAM', 'LRE')
identFiles = dir([IdentifDirectoryName '/' M_.fname '_identif_*']);
options_ident.prior_mc=size(pdraws,1);
- SampleSize = options_ident.prior_mc;
+ SampleSize = options_ident.prior_mc;
options_.options_ident = options_ident;
if iload>1,
idemodel0=idemodel;
@@ -341,7 +395,7 @@ else
iteration = 0;
h = waitbar(0,'Monte Carlo identification checks ...');
for file_index=1:length(identFiles)
- load([IdentifDirectoryName '/' M_.fname '_identif_' int2str(file_index)], 'stoH', 'stoJJ', 'stoLRE')
+ load([IdentifDirectoryName '/' M_.fname '_identif_' int2str(file_index)], 'stoH', 'stoJJ', 'stoLRE')
for index=1:size(stoH,3),
iteration = iteration+1;
normH = max(abs(stoH(:,:,index))')';
@@ -350,26 +404,26 @@ else
% normH = TAU(:,iteration);
% normJ = GAM(:,iteration);
% normLRE = LRE(:,iteration);
- [idemodel.Mco(:,iteration), idemoments.Mco(:,iteration), idelre.Mco(:,iteration), ...
- idemodel.Pco(:,:,iteration), idemoments.Pco(:,:,iteration), idelre.Pco(:,:,iteration), ...
- idemodel.cond(iteration), idemoments.cond(iteration), idelre.cond(iteration), ...
- idemodel.ee(:,:,iteration), idemoments.ee(:,:,iteration), idelre.ee(:,:,iteration), ...
- idemodel.ind(:,iteration), idemoments.ind(:,iteration), ...
- idemodel.indno{iteration}, idemoments.indno{iteration}, ...
- idemodel.ino(iteration), idemoments.ino(iteration)] = ...
+ [idemodel.Mco(:,iteration), idemoments.Mco(:,iteration), idelre.Mco(:,iteration), ...
+ idemodel.Pco(:,:,iteration), idemoments.Pco(:,:,iteration), idelre.Pco(:,:,iteration), ...
+ idemodel.cond(iteration), idemoments.cond(iteration), idelre.cond(iteration), ...
+ idemodel.ee(:,:,iteration), idemoments.ee(:,:,iteration), idelre.ee(:,:,iteration), ...
+ idemodel.ind(:,iteration), idemoments.ind(:,iteration), ...
+ idemodel.indno{iteration}, idemoments.indno{iteration}, ...
+ idemodel.ino(iteration), idemoments.ino(iteration)] = ...
identification_checks(stoH(:,:,index)./normH(:,ones(nparam,1)), ...
- stoJJ(:,:,index)./normJ(:,ones(nparam,1)), ...
- stoLRE(:,:,index)./normLRE(:,ones(size(stoLRE,2),1)), bayestopt_);
+ stoJJ(:,:,index)./normJ(:,ones(nparam,1)), ...
+ stoLRE(:,:,index)./normLRE(:,ones(size(stoLRE,2),1)));
waitbar(iteration/SampleSize,h,['MC Identification checks ',int2str(iteration),'/',int2str(SampleSize)])
end
end
close(h);
- save([IdentifDirectoryName '/' M_.fname '_identif'], 'idemodel', 'idemoments', 'idelre', '-append')
+ save([IdentifDirectoryName '/' M_.fname '_identif.mat'], 'idemodel', 'idemoments', 'idelre', '-append')
end
iteration = 0;
h = waitbar(0,'Monte Carlo identification checks ...');
for file_index=1:length(identFiles)
- load([IdentifDirectoryName '/' M_.fname '_identif_' int2str(file_index)], 'stoH', 'stoJJ', 'stoLRE')
+ load([IdentifDirectoryName '/' M_.fname '_identif_' int2str(file_index)], 'stoH', 'stoJJ', 'stoLRE')
for index=1:size(stoH,3),
iteration = iteration+1;
fobj(iteration)=sum(((GAM(:,iteration)-GAM(:,1))).^2);
@@ -380,7 +434,7 @@ else
FOCH(iteration,:) = (TAU(:,iteration)-TAU(:,1))'*stoH(:,:,index);
FOCR(iteration,:) = ((GAM(:,iteration)./GAM(:,1)-1)./GAM(:,1))'*stoJJ(:,:,index);
FOCHR(iteration,:) = ((TAU(:,iteration)./TAU(:,1)-1)./TAU(:,1))'*stoH(:,:,index);
-
+
waitbar(iteration/SampleSize,h,['MC Identification checks ',int2str(iteration),'/',int2str(SampleSize)])
end
end
@@ -422,7 +476,7 @@ if SampleSize>1,
if j>offset
indd = 1:length(siLREmean(:,j-offset));
tstLREmean(indd,j-offset) = abs(derLREmean(indd,j-offset))./siLREmean(indd,j-offset);
- end
+ end
end
end
@@ -431,114 +485,18 @@ if nargout>3 && iload,
filnam = dir([IdentifDirectoryName '/' M_.fname '_identif_*.mat']);
H=[];
JJ = [];
- gp = [];
+ gp = [];
for j=1:length(filnam),
load([IdentifDirectoryName '/' M_.fname '_identif_',int2str(j),'.mat']);
H = cat(3,H, stoH(:,abs(iload),:));
JJ = cat(3,JJ, stoJJ(:,abs(iload),:));
gp = cat(3,gp, stoLRE(:,abs(iload),:));
-
+
end
end
-% mTAU = mean(TAU');
-% mGAM = mean(GAM');
-% sTAU = std(TAU');
-% sGAM = std(GAM');
-% if nargout>=3,
-% GAM0=GAM;
-% end
-% if useautocorr,
-% idiag = find(dyn_vech(eye(size(options_.varobs,1))));
-% GAM(idiag,:) = GAM(idiag,:)./(sGAM(idiag)'*ones(1,SampleSize));
-% % siJmean(idiag,:) = siJmean(idiag,:)./(sGAM(idiag)'*ones(1,nparam));
-% % siJmean = siJmean./(max(siJmean')'*ones(size(params)));
-% end
-%
-% [pcc, dd] = eig(cov(GAM'));
-% [latent, isort] = sort(-diag(dd));
-% latent = -latent;
-% pcc=pcc(:,isort);
-% siPCA = (siJmean'*abs(pcc')).^2';
-% siPCA = siPCA./(max(siPCA')'*ones(1,nparam)).*(latent*ones(1,nparam));
-% siPCA = sum(siPCA,1);
-% siPCA = siPCA./max(siPCA);
-%
-% [pcc, dd] = eig(corrcoef(GAM'));
-% [latent, isort] = sort(-diag(dd));
-% latent = -latent;
-% pcc=pcc(:,isort);
-% siPCA2 = (siJmean'*abs(pcc')).^2';
-% siPCA2 = siPCA2./(max(siPCA2')'*ones(1,nparam)).*(latent*ones(1,nparam));
-% siPCA2 = sum(siPCA2,1);
-% siPCA2 = siPCA2./max(siPCA2);
-%
-% [pcc, dd] = eig(cov(TAU'));
-% [latent, isort] = sort(-diag(dd));
-% latent = -latent;
-% pcc=pcc(:,isort);
-% siHPCA = (siHmean'*abs(pcc')).^2';
-% siHPCA = siHPCA./(max(siHPCA')'*ones(1,nparam)).*(latent*ones(1,nparam));
-% siHPCA = sum(siHPCA,1);
-% siHPCA = siHPCA./max(siHPCA);
-%
-% [pcc, dd] = eig(corrcoef(TAU'));
-% [latent, isort] = sort(-diag(dd));
-% latent = -latent;
-% pcc=pcc(:,isort);
-% siHPCA2 = (siHmean'*abs(pcc')).^2';
-% siHPCA2 = siHPCA2./(max(siHPCA2')'*ones(1,nparam)).*(latent*ones(1,nparam));
-% siHPCA2 = sum(siHPCA2,1);
-% siHPCA2 = siHPCA2./max(siHPCA2);
-
-
disp_identification(pdraws, idemodel, idemoments, name, advanced)
-% figure,
-% % myboxplot(siPCA(1:(max(find(cumsum(latent)./length(indJJ)<0.99))+1),:))
-% subplot(221)
-% bar(siHPCA)
-% % set(gca,'ylim',[0 1])
-% set(gca,'xticklabel','')
-% set(gca,'xlim',[0.5 nparam+0.5])
-% for ip=1:nparam,
-% text(ip,-0.02,name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% title('Sensitivity in TAU''s PCA')
-%
-% subplot(222)
-% % myboxplot(siPCA(1:(max(find(cumsum(latent)./length(indJJ)<0.99))+1),:))
-% bar(siHPCA2)
-% % set(gca,'ylim',[0 1])
-% set(gca,'xticklabel','')
-% set(gca,'xlim',[0.5 nparam+0.5])
-% for ip=1:nparam,
-% text(ip,-0.02,name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% title('Sensitivity in standardized TAU''s PCA')
-%
-%
-% subplot(223)
-% % myboxplot(siPCA(1:(max(find(cumsum(latent)./length(indJJ)<0.99))+1),:))
-% bar(siPCA)
-% % set(gca,'ylim',[0 1])
-% set(gca,'xticklabel','')
-% set(gca,'xlim',[0.5 nparam+0.5])
-% for ip=1:nparam,
-% text(ip,-0.02,name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% title('Sensitivity in moments'' PCA')
-%
-% subplot(224)
-% % myboxplot(siPCA(1:(max(find(cumsum(latent)./length(indJJ)<0.99))+1),:))
-% bar(siPCA2)
-% % set(gca,'ylim',[0 1])
-% set(gca,'xticklabel','')
-% set(gca,'xlim',[0.5 nparam+0.5])
-% for ip=1:nparam,
-% text(ip,-0.02,name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% title('Sensitivity in standardized moments'' PCA')
if advanced,
figure('Name','Identification LRE model form'),
@@ -563,7 +521,7 @@ if advanced,
text(ip,-0.02,deblank(M_.param_names(indx(is(ip)),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
title('Sensitivity in the LRE model')
-
+
subplot(212)
if SampleSize>1,
mmm = mean(-idelre.Mco');
@@ -586,10 +544,10 @@ if advanced,
eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_LRE']);
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_LRE']);
if options_.nograph, close(gcf); end
-
+
figure('Name','Identification in the model'),
% subplot(311)
- %
+ %
% if SampleSize>1,
% mmm = mean(ide_strength_H);
% else
@@ -609,7 +567,7 @@ if advanced,
% text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
% end
% title('Identification strength in the model')
-
+
subplot(211)
% mmm = mean(siHmean);
% [ss, is] = sort(mmm);
@@ -624,7 +582,7 @@ if advanced,
myboxplot(log(siHnorm(:,is)))
else
bar(siHnorm(:,is))
- end
+ end
% set(gca,'ylim',[0 1.05])
set(gca,'xticklabel','')
dy = get(gca,'ylim');
@@ -633,7 +591,7 @@ if advanced,
text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
title('Sensitivity in the model')
-
+
subplot(212)
if SampleSize>1,
mmm = mean(-idemodel.Mco');
@@ -672,15 +630,16 @@ end
if SampleSize>1,
myboxplot(log(ide_strength_J(:,is)))
else
- bar(ide_strength_J(:,is))
-end
+ bar(log(ide_strength_J(:,is)))
+end
% set(gca,'ylim',[0 1.05])
+set(gca,'xlim',[0 nparam+1])
set(gca,'xticklabel','')
dy = get(gca,'ylim');
% dy=dy(2)-dy(1);
-for ip=1:nparam,
- text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-end
+% for ip=1:nparam,
+% text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+% end
title('Identification strength in the moments')
subplot(212)
@@ -699,6 +658,7 @@ else
bar(siJnorm(:,is))
end
% set(gca,'ylim',[0 1.05])
+set(gca,'xlim',[0 nparam+1])
set(gca,'xticklabel','')
dy = get(gca,'ylim');
% dy=dy(2)-dy(1);
@@ -730,30 +690,55 @@ title('Sensitivity in the moments')
% eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_moments']);
% if options_.nograph, close(gcf); end
-if SampleSize==1,
+if SampleSize==1 && advanced,
% identificaton patterns
[U,S,V]=svd(siJ./normJ(:,ones(nparam,1)),0);
- figure('name','Identification patterns (moments)'),
+ if nparam<5,
+ f1 = figure('name','Identification patterns (moments)');
+ else
+ f1 = figure('name','Identification patterns (moments): SMALLEST SV');
+ f2 = figure('name','Identification patterns (moments): HIGHEST SV');
+ end
for j=1:min(nparam,8),
- subplot(2,4,j),
+ if j<5,
+ figure(f1),
+ jj=j;
+ else
+ figure(f2),
+ jj=j-4;
+ end
+ subplot(4,1,jj),
if j<5
bar(abs(V(:,end-j+1))),
Stit = S(end-j+1,end-j+1);
- if j==1, ylabel('SMALLEST singular values'), end,
+% if j==4 || j==nparam,
+% xlabel('SMALLEST singular values'),
+% end,
else
bar(abs(V(:,j))),
Stit = S(j,j);
- if j==5, ylabel('LARGEST singular values'), end,
+% if j==8 || j==nparam,
+% xlabel('LARGEST singular values'),
+% end,
end
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(['Singular value ',num2str(Stit)])
end
- saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_pattern'])
- eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_pattern']);
- eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_pattern']);
+ figure(f1);
+ saveas(f1,[IdentifDirectoryName,'/',M_.fname,'_ident_pattern_1'])
+ eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_pattern_1']);
+ eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_pattern_1']);
+ if nparam>4,
+ figure(f2),
+ saveas(f2,[IdentifDirectoryName,'/',M_.fname,'_ident_pattern_2'])
+ eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_pattern_2']);
+ eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_pattern_2']);
+ end
end
if advanced,
@@ -773,17 +758,17 @@ if advanced,
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_COND']);
if options_.nograph, close(gcf); end
end
-
-
+
+
pco = NaN(np,np);
- for j=1:np,
+ for j=1:np,
if SampleSize>1
- pco(j+1:end,j) = mean(abs(squeeze(idelre.Pco(j+1:end,j,:))'));
+ pco(j+1:end,j) = mean(abs(squeeze(idelre.Pco(j+1:end,j,:))'));
else
- pco(j+1:end,j) = abs(idelre.Pco(j+1:end,j))';
+ pco(j+1:end,j) = abs(idelre.Pco(j+1:end,j))';
end
end
- figure('name','Pairwise correlations in the LRE model'),
+ figure('name','Pairwise correlations in the LRE model'),
imagesc(pco',[0 1]);
set(gca,'xticklabel','')
set(gca,'yticklabel','')
@@ -796,16 +781,16 @@ if advanced,
eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_PCORR_LRE']);
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_PCORR_LRE']);
if options_.nograph, close(gcf); end
-
+
pco = NaN(nparam,nparam);
- for j=1:nparam,
+ for j=1:nparam,
if SampleSize>1
- pco(j+1:end,j) = mean(abs(squeeze(idemodel.Pco(j+1:end,j,:))'));
+ pco(j+1:end,j) = mean(abs(squeeze(idemodel.Pco(j+1:end,j,:))'));
else
- pco(j+1:end,j) = abs(idemodel.Pco(j+1:end,j))';
+ pco(j+1:end,j) = abs(idemodel.Pco(j+1:end,j))';
end
end
- figure('name','Pairwise correlations in the model'),
+ figure('name','Pairwise correlations in the model'),
imagesc(pco',[0 1]);
set(gca,'xticklabel','')
set(gca,'yticklabel','')
@@ -818,15 +803,15 @@ if advanced,
eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_PCORR_model']);
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_PCORR_model']);
if options_.nograph, close(gcf); end
-
- for j=1:nparam,
+
+ for j=1:nparam,
if SampleSize>1
- pco(j+1:end,j) = mean(abs(squeeze(idemoments.Pco(j+1:end,j,:))'));
+ pco(j+1:end,j) = mean(abs(squeeze(idemoments.Pco(j+1:end,j,:))'));
else
- pco(j+1:end,j) = abs(idemoments.Pco(j+1:end,j))';
+ pco(j+1:end,j) = abs(idemoments.Pco(j+1:end,j))';
end
end
- figure('name','Pairwise correlations in the moments'),
+ figure('name','Pairwise correlations in the moments'),
imagesc(pco',[0 1]);
set(gca,'xticklabel','')
set(gca,'yticklabel','')
@@ -840,110 +825,6 @@ if advanced,
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_PCORR_moments']);
if options_.nograph, close(gcf); end
end
-% ifig=0;
-% nbox = min(np-1,12);
-% for j=1:np,
-% if mod(j,12)==1,
-% ifig = ifig+1;
-% figure('name','Pairwise correlations in the LRE model'),
-% iplo=0;
-% end
-% iplo=iplo+1;
-% if SampleSize>1
-% mmm = mean(abs(squeeze(idelre.Pco(:,j,:))'));
-% else
-% mmm = abs(idelre.Pco(:,j))';
-% end
-% [sss, immm] = sort(-mmm);
-% subplot(3,4,iplo),
-% if nbox==1,
-% myboxplot(squeeze(idelre.Pco(immm(2:nbox+1),j,:))),
-% else
-% myboxplot(squeeze(idelre.Pco(immm(2:nbox+1),j,:))'),
-% end
-% set(gca,'ylim',[-1 1],'ygrid','on')
-% set(gca,'xticklabel','')
-% for ip=1:nbox, %np,
-% text(ip,-1.02,deblank(M_.param_names(indx(immm(ip+1)),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% title(deblank(M_.param_names(indx(j),:))),
-% if j==np || mod(j,12)==0
-% saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_PCORR_LRE',int2str(ifig)])
-% eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_PCORR_LRE',int2str(ifig)]);
-% eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_PCORR_LRE',int2str(ifig)]);
-% if options_.nograph, close(gcf); end
-% end
-% end
-%
-% ifig=0;
-% nbox = min(nparam-1,12);
-% for j=1:nparam,
-% if mod(j,12)==1,
-% ifig = ifig+1;
-% figure('name','Pairwise correlations in the model'),
-% iplo=0;
-% end
-% iplo=iplo+1;
-% if SampleSize>1,
-% mmm = mean(abs(squeeze(idemodel.Pco(:,j,:))'));
-% else
-% mmm = abs(idemodel.Pco(:,j)');
-% end
-% [sss, immm] = sort(-mmm);
-% subplot(3,4,iplo),
-% if nbox==1,
-% myboxplot(squeeze(idemodel.Pco(immm(2:nbox+1),j,:))),
-% else
-% myboxplot(squeeze(idemodel.Pco(immm(2:nbox+1),j,:))'),
-% end
-% set(gca,'ylim',[-1 1],'ygrid','on')
-% set(gca,'xticklabel','')
-% for ip=1:nbox, %np,
-% text(ip,-1.02,name{immm(ip+1)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% title(name{j}),
-% if j==nparam || mod(j,12)==0
-% saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_PCORR_model',int2str(ifig)])
-% eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_PCORR_model',int2str(ifig)]);
-% eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_PCORR_model',int2str(ifig)]);
-% if options_.nograph, close(gcf); end
-% end
-% end
-%
-% ifig=0;
-% nbox = min(nparam-1,12);
-% for j=1:nparam,
-% if mod(j,12)==1,
-% ifig = ifig+1;
-% figure('name','Pairwise correlations in the 1st and 2nd moments'),
-% iplo=0;
-% end
-% iplo=iplo+1;
-% if SampleSize>1
-% mmm = mean(abs(squeeze(idemoments.Pco(:,j,:))'));
-% else
-% mmm = abs(idemoments.Pco(:,j)');
-% end
-% [sss, immm] = sort(-mmm);
-% subplot(3,4,iplo),
-% if nbox==1,
-% myboxplot(squeeze(idemoments.Pco(immm(2:nbox+1),j,:))),
-% else
-% myboxplot(squeeze(idemoments.Pco(immm(2:nbox+1),j,:))'),
-% end
-% set(gca,'ylim',[-1 1],'ygrid','on')
-% set(gca,'xticklabel','')
-% for ip=1:nbox, %np,
-% text(ip,-1.02,name{immm(ip+1)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% title(name{j}),
-% if j==nparam || mod(j,12)==0
-% saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_PCORR_moments',int2str(ifig)])
-% eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_PCORR_moments',int2str(ifig)]);
-% eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_PCORR_moments',int2str(ifig)]);
-% if options_.nograph, close(gcf); end
-% end
-% end
if exist('OCTAVE_VERSION')
diff --git a/matlab/identification_checks.m b/matlab/identification_checks.m
index a8b60b3bb15bd388f1db609c3e84fe19104599fa..02c55a2cc788bb348fd1f48744f02107661bd318 100644
--- a/matlab/identification_checks.m
+++ b/matlab/identification_checks.m
@@ -1,4 +1,38 @@
-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_)
+function [McoH, McoJ, McoGP, PcoH, PcoJ, PcoGP, condH, condJ, condGP, eH, eJ, eGP, ind01, ind02, indnoH, indnoJ, ixnoH, ixnoJ] = identification_checks(H, JJ, gp)
+% function [McoH, McoJ, McoGP, PcoH, PcoJ, PcoGP, condH, condJ, condGP, eH,
+% eJ, eGP, ind01, ind02, indnoH, indnoJ, ixnoH, ixnoJ] = identification_checks(H, JJ, gp)
+% checks for identification
+%
+% INPUTS
+% o H [matrix] [(entries in st.sp. model solutio) x nparams]
+% derivatives of model solution w.r.t. parameters and shocks
+% o JJ [matrix] [moments x nparams]
+% derivatives of moments w.r.t. parameters and shocks
+% o gp [matrix] [jacobian_entries x nparams]
+% derivatives of jacobian (i.e. LRE model) w.r.t. parameters and shocks
+%
+% OUTPUTS
+% o McoH [array] multicollinearity coefficients in the model solution
+% o McoJ [array] multicollinearity coefficients in the moments
+% o McoGP [array] multicollinearity coefficients in the LRE model
+% o PcoH [matrix] pairwise correlations in the model solution
+% o PcoJ [matrix] pairwise correlations in the moments
+% o PcoGP [matrix] pairwise correlations in the LRE model
+% o condH condition number of H
+% o condJ condition number of JJ
+% o condGP condition number of gp
+% o eH eigevectors of H
+% o eJ eigevectors of JJ
+% o eGP eigevectors of gp
+% o ind01 [array] binary indicator for zero columns of H
+% o ind02 [array] binary indicator for zero columns of JJ
+% o indnoH [matrix] index of non-identified params in H
+% o indnoJ [matrix] index of non-identified params in JJ
+% o ixnoH number of rows in ind01
+% o ixnoJ number of rows in ind02
+%
+% SPECIAL REQUIREMENTS
+% None
% Copyright (C) 2008-2011 Dynare Team
%
@@ -20,54 +54,44 @@ function [McoH, McoJ, McoGP, PcoH, PcoJ, PcoGP, condH, condJ, condGP, eH, eJ, eG
% My suggestion is to have the following steps for identification check in
% dynare:
-% 1. check rank of H at theta
+% 1. check rank of H, JJ, gp at theta
npar = size(H,2);
-npar0 = size(gp,2);
+npar0 = size(gp,2); % shocks do not enter jacobian
indnoH = zeros(1,npar);
indnoJ = zeros(1,npar);
indnoLRE = zeros(1,npar0);
-ind1 = find(vnorm(H)>=eps);
+
+% H matrix
+ind1 = find(vnorm(H)>=eps); % take non-zero columns
H1 = H(:,ind1);
-covH = H1'*H1;
-sdH = sqrt(diag(covH));
-sdH = sdH*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)));
+eH(ind1,length(find(vnorm(H)<eps))+1:end) = e1; % non-zero eigenvectors
+eH(find(vnorm(H)<eps),1:length(find(vnorm(H)<eps)))=eye(length(find(vnorm(H)<eps)));
condH = cond(H1);
-condHH = cond(H1'*H1);
rankH = rank(H);
rankHH = rank(H'*H);
-ind2 = find(vnorm(JJ)>=eps);
+ind2 = find(vnorm(JJ)>=eps); % take non-zero columns
JJ1 = JJ(:,ind2);
-covJJ = JJ1'*JJ1;
-sdJJ = sqrt(diag(covJJ));
-sdJJ = sdJJ*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)));
-condJJ = cond(JJ1'*JJ1);
+eJ(ind2,length(find(vnorm(JJ)<eps))+1:end) = ee1; % non-zero eigenvectors
+eJ(find(vnorm(JJ)<eps),1:length(find(vnorm(JJ)<eps)))=eye(length(find(vnorm(JJ)<eps)));
condJ = cond(JJ1);
rankJJ = rank(JJ'*JJ);
rankJ = rank(JJ);
-ind3 = find(vnorm(gp)>=eps);
+ind3 = find(vnorm(gp)>=eps); % take non-zero columns
gp1 = gp(:,ind3);
covgp = gp1'*gp1;
sdgp = sqrt(diag(covgp));
sdgp = sdgp*sdgp';
-[ex1,ex2] = eig( (gp1'*gp1)./sdgp );
-% eJ = NaN(npar,length(ind2));
+[eu,ex2,ex1] = svd(gp1, 0 );
eGP = zeros(npar0,npar0);
-eGP(ind3,length(find(vnorm(gp)==0))+1:end) = ex1;
-eGP(find(vnorm(gp)==0),1:length(find(vnorm(gp)==0)))=eye(length(find(vnorm(gp)==0)));
+eGP(ind3,length(find(vnorm(gp)<eps))+1:end) = ex1; % non-zero eigenvectors
+eGP(find(vnorm(gp)<eps),1:length(find(vnorm(gp)<eps)))=eye(length(find(vnorm(gp)<eps)));
% condJ = cond(JJ1'*JJ1);
condGP = cond(gp1);
@@ -77,11 +101,7 @@ ind02 = zeros(npar,1);
ind01(ind1) = 1;
ind02(ind2) = 1;
-% rank(H1)==size(H1,2)
-% rank(JJ1)==size(JJ1,2)
-
-% to find near linear dependence problems I use
-
+% find near linear dependence problems:
McoH = NaN(npar,1);
McoJ = NaN(npar,1);
McoGP = NaN(npar0,1);
@@ -95,32 +115,23 @@ for ii = 1:size(gp1,2);
McoGP(ind3(ii),:) = [cosn([gp1(:,ii),gp1(:,find([1:1:size(gp1,2)]~=ii))])];
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;
+ % using the vnorm and the svd of H computed before;
% disp('Some parameters are NOT identified in the model: H rank deficient')
% disp(' ')
if length(ind1)<npar,
+ % parameters with zero column in H
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),
+ % linearely dependent parameters in H
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;
+ indnoH(ixno,ind1) = (abs(e1(:,e0(j))) > 1.e-6 )';
end
else % rank(H)==length(theta), go to 2
% 2. check rank of J
@@ -135,22 +146,15 @@ if rankJ<npar || rankJJ<npar || min(1-McoJ)<1.e-10
% disp('Some parameters are NOT identified by the moments included in J')
% disp(' ')
if length(ind2)<npar,
+ % parameters with zero column in JJ
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),
+ % linearely dependent parameters in JJ
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;
+ indnoJ(ixno,ind2) = (abs(ee1(:,ee0(j))) > 1.e-6)';
end
else %rank(J)==length(theta) =>
% disp('All parameters are identified at theta by the moments included in J')
@@ -189,11 +193,6 @@ for ii = 1:size(gp1,2);
end
-% ind01 = zeros(npar,1);
-% ind02 = zeros(npar,1);
-% ind01(ind1) = 1;
-% ind02(ind2) = 1;
-