Merge pull request #955 from JohannesPfeifer/identification_fixes

Various fixes to GSA and Identification

doc/dynare.texi

@@ -5358,6 +5358,7 @@ decomposition of the above k-step ahead filtered values. Stores results in @code
 
 
 @item diffuse_filter
+@anchor{diffuse_filter}
 Uses the diffuse Kalman filter (as described in
 @cite{Durbin and Koopman (2012)} and @cite{Koopman and Durbin
 (2003)}) to estimate models with non-stationary observed variables.
@@ -7238,8 +7239,8 @@ for identification analysis. Default: @code{0}
 @item ar = @var{INTEGER}
 Maximum number of lags for moments in identification analysis. Default: @code{1}
 
-@item lik_init = @var{INTEGER}
-@xref{lik_init}.
+@item diffuse_filter = @var{INTEGER}
+@xref{diffuse_filter}.
 
 @end table
 

matlab/dynare_identification.m

@@ -59,8 +59,24 @@ 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);
 options_ident = set_default_option(options_ident,'normalize_jacobians',1);
+
+%Deal with non-stationary cases
+if isfield(options_ident,'diffuse_filter') %set lik_init and options_
+    options_ident.lik_init=3; %overwrites any other lik_init set
+    options_.diffuse_filter=options_ident.diffuse_filter; %make options_ inherit diffuse filter; will overwrite any conflicting lik_init in dynare_estimation_init
+else
+    if options_.diffuse_filter==1 %warning if estimation with diffuse filter was done, but not passed
+        warning('IDENTIFICATION:: Previously the diffuse_filter option was used, but it was not passed to the identification command. This may result in problems if your model contains unit roots.')
+    end
+    if isfield(options_ident,'lik_init') 
+        if options_ident.lik_init==3 %user specified diffuse filter using the lik_init option
+            options_ident.analytic_derivation=0; %diffuse filter not compatible with analytic derivation
+        end
+    end
+end
 options_ident = set_default_option(options_ident,'lik_init',1);
 options_ident = set_default_option(options_ident,'analytic_derivation',1);
+
 if isfield(options_ident,'nograph'),
     options_.nograph=options_ident.nograph;
 end
@@ -70,6 +86,9 @@ end
 if isfield(options_ident,'graph_format'),
     options_.graph_format=options_ident.graph_format;
 end
+if isfield(options_ident,'prior_trunc'),
+    options_.prior_trunc=options_ident.prior_trunc;
+end
 
 if options_ident.gsa_sample_file,
     GSAFolder = checkpath('gsa',M_.dname);
@@ -122,8 +141,7 @@ options_.prior_mc = options_ident.prior_mc;
 options_.options_ident = options_ident;
 options_.Schur_vec_tol = 1.e-8;
 options_.nomoments=0;
-options_.analytic_derivation=1;
-
+options_ = set_default_option(options_,'analytic_derivation',1);
 options_ = set_default_option(options_,'datafile','');
 options_.mode_compute = 0;
 options_.plot_priors = 0;

matlab/gensylv/sylvester3.m

@@ -52,6 +52,7 @@ else
 end
 i = 1;
 c = zeros(n,1,p);
+c1 = zeros(n,1,p);
 while i < m
     if t(i+1,i) == 0
         if i == 1

matlab/getH.m

@@ -19,10 +19,18 @@ function [H, dA, dOm, Hss, gp, d2A, d2Om, H2ss] = getH(A, B, M_,oo_,options_,kro
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
-if nargin<6 || isempty(kronflag), kronflag = 0; end
-if nargin<7 || isempty(indx), indx = [1:M_.param_nbr]; end,
-if nargin<8 || isempty(indexo), indexo = []; end,
-if nargin<9 || isempty(iv), iv = (1:length(A))'; end,
+if nargin<6 || isempty(kronflag)
+    kronflag = 0; 
+end
+if nargin<7 || isempty(indx)
+    indx = []; 
+end
+if nargin<8 || isempty(indexo)
+    indexo = []; 
+end
+if nargin<9 || isempty(iv)
+    iv = (1:length(A))'; 
+end
 
 [I,J]=find(M_.lead_lag_incidence');
 yy0=oo_.dr.ys(I);

matlab/getJJ.m

@@ -17,10 +17,18 @@ function [JJ, H, gam, gp, dA, dOm, dYss] = getJJ(A, B, M_,oo_,options_,kronflag,
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
-if nargin<7 || isempty(indx), indx = [1:M_.param_nbr];, end,
-if nargin<8 || isempty(indexo), indexo = [];, end,
-if nargin<10 || isempty(nlags), nlags=3; end,
-if nargin<11 || isempty(useautocorr), useautocorr=0; end,
+if nargin<7 || isempty(indx)
+%     indx = [1:M_.param_nbr];
+end,
+if nargin<8 || isempty(indexo)
+    indexo = [];
+end,
+if nargin<10 || isempty(nlags)
+    nlags=3; 
+end
+if nargin<11 || isempty(useautocorr)
+    useautocorr=0; 
+end
 
 %   if useautocorr,
 warning('off','MATLAB:divideByZero')

matlab/gsa/map_ident_.m

@@ -64,7 +64,7 @@ if opt_gsa.load_ident_files==0,
   if opt_gsa.morris==2,
    pdraws = dynare_identification(options_.options_ident,[lpmatx lpmat(istable,:)]);
 %    [pdraws, TAU, GAM] = dynare_identification(options_.options_ident,[lpmatx lpmat(istable,:)]);
-    if max(max(abs(pdraws-[lpmatx lpmat(istable,:)])))==0,
+    if ~isempty(pdraws) && max(max(abs(pdraws-[lpmatx lpmat(istable,:)])))==0,
       disp(['Sample check OK ', num2str(max(max(abs(pdraws-[lpmatx lpmat(istable,:)]))))]),
       clear pdraws;
     end

matlab/gsa/mc_moments.m

@@ -20,7 +20,7 @@ function [vdec, cc, ac] = mc_moments(mm, ss, dr)
 global options_ M_ estim_params_ oo_
 
   [nr1, nc1, nsam] = size(mm);
-  nobs=size(options_.varobs,1);
+  nobs=size(options_.varobs,2);
   disp('Computing theoretical moments ...')
   h = dyn_waitbar(0,'Theoretical moments ...');
   vdec = zeros(nobs,M_.exo_nbr,nsam);
@@ -37,7 +37,7 @@ global options_ M_ estim_params_ oo_
     cc(:,:,j)=triu(corr);
     dum=[];
     for i=1:options_.ar
-    dum=[dum, autocorr{i}];
+        dum=[dum, autocorr{i}];
     end
     ac(:,:,j)=dum;
     dyn_waitbar(j/nsam,h)

matlab/gsa/prior_draw_gsa.m

@@ -42,7 +42,7 @@ function pdraw = prior_draw_gsa(init,rdraw)
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
-global bayestopt_ options_
+global bayestopt_ options_ estim_params_ M_
 persistent npar pshape p6 p7 p3 p4 lbcum ubcum
   
 if init
@@ -55,7 +55,18 @@ if init
     pdraw = zeros(npar,1);
     lbcum = zeros(npar,1);
     ubcum = ones(npar,1);
-    bounds = prior_bounds(bayestopt_,options_);
+    [junk1,junk2,junk3,lb,ub,junk4] = set_prior(estim_params_,M_,options_); %Prepare bounds
+    if ~isempty(bayestopt_) && any(bayestopt_.pshape > 0)
+        % Set prior bounds
+        bounds = prior_bounds(bayestopt_,options_);
+        bounds.lb = max(bounds.lb,lb);
+        bounds.ub = min(bounds.ub,ub);
+    else  % estimated parameters but no declared priors
+        % No priors are declared so Dynare will estimate the model by
+        % maximum likelihood with inequality constraints for the parameters.
+        bounds.lb = lb;
+        bounds.ub = ub;
+    end
     % set bounds for cumulative probabilities
     for i = 1:npar
       switch pshape(i)
@@ -63,8 +74,8 @@ if init
           p4(i) = min(p4(i),bounds.ub(i));
           p3(i) = max(p3(i),bounds.lb(i));
         case 3% Gaussian prior.
-          lbcum(i) = 0.5 * erfc(-(bounds.lb(i)-p6(i))/p7(i) ./ sqrt(2));;
-          ubcum(i) = 0.5 * erfc(-(bounds.ub(i)-p6(i))/p7(i) ./ sqrt(2));;
+          lbcum(i) = 0.5 * erfc(-(bounds.lb(i)-p6(i))/p7(i) ./ sqrt(2));
+          ubcum(i) = 0.5 * erfc(-(bounds.ub(i)-p6(i))/p7(i) ./ sqrt(2));
         case 2% Gamma prior.
           lbcum(i) = gamcdf(bounds.lb(i)-p3(i),p6(i),p7(i));
           ubcum(i) = gamcdf(bounds.ub(i)-p3(i),p6(i),p7(i));

matlab/gsa/set_shocks_param.m

 function set_shocks_param(xparam1)
+% function set_shocks_param(xparam1)
+% Set the structural and measurement error variances and covariances
 
-% Copyright (C) 2012 Dynare Team
+% Copyright (C) 2012-2015 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -18,31 +20,86 @@ function set_shocks_param(xparam1)
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
 global estim_params_ M_
-  
-  nvx = estim_params_.nvx;
-  ncx = estim_params_.ncx;
-  np = estim_params_.np;
-  Sigma_e = M_.Sigma_e;
-  offset = 0;
-  if nvx
-    offset = offset + nvx;
+
+nvx = estim_params_.nvx;
+ncx = estim_params_.ncx;
+nvn = estim_params_.nvn;
+ncn = estim_params_.ncn;
+
+Sigma_e = M_.Sigma_e;
+Correlation_matrix = M_.Correlation_matrix;
+
+H = M_.H;
+Correlation_matrix_ME = M_.Correlation_matrix_ME;
+% setting shocks variance on the diagonal of Covariance matrix; used later
+% for updating covariances
+if nvx
     var_exo = estim_params_.var_exo;
     for i=1:nvx
-      k = var_exo(i,1);
-      Sigma_e(k,k) = xparam1(i)^2;
+        k =var_exo(i,1);
+        Sigma_e(k,k) = xparam1(i)^2;
     end
-  end
-  
-  if ncx
-    offset = offset + estim_params_.nvn;
+end
+% update offset
+offset = nvx;
+
+% setting measument error variance; on the diagonal of Covariance matrix; used later
+% for updating covariances
+if nvn
+    for i=1:nvn
+        k = estim_params_.nvn_observable_correspondence(i,1);
+        H(k,k) = xparam1(i+offset)^2;
+    end
+end
+
+% update offset
+offset = nvx+nvn;
+
+% setting shocks covariances
+if ncx
     corrx = estim_params_.corrx;
     for i=1:ncx
-      k1 = corrx(i,1);
-      k2 = corrx(i,2);
-      Sigma_e(k1,k2) = xparam1(i+offset)*sqrt(Sigma_e_(k1,k1)*Sigma_e_(k2,k2));
-      Sigma_e(k2,k1) = Sigma_e_(k1,k2);
+        k1 = corrx(i,1);
+        k2 = corrx(i,2);
+        Correlation_matrix(k1,k2) = xparam1(i+offset);
+        Correlation_matrix(k2,k1) = Correlation_matrix(k1,k2);
     end
-  end
-  
+end
+%build covariance matrix from correlation matrix and variances already on
+%diagonal
+Sigma_e = diag(sqrt(diag(Sigma_e)))*Correlation_matrix*diag(sqrt(diag(Sigma_e))); 
+%if calibrated covariances, set them now to their stored value
+if isfield(estim_params_,'calibrated_covariances')
+    Sigma_e(estim_params_.calibrated_covariances.position)=estim_params_.calibrated_covariances.cov_value;
+end
+% update offset
+offset = nvx+nvn+ncx;
+
+% setting measurement error covariances
+if ncn
+    corrn_observable_correspondence = estim_params_.corrn_observable_correspondence;
+    for i=1:ncn
+        k1 = corrn_observable_correspondence(i,1);
+        k2 = corrn_observable_correspondence(i,2);
+        Correlation_matrix_ME(k1,k2) = xparam1(i+offset);
+        Correlation_matrix_ME(k2,k1) = Correlation_matrix_ME(k1,k2);
+    end
+end
+%build covariance matrix from correlation matrix and variances already on
+%diagonal
+H = diag(sqrt(diag(H)))*Correlation_matrix_ME*diag(sqrt(diag(H)));
+%if calibrated covariances, set them now to their stored value
+if isfield(estim_params_,'calibrated_covariances_ME')
+    H(estim_params_.calibrated_covariances_ME.position)=estim_params_.calibrated_covariances_ME.cov_value;
+end
+
   
-  M_.Sigma_e = Sigma_e;
\ No newline at end of file
+% updating matrices in M
+if nvx || ncx
+    M_.Sigma_e = Sigma_e;
+    M_.Correlation_matrix=Correlation_matrix;
+end
+if nvn || ncn
+    M_.H = H;
+    M_.Correlation_matrix_ME=Correlation_matrix_ME;    
+end 
\ No newline at end of file

matlab/gsa/stab_map_.m

@@ -87,7 +87,7 @@ nshock = estim_params_.nvx;
 nshock = nshock + estim_params_.nvn;
 nshock = nshock + estim_params_.ncx;
 nshock = nshock + estim_params_.ncn;
-lpmat0=[];
+lpmat0=zeros(Nsam,0);
 xparam1=[];
 
 pshape = bayestopt_.pshape(nshock+1:end);
@@ -96,7 +96,22 @@ p2 = bayestopt_.p2(nshock+1:end);
 p3 = bayestopt_.p3(nshock+1:end);
 p4 = bayestopt_.p4(nshock+1:end);
 
-bounds = prior_bounds(bayestopt_,options_);
+[junk1,junk2,junk3,lb,ub,junk4] = set_prior(estim_params_,M_,options_); %Prepare bounds
+if ~isempty(bayestopt_) && any(bayestopt_.pshape > 0)
+    % Set prior bounds
+    bounds = prior_bounds(bayestopt_,options_);
+    bounds.lb = max(bounds.lb,lb);
+    bounds.ub = min(bounds.ub,ub);
+else  % estimated parameters but no declared priors
+    % No priors are declared so Dynare will estimate the model by
+    % maximum likelihood with inequality constraints for the parameters.
+    bounds.lb = lb;
+    bounds.ub = ub;
+    if opt_gsa.prior_range==0
+        warning('GSA:: When using ML, sampling from the prior is not possible. Setting prior_range=1')
+        opt_gsa.prior_range=1;
+    end
+end
 
 if nargin==0,
     OutputDirectoryName='';
@@ -143,7 +158,7 @@ if fload==0,
                 end
             end
         else %ilptau==0
-            %[lpmat] = rand(Nsam,np);
+            [lpmat] = NaN(Nsam,np);
             for j=1:np,
                 lpmat(:,j) = randperm(Nsam)'./(Nsam+1); %latin hypercube
             end
@@ -151,7 +166,7 @@ if fload==0,
         end
     end
     %   try
-    dummy=prior_draw_gsa(1);
+    dummy=prior_draw_gsa(1); %initialize persistent variables
     %   catch
     %     if pprior,
     %       if opt_gsa.prior_range==0;
@@ -242,6 +257,7 @@ if fload==0,
         else
             d = chol(inv(hh));
             lp=randn(Nsam*2,nshock+np)*d+kron(ones(Nsam*2,1),xparam1');
+            lnprior=zeros(1,Nsam*2);
             for j=1:Nsam*2,
                 lnprior(j) = any(lp(j,:)'<=bounds.lb | lp(j,:)'>=bounds.ub);
             end
@@ -263,6 +279,7 @@ if fload==0,
     iwrong=zeros(1,Nsam);
     inorestriction=zeros(1,Nsam);
     irestriction=zeros(1,Nsam);
+    infox=zeros(1,Nsam);
     for j=1:Nsam,
         M_ = set_all_parameters([lpmat0(j,:) lpmat(j,:)]',estim_params_,M_);
         %try stoch_simul([]);
@@ -273,7 +290,7 @@ if fload==0,
                 [Tt,Rr,SteadyState,info,M_,options_,oo_] = dynare_resolve(M_,options_,oo_,'restrict');
             end
             infox(j,1)=info(1);
-            if infox(j,1)==0 && ~exist('T'),
+            if infox(j,1)==0 && ~exist('T','var'),
                 dr_=oo_.dr;
                 if prepSA,
                     try
@@ -321,7 +338,7 @@ if fload==0,
                 %           bayestopt_.restrict_columns, ...
                 %           bayestopt_.restrict_aux);
             end
-            if ~exist('nspred'),
+            if ~exist('nspred','var'),
                 nspred = dr_.nspred; %size(dr_.ghx,2);
                 nboth = dr_.nboth;
                 nfwrd = dr_.nfwrd;
@@ -339,7 +356,7 @@ if fload==0,
             istable(j)=0;
             if isfield(dr_,'eigval')
                 egg(:,j) = sort(dr_.eigval);
-                if exist('nspred')
+                if exist('nspred','var')
                     if any(isnan(egg(1:nspred,j)))
                         iwrong(j)=j;
                     else
@@ -349,7 +366,7 @@ if fload==0,
                     end
                 end
             else
-                if exist('egg'),
+                if exist('egg','var'),
                     egg(:,j)=ones(size(egg,1),1).*NaN;
                 end
                 iwrong(j)=j;
@@ -457,6 +474,7 @@ else
         stoch_simul([]);
         %T=zeros(size(dr_.ghx,1),size(dr_.ghx,2)+size(dr_.ghu,2),length(istable));
         ntrans=length(istable);
+        yys=NaN(length(ys_),ntrans);
         for j=1:ntrans,
             M_.params(estim_params_.param_vals(:,1)) = lpmat(istable(j),:)';
             %stoch_simul([]);
@@ -465,12 +483,12 @@ else
             %[Tt,Rr,SteadyState,info] = dynare_resolve(bayestopt_.restrict_var_list,...
             %    bayestopt_.restrict_columns,...
             %    bayestopt_.restrict_aux);
-            if ~exist('T')
+            if ~exist('T','var')
                 T=zeros(size(dr_.ghx,1),size(dr_.ghx,2)+size(dr_.ghu,2),ntrans);
             end
             dr_ = oo_.dr;
             T(:,:,j) = [dr_.ghx dr_.ghu];
-            if ~exist('nspred')
+            if ~exist('nspred','var')
                 nspred = dr_.nspred; %size(dr_.ghx,2);
                 nboth = dr_.nboth;
                 nfwrd = dr_.nfwrd;

matlab/gsa/th_moments.m

 function [vdec, corr, autocorr, z, zz] = th_moments(dr,var_list)
+% [vdec, corr, autocorr, z, zz] = th_moments(dr,var_list)
 
-% Copyright (C) 2012 Dynare Team
+% Copyright (C) 2012-2015 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -17,38 +18,38 @@ function [vdec, corr, autocorr, z, zz] = th_moments(dr,var_list)
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
-  global M_ oo_ options_
-  
-  nvar = size(var_list,1);
-  if nvar == 0
+global M_ oo_ options_
+
+nvar = size(var_list,2);
+if nvar == 0
     nvar = length(dr.order_var);
     ivar = [1:nvar]';
-  else
+else
     ivar=zeros(nvar,1);
     for i=1:nvar
-      i_tmp = strmatch(var_list(i,:),M_.endo_names,'exact');
-      if isempty(i_tmp)
-      	error (['One of the variable specified does not exist']) ;
-      else
-	ivar(i) = i_tmp;
-      end
+        i_tmp = strmatch(var_list(:,i),M_.endo_names,'exact');
+        if isempty(i_tmp)
+            error(['One of the variables specified does not exist']) ;
+        else
+            ivar(i) = i_tmp;
+        end
     end
-  end
-  
-  [gamma_y,stationary_vars] = th_autocovariances(dr,ivar,M_, options_);
-  m = dr.ys(ivar(stationary_vars));
+end
+
+[gamma_y,stationary_vars] = th_autocovariances(dr,ivar,M_, options_);
+m = dr.ys(ivar(stationary_vars));
+
 
-  
 %   i1 = find(abs(diag(gamma_y{1})) > 1e-12);
-  i1 = [1:length(ivar)];
-  s2 = diag(gamma_y{1});
-  sd = sqrt(s2);
+i1 = [1:length(ivar)];
+s2 = diag(gamma_y{1});
+sd = sqrt(s2);
+
+
+z = [ m sd s2 ];
+mean = m;
+var = gamma_y{1};
 
-  
-  z = [ m sd s2 ];
-  mean = m;
-  var = gamma_y{1};
-  
 
 %'THEORETICAL MOMENTS';
 %'MEAN','STD. DEV.','VARIANCE');
@@ -56,28 +57,27 @@ z;
 
 %'VARIANCE DECOMPOSITION (in percent)';
 if M_.exo_nbr>1,
-vdec = 100*gamma_y{options_.ar+2}(i1,:);
+    vdec = 100*gamma_y{options_.ar+2}(i1,:);
 else
-vdec = 100*ones(size(gamma_y{1}(i1,1)));
-end  
+    vdec = 100*ones(size(gamma_y{1}(i1,1)));
+end
 %'MATRIX OF CORRELATIONS';
 if options_.opt_gsa.useautocorr,
     corr = gamma_y{1}(i1,i1)./(sd(i1)*sd(i1)');
     corr = corr-diag(diag(corr))+diag(diag(gamma_y{1}(i1,i1)));
 else
-  corr = gamma_y{1}(i1,i1);
+    corr = gamma_y{1}(i1,i1);
 end
-  if options_.ar > 0
-%'COEFFICIENTS OF AUTOCORRELATION';
+if options_.ar > 0
+    %'COEFFICIENTS OF AUTOCORRELATION';
     for i=1:options_.ar
-      if options_.opt_gsa.useautocorr,
-      autocorr{i} = gamma_y{i+1}(i1,i1);
-      else
-      autocorr{i} = gamma_y{i+1}(i1,i1).*(sd(i1)*sd(i1)');
-      end
-      zz(:,i) = diag(gamma_y{i+1}(i1,i1));
+        if options_.opt_gsa.useautocorr,
+            autocorr{i} = gamma_y{i+1}(i1,i1);
+        else
+            autocorr{i} = gamma_y{i+1}(i1,i1).*(sd(i1)*sd(i1)');
+        end
+        zz(:,i) = diag(gamma_y{i+1}(i1,i1));
     end
-  end
-  
+end
+
 
-  
\ No newline at end of file

matlab/identification_analysis.m

@@ -86,7 +86,7 @@ if info(1)==0,
     if init,
         indJJ = (find(max(abs(JJ'),[],1)>1.e-8));
         if isempty(indJJ) && any(any(isnan(JJ)))
-            error('There are NaN in the JJ matrix. Please check whether your model has units roots and you forgot to set lik_init~=1.' )
+            error('There are NaN in the JJ matrix. Please check whether your model has units roots and you forgot to set diffuse_filter=1.' )
         end
         while length(indJJ)<nparam && nlags<10,
             disp('The number of moments with non-zero derivative is smaller than the number of parameters')
@@ -118,14 +118,14 @@ if info(1)==0,
     ide_strength_J_prior=NaN(1,nparam);
     if init, %~isempty(indok),
         normaliz = abs(params);
-        if prior_exist,
+        if prior_exist,           
             if ~isempty(estim_params_.var_exo),
-                normaliz1 = estim_params_.var_exo(:,7); % normalize with prior standard deviation
+                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
+                normaliz1 = [normaliz1 estim_params_.param_vals(:,7)']; % normalize with prior standard deviation
             end
             %                         normaliz = max([normaliz; normaliz1]);
             normaliz1(isinf(normaliz1)) = 1;

matlab/identification_checks.m

@@ -82,7 +82,7 @@ end
 
 
 ixnoJ = 0;
-if rankJ<npar || rankJJ<npar || min(1-McoJ)<1.e-10
+if npar>0 && (rankJ<npar || rankJJ<npar || min(1-McoJ)<1.e-10)
     %         - find out which parameters are involved
     %   disp('Some parameters are NOT identified by the moments included in J')
     %   disp(' ')

matlab/plot_identification.m

@@ -56,14 +56,22 @@ if SampleSize == 1,
     subplot(211)
     mmm = (idehess.ide_strength_J);
     [ss, is] = sort(mmm);
-    bar(log([idehess.ide_strength_J(:,is)' idehess.ide_strength_J_prior(:,is)']))
+    if ~all(isnan(idehess.ide_strength_J_prior))
+        bar(log([idehess.ide_strength_J(:,is)' idehess.ide_strength_J_prior(:,is)']))
+    else
+        bar(log([idehess.ide_strength_J(:,is)' ]))
+    end
     set(gca,'xlim',[0 nparam+1])
     set(gca,'xticklabel','')
     dy = get(gca,'ylim');
     for ip=1:nparam,
         text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
     end
-    legend('relative to param value','relative to prior std','Location','Best')
+    if ~all(isnan(idehess.ide_strength_J_prior))