stepan
/
dynare
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Fixed bugs, clean-up and some more commenting of identification routines

time-shift
Marco Ratto 2011-02-28 16:55:21 +01:00
parent 84d7c97d4e
commit 917b8e5285
2 changed files with 268 additions and 388 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

541
matlab/dynare_identification.m
View File

@ -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,:);
else
params = pdraws0(burnin_iteration+1,:);
end
if SampleSize==1,
params = set_prior(estim_params_,M_,options_)';
else
params = prior_draw();
if nargin==2,
if burnin_iteration>=BurninSampleSize,
params = pdraws0(iteration+1,:);
else
params = pdraws0(burnin_iteration+1,:);
end
else
params = prior_draw();
end
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')

115
matlab/identification_checks.m
View File

@ -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;