Modifications following meetings in Paris (not yet completed 100%):
1) new default option for prior_mc = 1; 2) new option: advanced (default=0). If advanced=0, simple messages and plots; advanced=1 shows all details about weak identification diagnostics; 3) new option: periods (default=300). Length of stochastic simulations to generate simulated moments for 'GMM type' analysis of strength of identification; 4) new option: replic (default = 100). Number of replicated simulated moments for 'GMM type' analysis of strength of identification; 5) new feature: allow local identification analysis WITHOUT defining priors, using parameter values as defined in the .mod file definition. 6) simplified graphical and tabular output.time-shift
Marco Ratto
parent
0a637be94c
commit
70400e5e84
|
|
@ -30,14 +30,27 @@ end
|
|||
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);
|
||||
options_ident = set_default_option(options_ident,'prior_mc',2000);
|
||||
options_ident = set_default_option(options_ident,'prior_mc',1);
|
||||
options_ident = set_default_option(options_ident,'prior_range',0);
|
||||
options_ident = set_default_option(options_ident,'periods',300);
|
||||
options_ident = set_default_option(options_ident,'replic',100);
|
||||
options_ident = set_default_option(options_ident,'advanced',0);
|
||||
if nargin==2,
|
||||
options_ident.prior_mc=size(pdraws0,1);
|
||||
end
|
||||
if isempty(estim_params_),
|
||||
options_ident.prior_mc=1;
|
||||
options_ident.prior_range=0;
|
||||
prior_exist=0;
|
||||
else
|
||||
prior_exist=1;
|
||||
end
|
||||
|
||||
iload = options_ident.load_ident_files;
|
||||
advanced = options_ident.advanced;
|
||||
nlags = options_ident.ar;
|
||||
periods = options_ident.periods;
|
||||
replic = options_ident.replic;
|
||||
useautocorr = options_ident.useautocorr;
|
||||
options_.ar=nlags;
|
||||
options_.prior_mc = options_ident.prior_mc;
|
||||
|
|
@ -68,33 +81,60 @@ if ~(exist('sylvester3mr','file')==2),
|
|||
end
|
||||
|
||||
IdentifDirectoryName = CheckPath('identification');
|
||||
if prior_exist,
|
||||
|
||||
indx = estim_params_.param_vals(:,1);
|
||||
indexo=[];
|
||||
if ~isempty(estim_params_.var_exo)
|
||||
indexo = estim_params_.var_exo(:,1);
|
||||
indx = estim_params_.param_vals(:,1);
|
||||
indexo=[];
|
||||
if ~isempty(estim_params_.var_exo)
|
||||
indexo = estim_params_.var_exo(:,1);
|
||||
end
|
||||
|
||||
nparam = length(bayestopt_.name);
|
||||
np = estim_params_.np;
|
||||
name = bayestopt_.name;
|
||||
|
||||
offset = estim_params_.nvx;
|
||||
offset = offset + estim_params_.nvn;
|
||||
offset = offset + estim_params_.ncx;
|
||||
offset = offset + estim_params_.ncn;
|
||||
else
|
||||
indx = [1:M_.param_nbr];
|
||||
indexo = [1:M_.exo_nbr];
|
||||
offset = M_.exo_nbr;
|
||||
np = M_.param_nbr;
|
||||
nparam = np+offset;
|
||||
name = [cellstr(M_.exo_names); cellstr(M_.param_names)];
|
||||
end
|
||||
|
||||
nparam = length(bayestopt_.name);
|
||||
|
||||
MaxNumberOfBytes=options_.MaxNumberOfBytes;
|
||||
|
||||
disp(' ')
|
||||
disp(['==== Identification analysis ====' ]),
|
||||
disp(' ')
|
||||
|
||||
if iload <=0,
|
||||
|
||||
iteration = 0;
|
||||
burnin_iteration = 0;
|
||||
if SampleSize==1,
|
||||
BurninSampleSize=0;
|
||||
else
|
||||
BurninSampleSize=50;
|
||||
end
|
||||
loop_indx = 0;
|
||||
file_index = 0;
|
||||
run_index = 0;
|
||||
|
||||
if SampleSize > 1,
|
||||
h = waitbar(0,'Monte Carlo identification checks ...');
|
||||
end
|
||||
[I,J]=find(M_.lead_lag_incidence');
|
||||
|
||||
while iteration < SampleSize,
|
||||
loop_indx = loop_indx+1;
|
||||
if prior_exist,
|
||||
if nargin==2,
|
||||
if burnin_iteration>=50,
|
||||
if burnin_iteration>=BurninSampleSize,
|
||||
params = pdraws0(iteration+1,:);
|
||||
else
|
||||
params = pdraws0(burnin_iteration+1,:);
|
||||
|
|
@ -103,6 +143,9 @@ if iload <=0,
|
|||
params = prior_draw();
|
||||
end
|
||||
set_all_parameters(params);
|
||||
else
|
||||
params = [sqrt(diag(M_.Sigma_e))', M_.params'];
|
||||
end
|
||||
[A,B,ys,info]=dynare_resolve;
|
||||
|
||||
|
||||
|
|
@ -117,7 +160,7 @@ if iload <=0,
|
|||
yy0=oo_.dr.ys(I);
|
||||
[residual, g1 ] = feval([M_.fname,'_dynamic'],yy0, oo_.exo_steady_state', M_.params,1);
|
||||
|
||||
if burnin_iteration<50,
|
||||
if burnin_iteration<BurninSampleSize,
|
||||
burnin_iteration = burnin_iteration + 1;
|
||||
pdraws(burnin_iteration,:) = params;
|
||||
TAU(:,burnin_iteration)=tau;
|
||||
|
|
@ -143,11 +186,11 @@ if iload <=0,
|
|||
dum = [dum; vec(gam{j+1})];
|
||||
end
|
||||
GAM(:,burnin_iteration)=[oo_.dr.ys(bayestopt_.mfys); dum];
|
||||
warning warning_old_state;
|
||||
% warning warning_old_state;
|
||||
else
|
||||
iteration = iteration + 1;
|
||||
run_index = run_index + 1;
|
||||
if iteration==1,
|
||||
if iteration==1 & BurninSampleSize,
|
||||
indJJ = (find(std(GAM')>1.e-8));
|
||||
indH = (find(std(TAU')>1.e-8));
|
||||
indLRE = (find(std(LRE')>1.e-8));
|
||||
|
|
@ -163,10 +206,23 @@ if iload <=0,
|
|||
end
|
||||
|
||||
if iteration,
|
||||
[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));
|
||||
indLRE = (find(max(abs(gp'))>1.e-8));
|
||||
TAU = zeros(length(indH),SampleSize);
|
||||
GAM = zeros(length(indJJ),SampleSize);
|
||||
LRE = zeros(length(indLRE),SampleSize);
|
||||
MAX_tau = min(SampleSize,ceil(MaxNumberOfBytes/(length(indH)*nparam)/8));
|
||||
MAX_gam = min(SampleSize,ceil(MaxNumberOfBytes/(length(indJJ)*nparam)/8));
|
||||
stoH = zeros([length(indH),nparam,MAX_tau]);
|
||||
stoJJ = zeros([length(indJJ),nparam,MAX_tau]);
|
||||
delete([IdentifDirectoryName '/' M_.fname '_identif_*.mat'])
|
||||
end
|
||||
TAU(:,iteration)=tau(indH);
|
||||
vg1 = [oo_.dr.ys(oo_.dr.order_var); vec(g1)];
|
||||
LRE(:,iteration)=vg1(indLRE);
|
||||
[JJ, H, gam, gp] = getJJ(A, B, M_,oo0,options_,0,indx,indexo,bayestopt_.mf2,nlags,useautocorr);
|
||||
GAM(:,iteration)=gam(indJJ);
|
||||
stoLRE(:,:,run_index) = gp(indLRE,:);
|
||||
stoH(:,:,run_index) = H(indH,:);
|
||||
|
|
@ -177,12 +233,16 @@ if iload <=0,
|
|||
% use prior uncertainty
|
||||
siJ = (JJ(indJJ,:));
|
||||
siH = (H(indH,:));
|
||||
|
||||
siLRE = (gp(indLRE,:));
|
||||
% siJ = abs(JJ(indJJ,:).*(ones(length(indJJ),1)*bayestopt_.p2'));
|
||||
% 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;
|
||||
|
|
@ -210,8 +270,36 @@ if iload <=0,
|
|||
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.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_);
|
||||
indok = find(max(idemoments.indno{iteration},[],1)==0);
|
||||
ide_strength_J(iteration,:)=NaN(1,nparam);
|
||||
if iteration ==1,
|
||||
cmm = simulated_moment_uncertainty(indJJ, periods, replic);
|
||||
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,
|
||||
|
|
@ -224,13 +312,17 @@ if iload <=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', ...
|
||||
|
|
@ -263,7 +355,8 @@ else
|
|||
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.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_);
|
||||
|
|
@ -273,13 +366,28 @@ else
|
|||
close(h);
|
||||
save([IdentifDirectoryName '/' M_.fname '_identif'], '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')
|
||||
for index=1:size(stoH,3),
|
||||
iteration = iteration+1;
|
||||
fobj(iteration)=sum(((GAM(:,iteration)-GAM(:,1))).^2);
|
||||
fobjH(iteration)=sum(((TAU(:,iteration)-TAU(:,1))).^2);
|
||||
fobjR(iteration)=sum(((GAM(:,iteration)./GAM(:,1)-1)).^2);
|
||||
fobjHR(iteration)=sum(((TAU(:,iteration)./TAU(:,1)-1)).^2);
|
||||
FOC(iteration,:) = (GAM(:,iteration)-GAM(:,1))'*stoJJ(:,:,index);
|
||||
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
|
||||
close(h);
|
||||
end
|
||||
|
||||
offset = estim_params_.nvx;
|
||||
offset = offset + estim_params_.nvn;
|
||||
offset = offset + estim_params_.ncx;
|
||||
offset = offset + estim_params_.ncn;
|
||||
|
||||
if SampleSize>1,
|
||||
siJmean = siJmean.*(ones(length(indJJ),1)*std(pdraws));
|
||||
siHmean = siHmean.*(ones(length(indH),1)*std(pdraws));
|
||||
siLREmean = siLREmean.*(ones(length(indLRE),1)*std(pdraws(:, offset+1:end )));
|
||||
|
|
@ -290,15 +398,16 @@ derLREmean = derLREmean.*(ones(length(indLRE),1)*std(pdraws(:, offset+1:end )));
|
|||
|
||||
derHmean = abs(derHmean./(max(siHmean')'*ones(1,size(pdraws,2))));
|
||||
derJmean = abs(derJmean./(max(siJmean')'*ones(1,size(pdraws,2))));
|
||||
derLREmean = abs(derLREmean./(max(siLREmean')'*ones(1,estim_params_.np)));
|
||||
derLREmean = abs(derLREmean./(max(siLREmean')'*ones(1,np)));
|
||||
|
||||
siHmean = siHmean./(max(siHmean')'*ones(1,size(pdraws,2)));
|
||||
siJmean = siJmean./(max(siJmean')'*ones(1,size(pdraws,2)));
|
||||
siLREmean = siLREmean./(max(siLREmean')'*ones(1,estim_params_.np));
|
||||
siLREmean = siLREmean./(max(siLREmean')'*ones(1,np));
|
||||
|
||||
tstJmean = derJmean*0;
|
||||
tstHmean = derHmean*0;
|
||||
tstLREmean = derLREmean*0;
|
||||
|
||||
if exist('OCTAVE_VERSION')
|
||||
warning('off', 'Octave:divide-by-zero')
|
||||
else
|
||||
|
|
@ -315,6 +424,7 @@ if j>offset
|
|||
tstLREmean(indd,j-offset) = abs(derLREmean(indd,j-offset))./siLREmean(indd,j-offset);
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
if nargout>3 & iload,
|
||||
|
|
@ -382,7 +492,7 @@ end
|
|||
% siHPCA2 = siHPCA2./max(siHPCA2);
|
||||
|
||||
|
||||
disp_identification(pdraws, idemodel, idemoments)
|
||||
disp_identification(pdraws, idemodel, idemoments, name, advanced)
|
||||
|
||||
% figure,
|
||||
% % myboxplot(siPCA(1:(max(find(cumsum(latent)./length(indJJ)<0.99))+1),:))
|
||||
|
|
@ -392,7 +502,7 @@ disp_identification(pdraws, idemodel, idemoments)
|
|||
% set(gca,'xticklabel','')
|
||||
% set(gca,'xlim',[0.5 nparam+0.5])
|
||||
% for ip=1:nparam,
|
||||
% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
||||
% text(ip,-0.02,name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
||||
% end
|
||||
% title('Sensitivity in TAU''s PCA')
|
||||
%
|
||||
|
|
@ -403,7 +513,7 @@ disp_identification(pdraws, idemodel, idemoments)
|
|||
% set(gca,'xticklabel','')
|
||||
% set(gca,'xlim',[0.5 nparam+0.5])
|
||||
% for ip=1:nparam,
|
||||
% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
||||
% text(ip,-0.02,name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
||||
% end
|
||||
% title('Sensitivity in standardized TAU''s PCA')
|
||||
%
|
||||
|
|
@ -415,7 +525,7 @@ disp_identification(pdraws, idemodel, idemoments)
|
|||
% set(gca,'xticklabel','')
|
||||
% set(gca,'xlim',[0.5 nparam+0.5])
|
||||
% for ip=1:nparam,
|
||||
% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
||||
% text(ip,-0.02,name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
||||
% end
|
||||
% title('Sensitivity in moments'' PCA')
|
||||
%
|
||||
|
|
@ -426,30 +536,50 @@ disp_identification(pdraws, idemodel, idemoments)
|
|||
% set(gca,'xticklabel','')
|
||||
% set(gca,'xlim',[0.5 nparam+0.5])
|
||||
% for ip=1:nparam,
|
||||
% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
||||
% 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'),
|
||||
subplot(211)
|
||||
mmm = mean(siLREmean);
|
||||
if SampleSize > 1,
|
||||
mmm = mean(siLREnorm);
|
||||
else
|
||||
mmm = (siLREnorm);
|
||||
end
|
||||
[ss, is] = sort(mmm);
|
||||
myboxplot(siLREmean(:,is))
|
||||
set(gca,'ylim',[0 1.05])
|
||||
if SampleSize ==1,
|
||||
bar(siLREnorm(:,is))
|
||||
else
|
||||
myboxplot(log(siLREnorm(:,is)))
|
||||
end
|
||||
% mmm = mean(siLREmean);
|
||||
% [ss, is] = sort(mmm);
|
||||
% myboxplot(siLREmean(:,is))
|
||||
% set(gca,'ylim',[0 1.05])
|
||||
set(gca,'xticklabel','')
|
||||
for ip=1:estim_params_.np,
|
||||
text(ip,-0.02,deblank(M_.param_names(estim_params_.param_vals(is(ip),1),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
||||
for ip=1:np,
|
||||
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');
|
||||
else
|
||||
mmm = (-idelre.Mco');
|
||||
end
|
||||
[ss, is] = sort(mmm);
|
||||
myboxplot(idelre.Mco(is,:)')
|
||||
if SampleSize ==1,
|
||||
bar(idelre.Mco(is,:)')
|
||||
else
|
||||
myboxplot(idelre.Mco(is,:)')
|
||||
end
|
||||
set(gca,'ylim',[0 1])
|
||||
set(gca,'xticklabel','')
|
||||
for ip=1:estim_params_.np,
|
||||
text(ip,-0.02,deblank(M_.param_names(estim_params_.param_vals(is(ip),1),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
||||
for ip=1:np,
|
||||
text(ip,-0.02,deblank(M_.param_names(indx(is(ip)),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
||||
end
|
||||
title('Multicollinearity in the LRE model')
|
||||
saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_LRE'])
|
||||
|
|
@ -458,60 +588,176 @@ 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
|
||||
% mmm = (ide_strength_H);
|
||||
% end
|
||||
% [ss, is] = sort(mmm);
|
||||
% if SampleSize>1,
|
||||
% myboxplot(ide_strength_H(:,is))
|
||||
% else
|
||||
% bar(ide_strength_H(:,is))
|
||||
% end
|
||||
% % set(gca,'ylim',[0 1.05])
|
||||
% 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
|
||||
% title('Identification strength in the model')
|
||||
|
||||
subplot(211)
|
||||
mmm = mean(siHmean);
|
||||
% mmm = mean(siHmean);
|
||||
% [ss, is] = sort(mmm);
|
||||
% myboxplot(siHmean(:,is))
|
||||
if SampleSize>1,
|
||||
mmm = mean(siHnorm);
|
||||
else
|
||||
mmm = (siHnorm);
|
||||
end
|
||||
[ss, is] = sort(mmm);
|
||||
myboxplot(siHmean(:,is))
|
||||
set(gca,'ylim',[0 1.05])
|
||||
if SampleSize>1,
|
||||
myboxplot(log(siHnorm(:,is)))
|
||||
else
|
||||
bar(siHnorm(:,is))
|
||||
end
|
||||
% set(gca,'ylim',[0 1.05])
|
||||
set(gca,'xticklabel','')
|
||||
dy = get(gca,'ylim');
|
||||
% dy=dy(2)-dy(1);
|
||||
for ip=1:nparam,
|
||||
text(ip,-0.02,bayestopt_.name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
||||
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');
|
||||
[ss, is] = sort(mmm);
|
||||
else
|
||||
mmm = (-idemodel.Mco');
|
||||
end
|
||||
% [ss, is] = sort(mmm);
|
||||
if SampleSize>1,
|
||||
myboxplot(idemodel.Mco(is,:)')
|
||||
else
|
||||
bar(idemodel.Mco(is,:)')
|
||||
end
|
||||
set(gca,'ylim',[0 1])
|
||||
set(gca,'xticklabel','')
|
||||
for ip=1:nparam,
|
||||
text(ip,-0.02,bayestopt_.name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
||||
text(ip,-0.02,name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
||||
end
|
||||
title('Multicollinearity in the model')
|
||||
saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_model'])
|
||||
eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_model']);
|
||||
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_model']);
|
||||
if options_.nograph, close(gcf); end
|
||||
end
|
||||
|
||||
figure('Name','Identification in the moments'),
|
||||
subplot(211)
|
||||
mmm = mean(siJmean);
|
||||
% mmm = mean(siHmean);
|
||||
% [ss, is] = sort(mmm);
|
||||
% myboxplot(siHmean(:,is))
|
||||
if SampleSize>1,
|
||||
mmm = mean(ide_strength_J);
|
||||
else
|
||||
mmm = (ide_strength_J);
|
||||
end
|
||||
[ss, is] = sort(mmm);
|
||||
myboxplot(siJmean(:,is))
|
||||
set(gca,'ylim',[0 1.05])
|
||||
if SampleSize>1,
|
||||
myboxplot(log(ide_strength_J(:,is)))
|
||||
else
|
||||
bar(ide_strength_J(:,is))
|
||||
end
|
||||
% set(gca,'ylim',[0 1.05])
|
||||
set(gca,'xticklabel','')
|
||||
dy = get(gca,'ylim');
|
||||
% dy=dy(2)-dy(1);
|
||||
for ip=1:nparam,
|
||||
text(ip,-0.02,bayestopt_.name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
||||
text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
||||
end
|
||||
title('Identification strength in the moments')
|
||||
|
||||
subplot(212)
|
||||
% mmm = mean(siJmean);
|
||||
% [ss, is] = sort(mmm);
|
||||
% myboxplot(siJmean(:,is))
|
||||
if SampleSize > 1,
|
||||
mmm = mean(siJnorm);
|
||||
else
|
||||
mmm = (siJnorm);
|
||||
end
|
||||
% [ss, is] = sort(mmm);
|
||||
if SampleSize > 1,
|
||||
myboxplot(log(siJnorm(:,is)))
|
||||
else
|
||||
bar(siJnorm(:,is))
|
||||
end
|
||||
% set(gca,'ylim',[0 1.05])
|
||||
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
|
||||
title('Sensitivity in the moments')
|
||||
|
||||
subplot(212)
|
||||
mmm = mean(-idemoments.Mco');
|
||||
[ss, is] = sort(mmm);
|
||||
myboxplot(idemoments.Mco(is,:)')
|
||||
set(gca,'ylim',[0 1])
|
||||
set(gca,'xticklabel','')
|
||||
for ip=1:nparam,
|
||||
text(ip,-0.02,bayestopt_.name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
||||
% subplot(313)
|
||||
% if SampleSize>1,
|
||||
% mmm = mean(-idemoments.Mco');
|
||||
% else
|
||||
% mmm = (-idemoments.Mco');
|
||||
% end
|
||||
% % [ss, is] = sort(mmm);
|
||||
% if SampleSize>1,
|
||||
% myboxplot(idemoments.Mco(is,:)')
|
||||
% else
|
||||
% bar(idemoments.Mco(is,:)')
|
||||
% end
|
||||
% set(gca,'ylim',[0 1])
|
||||
% set(gca,'xticklabel','')
|
||||
% for ip=1:nparam,
|
||||
% text(ip,-0.02,name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
||||
% end
|
||||
% title('Multicollinearity in the moments')
|
||||
% saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_moments'])
|
||||
% eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_moments']);
|
||||
% eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_moments']);
|
||||
% if options_.nograph, close(gcf); end
|
||||
|
||||
if SampleSize==1,
|
||||
% identificaton patterns
|
||||
[U,S,V]=svd(siJ./normJ(:,ones(nparam,1)),0);
|
||||
figure('name','Identification patterns (moments)'),
|
||||
for j=1:min(nparam,8),
|
||||
subplot(2,4,j),
|
||||
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,
|
||||
else
|
||||
bar(abs(V(:,j))),
|
||||
Stit = S(j,j);
|
||||
if j==5, ylabel('LARGEST singular values'), end,
|
||||
end
|
||||
set(gca,'xticklabel','')
|
||||
for ip=1:nparam,
|
||||
text(ip,-0.02,name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
||||
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']);
|
||||
end
|
||||
title('Multicollinearity in the moments')
|
||||
saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_moments'])
|
||||
eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_moments']);
|
||||
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_moments']);
|
||||
if options_.nograph, close(gcf); end
|
||||
|
||||
|
||||
if advanced,
|
||||
if SampleSize>1
|
||||
figure('Name','Condition Number'),
|
||||
subplot(221)
|
||||
hist(log10(idemodel.cond))
|
||||
|
|
@ -526,98 +772,178 @@ saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_COND'])
|
|||
eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_COND']);
|
||||
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_COND']);
|
||||
if options_.nograph, close(gcf); end
|
||||
ifig=0;
|
||||
nbox = min(estim_params_.np-1,12);
|
||||
for j=1:estim_params_.np,
|
||||
if mod(j,12)==1,
|
||||
ifig = ifig+1;
|
||||
figure('name','Partial correlations in the LRE model'),
|
||||
iplo=0;
|
||||
end
|
||||
iplo=iplo+1;
|
||||
mmm = mean(squeeze(idelre.Pco(:,j,:))');
|
||||
[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',[0 1])
|
||||
set(gca,'xticklabel','')
|
||||
for ip=1:nbox, %estim_params_.np,
|
||||
text(ip,-0.02,deblank(M_.param_names(estim_params_.param_vals(immm(ip+1),1),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
||||
end
|
||||
title(deblank(M_.param_names(estim_params_.param_vals(j,1),:))),
|
||||
if j==estim_params_.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','Partial correlations in the model'),
|
||||
iplo=0;
|
||||
end
|
||||
iplo=iplo+1;
|
||||
mmm = mean(squeeze(idemodel.Pco(:,j,:))');
|
||||
[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',[0 1])
|
||||
set(gca,'xticklabel','')
|
||||
for ip=1:nbox, %estim_params_.np,
|
||||
text(ip,-0.02,bayestopt_.name{immm(ip+1)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
||||
end
|
||||
title(bayestopt_.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','Partial correlations in the 1st and 2nd moments'),
|
||||
iplo=0;
|
||||
end
|
||||
iplo=iplo+1;
|
||||
mmm = mean(squeeze(idemoments.Pco(:,j,:))');
|
||||
[sss, immm] = sort(-mmm);
|
||||
subplot(3,4,iplo),
|
||||
if nbox==1,
|
||||
myboxplot(squeeze(idemoments.Pco(immm(2:nbox+1),j,:))),
|
||||
pco = NaN(np,np);
|
||||
for j=1:np,
|
||||
if SampleSize>1
|
||||
pco(j+1:end,j) = mean(abs(squeeze(idelre.Pco(j+1:end,j,:))'));
|
||||
else
|
||||
myboxplot(squeeze(idemoments.Pco(immm(2:nbox+1),j,:))'),
|
||||
end
|
||||
set(gca,'ylim',[0 1])
|
||||
set(gca,'xticklabel','')
|
||||
for ip=1:nbox, %estim_params_.np,
|
||||
text(ip,-0.02,bayestopt_.name{immm(ip+1)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
||||
end
|
||||
title(bayestopt_.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
|
||||
pco(j+1:end,j) = abs(idelre.Pco(j+1:end,j))';
|
||||
end
|
||||
end
|
||||
figure('name','Pairwise correlations in the LRE model'),
|
||||
imagesc(pco',[0 1]);
|
||||
set(gca,'xticklabel','')
|
||||
set(gca,'yticklabel','')
|
||||
for ip=1:nparam,
|
||||
text(ip,(0.5),name{ip},'rotation',90,'HorizontalAlignment','left','interpreter','none')
|
||||
text(0.5,ip,name{ip},'rotation',0,'HorizontalAlignment','right','interpreter','none')
|
||||
end
|
||||
colorbar;
|
||||
saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_PCORR_LRE'])
|
||||
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,
|
||||
if SampleSize>1
|
||||
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))';
|
||||
end
|
||||
end
|
||||
figure('name','Pairwise correlations in the model'),
|
||||
imagesc(pco',[0 1]);
|
||||
set(gca,'xticklabel','')
|
||||
set(gca,'yticklabel','')
|
||||
for ip=1:nparam,
|
||||
text(ip,(0.5),name{ip},'rotation',90,'HorizontalAlignment','left','interpreter','none')
|
||||
text(0.5,ip,name{ip},'rotation',0,'HorizontalAlignment','right','interpreter','none')
|
||||
end
|
||||
colorbar;
|
||||
saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_PCORR_model'])
|
||||
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,
|
||||
if SampleSize>1
|
||||
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))';
|
||||
end
|
||||
end
|
||||
figure('name','Pairwise correlations in the moments'),
|
||||
imagesc(pco',[0 1]);
|
||||
set(gca,'xticklabel','')
|
||||
set(gca,'yticklabel','')
|
||||
for ip=1:nparam,
|
||||
text(ip,(0.5),name{ip},'rotation',90,'HorizontalAlignment','left','interpreter','none')
|
||||
text(0.5,ip,name{ip},'rotation',0,'HorizontalAlignment','right','interpreter','none')
|
||||
end
|
||||
colorbar;
|
||||
saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_PCORR_moments'])
|
||||
eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_PCORR_moments']);
|
||||
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')
|
||||
|
|
@ -625,3 +951,8 @@ if exist('OCTAVE_VERSION')
|
|||
else
|
||||
warning on MATLAB:dividebyzero
|
||||
end
|
||||
|
||||
disp(' ')
|
||||
disp(['==== Identification analysis completed ====' ]),
|
||||
disp(' ')
|
||||
disp(' ')
|
||||
|
|
|
|||
Loading…
Reference in New Issue