matlab/disp_th_moments.m

@@ -44,7 +44,7 @@ m(non_stationary_vars) = NaN;
 i1 = find(abs(diag(oo_.gamma_y{1})) > 1e-12);
 s2 = diag(oo_.gamma_y{1});
 sd = sqrt(s2);
-if options_.order == 2
+if options_.order == 2 && ~M_.hessian_eq_zero
     m = m+oo_.gamma_y{options_.ar+3};
 end
 
@@ -123,7 +123,8 @@ if length(i1) == 0
 end
 
 if options_.nocorr == 0 && size(stationary_vars, 1) > 0
-    corr = oo_.gamma_y{1}(i1,i1)./(sd(i1)*sd(i1)');
+    corr=NaN(size(oo_.gamma_y{1}));
+    corr(i1,i1) = oo_.gamma_y{1}(i1,i1)./(sd(i1)*sd(i1)');
     if options_.contemporaneous_correlation 
         oo_.contemporaneous_correlation = corr;
     end
@@ -138,12 +139,12 @@ if options_.nocorr == 0 && size(stationary_vars, 1) > 0
         labels = deblank(M_.endo_names(ivar(i1),:));
         headers = char('Variables',labels);
         lh = size(labels,2)+2;
-        dyntable(options_,title,headers,labels,corr,lh,8,4);
+        dyntable(options_,title,headers,labels,corr(i1,i1),lh,8,4);
         if options_.TeX
             labels = deblank(M_.endo_names_tex(ivar(i1),:));
             headers=char('Variables',labels);
             lh = size(labels,2)+2;
-            dyn_latex_table(M_,options_,title,'th_corr_matrix',headers,labels,corr,lh,8,4);
+            dyn_latex_table(M_,options_,title,'th_corr_matrix',headers,labels,corr(i1,i1),lh,8,4);
         end
     end
 end

matlab/distributions/lpdfgweibull.m

@@ -206,7 +206,7 @@ end
 %$
 %$ try
 %$    if isoctave
-%$        s = quadv(density, .0000000001, 100000,1e-10);
+%$        s = quadl(density, .0000000001, 100000, 1e-10);
 %$    else
 %$        s = integral(density, 0, 100000);
 %$    end
@@ -229,7 +229,7 @@ end
 %$
 %$ try
 %$    if isoctave
-%$        s = quadv(density, .0000000001, 100000,1e-10);
+%$        s = quadl(density, .0000000001, 100000, 1e-10);
 %$    else
 %$        s = integral(density, 0, 100000);
 %$    end
@@ -252,7 +252,7 @@ end
 %$
 %$ try
 %$    if isoctave
-%$        s = quadv(density, .0000000001, 100000,1e-10)
+%$        s = quadl(density, .0000000001, 100000, 1e-10);
 %$    else
 %$        s = integral(density, 0, 100000);
 %$    end
@@ -279,7 +279,7 @@ end
 %$
 %$ try
 %$    if isoctave
-%$        s = quadv(xdens, .0000000001, 20,1e-10)
+%$        s = quadgk(xdens, .0000000001, 100000, 1e-10);
 %$    else
 %$        s = integral(xdens, 0, 100000);
 %$    end
@@ -302,7 +302,7 @@ end
 %$
 %$ try
 %$    if isoctave
-%$        s = quadv(xdens, .0000000001, 100000,1e-10)
+%$        s = quadl(xdens, .0000000001, 100000, 1e-10);
 %$    else
 %$        s = integral(xdens, 0, 100000);
 %$    end
@@ -325,7 +325,7 @@ end
 %$
 %$ try
 %$    if isoctave
-%$        s = quadv(xdens, .0000000001, 100000,1e-10)
+%$        s = quadl(xdens, .0000000001, 100000, 1e-10);
 %$    else
 %$        s = integral(xdens, 0, 100000);
 %$    end
@@ -351,7 +351,7 @@ end
 %$    s = NaN(n, 1);
 %$    for i=1:n
 %$        if isoctave()
-%$            s(i) = quadv(density, .0000000001, .1*i,1e-10)
+%$             s(i) = quadl(density, .0000000001, .1*i, 1e-10);
 %$        else
 %$            s(i) = integral(density, 0, .1*i);
 %$        end
@@ -382,7 +382,7 @@ end
 %$    s = NaN(n, 1);
 %$    for i=1:n
 %$        if isoctave()
-%$            s(i) = quadv(density, .0000000001, .1*i,1e-10)
+%$            s(i) = quadl(density, .0000000001, .1*i, 1e-10);
 %$        else
 %$            s(i) = integral(density, 0, .1*i);
 %$        end
@@ -413,7 +413,7 @@ end
 %$    s = NaN(n, 1);
 %$    for i=1:n
 %$        if isoctave()
-%$            s(i) = quadv(density, .0000000001, .1*i,1e-10)
+%$            s(i) = quadl(density, .0000000001, .1*i, 1e-10);
 %$        else
 %$            s(i) = integral(density, 0, .1*i);
 %$        end

matlab/dsge_likelihood.m

@@ -115,7 +115,7 @@ function [fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff,Model,DynareOpti
 %! @end deftypefn
 %@eod:
 
-% Copyright (C) 2004-2017 Dynare Team
+% Copyright (C) 2004-2018 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -146,20 +146,6 @@ Hess        = [];
 % Ensure that xparam1 is a column vector.
 xparam1 = xparam1(:);
 
-if DynareOptions.estimation_dll
-    [fval,exit_flag,SteadyState,trend_coeff,info,params,H,Q] ...
-        = logposterior(xparam1,DynareDataset, DynareOptions,Model, ...
-                       EstimatedParameters,BayesInfo,DynareResults);
-    mexErrCheck('logposterior', exit_flag);
-    Model.params = params;
-    if ~isequal(Model.H,0)
-        Model.H = H;
-    end
-    Model.Sigma_e = Q;
-    DynareResults.dr.ys = SteadyState;
-    return
-end
-
 % Set flag related to analytical derivatives.
 analytic_derivation = DynareOptions.analytic_derivation;
 
@@ -372,7 +358,7 @@ switch DynareOptions.lik_init
         error(['The model requires Diffuse filter, but you specified a different Kalman filter. You must set options_.kalman_algo ' ...
                'to 0 (default), 3 or 4'])
     end
-    [Pstar,Pinf] = compute_Pinf_Pstar(Z,T,R,Q,DynareOptions.qz_criterium,[1:length(T)]);
+    [Pstar,Pinf] = compute_Pinf_Pstar(Z,T,R,Q,DynareOptions.qz_criterium);
     Z =zeros(length(BayesInfo.mf),size(T,1));
     for i = 1:length(BayesInfo.mf)
         Z(i,BayesInfo.mf(i))=1;

matlab/dsge_simulated_theoretical_conditional_variance_decomposition.m

@@ -109,10 +109,10 @@ for file = 1:NumberOfDrawsFiles
     for linee = 1:NumberOfDraws
         linea = linea+1;
         if isdrsaved
-            set_parameters(pdraws{linee,1});% Needed to update the covariance matrix of the state innovations.
+            M_=set_parameters_locally(M_,pdraws{linee,1});% Needed to update the covariance matrix of the state innovations.
             dr = pdraws{linee,2};
         else
-            set_parameters(pdraws{linee,1});
+            M_=set_parameters_locally(M_,pdraws{linee,1});
             [dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
         end
         if first_call

matlab/dsge_simulated_theoretical_correlation.m

@@ -104,9 +104,10 @@ for file = 1:NumberOfDrawsFiles
     for linee = 1:NumberOfDraws
         linea = linea+1;
         if isdrsaved
+            M_=set_parameters_locally(M_,pdraws{linee,1});% Needed to update the covariance matrix of the state innovations.
             dr = pdraws{linee,2};
         else
-            set_parameters(pdraws{linee,1});
+            M_=set_parameters_locally(M_,pdraws{linee,1});
             [dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
         end
         tmp = th_autocovariances(dr,ivar,M_,options_,nodecomposition);

matlab/dsge_simulated_theoretical_covariance.m

@@ -103,9 +103,10 @@ for file = 1:NumberOfDrawsFiles
     for linee = 1:NumberOfDraws
         linea = linea+1;
         if isdrsaved
+            M_=set_parameters_locally(M_,pdraws{linee,1});% Needed to update the covariance matrix of the state innovations.
             dr = pdraws{linee,2};
         else
-            set_parameters(pdraws{linee,1});
+            M_=set_parameters_locally(M_,pdraws{linee,1});
             [dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
         end
         tmp = th_autocovariances(dr,ivar,M_,options_,nodecomposition);

matlab/dsge_simulated_theoretical_variance_decomposition.m

@@ -54,7 +54,7 @@ end
 if posterior
     delete_stale_file([M_.dname '/metropolis/' M_.fname '_PosteriorVarianceDecomposition*']);
 else
-    delete_stale_file([M_.dname '/prior/moments/' M_.fname '_PosteriorVarianceDecomposition*']);
+    delete_stale_file([M_.dname '/prior/moments/' M_.fname '_PriorVarianceDecomposition*']);
 end
 
 % Set varlist (vartan)
@@ -111,9 +111,10 @@ for file = 1:NumberOfDrawsFiles
     for linee = 1:NumberOfDraws
         linea = linea+1;
         if isdrsaved
+            M_=set_parameters_locally(M_,pdraws{linee,1});% Needed to update the covariance matrix of the state innovations.
             dr = pdraws{linee,2};
         else
-            set_parameters(pdraws{linee,1});
+            M_=set_parameters_locally(M_,pdraws{linee,1});
             [dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
         end
         if file==1 && linee==1

matlab/dyn_first_order_solver.m

@@ -236,7 +236,6 @@ else
         else
             dr.full_rank = 1;
         end
-        return
     end
 
     if nba ~= nsfwrd
@@ -251,6 +250,9 @@ else
         info(2) = temp'*temp;
         return
     end
+
+    if task==1, return, end
+
     %First order approximation
     indx_stable_root = 1: (nd - nsfwrd);         %=> index of stable roots
     indx_explosive_root = npred + nboth + 1:nd;  %=> index of explosive roots

matlab/dyn_forecast.m

@@ -151,12 +151,17 @@ else
     elseif horizon <= exo_det_length
         ex = zeros(exo_det_length,M.exo_nbr);
     end
+    if options.linear
+        iorder = 1;
+    else
+        iorder = options.order;
+    end
     if isequal(M.H,0)
         [yf,int_width] = simultxdet(y0,ex,oo.exo_det_simul,...
-                                    options.order,var_list,M,oo,options);
+                                    iorder,var_list,M,oo,options);
     else
         [yf,int_width,int_width_ME] = simultxdet(y0,ex,oo.exo_det_simul,...
-                                                 options.order,var_list,M,oo,options);
+                                                 iorder,var_list,M,oo,options);
     end
 end
 

matlab/dynare.m

@@ -50,9 +50,9 @@ change_path_flag = true;
 % Filter out some options.
 if nargin>1
     id = strfind(varargin,'nopathchange');
-    if ~isempty(id)
+    if ~all(cellfun(@isempty, id))
         change_path_flag = false;
-        varargin(id{1}) = [];
+        varargin(cellfun(@isempty, id) == 0) = [];
     end
 end
 
@@ -65,8 +65,20 @@ dynareroot = dynare_config;
 warning_config()
 
 if isoctave
-    if octave_ver_less_than('3.6.0')
-        warning('This version of Dynare has only been tested on Octave 3.6.0 and above. Since your Octave version is older than that, Dynare may fail to run, or give unexpected results. Consider upgrading your Octave installation.');
+    % The supported_octave_version.m file is not in git nor in the source
+    % package, it is manually added in binary packages distributed on dynare.org
+    if exist('supported_octave_version', 'file') && ~strcmp(supported_octave_version, version)
+        skipline()
+        warning(['This version of Octave is not supported. Consider installing ' ...
+                 'version %s of Octave\n' ...
+                 'from www.octave.org, otherwise m files will be used instead ' ...
+                 'of precompiled mex files and some\nfeatures, like solution ' ...
+                 'of models approximated at third order, will not be available.'], supported_octave_version())
+        skipline()
+    elseif octave_ver_less_than('3.6') % Should match the test in mex/build/octave/configure.ac
+        skipline()
+        warning(['This version of Dynare has only been tested on Octave 3.6 and above. Dynare may fail to run or give unexpected result. Consider upgrading your version of Octave.'])
+        skipline()
     end
 else
     if matlab_ver_less_than('7.5')

matlab/dynare_config.m

@@ -75,39 +75,25 @@ p = {'/distributions/' ; ...
 % For functions that exist only under some Octave versions
 % or some MATLAB versions, and for which we provide some replacement functions
 
+% Replacements for rows(), columns(), vec() and issquare() (inexistent under MATLAB)
 if ~isoctave
-    % Replacements for rows(), columns() and issquare() (inexistent under MATLAB)
     p{end+1} = '/missing/rows_columns';
     p{end+1} = '/missing/issquare';
-    % Replacement for vec() (inexistent under MATLAB)
     p{end+1} = '/missing/vec';
-    if ~user_has_matlab_license('statistics_toolbox')
-        % Replacements for functions of the stats toolbox
-        p{end+1} = '/missing/stats/';
-    end
 end
 
-% ordeig() doesn't exist in Octave
-if isoctave
+% ordeig() doesn't exist in Octave < 5
+if isoctave && octave_ver_less_than('5')
     p{end+1} = '/missing/ordeig';
 end
 
-if isoctave && ~isequal(supported_octave_version(), version())
-    skipline()
-    warning(['This version of Octave is not supported. Consider installing ' ...
-             'version %s of Octave,\notherwise m files will be used instead ' ...
-             'of precompiled mex files and some features, like solution\n' ...
-             'of models approximated at third order, will not be available.'], supported_octave_version())
-    skipline()
-end
-
 % ilu is missing in Octave < 4.0
 if isoctave && octave_ver_less_than('4.0')
     p{end+1} = '/missing/ilu';
 end
 
-% corrcoef with two outputs is missing in Octave (ticket #796)
-if isoctave && ~user_has_octave_forge_package('nan')
+% corrcoef with two outputs is missing in Octave < 4.4 (ticket #796)
+if isoctave && octave_ver_less_than('4.4') && ~user_has_octave_forge_package('nan')
     p{end+1} = '/missing/corrcoef';
 end
 
@@ -118,11 +104,36 @@ if (isoctave && ~user_has_octave_forge_package('statistics')) ...
     p{end+1} = '/missing/nanmean';
 end
 
+% Replacements for functions of the MATLAB statistics toolbox
+% These functions were part of Octave < 4.4, they are now in the statistics Forge package
+if (isoctave && ~octave_ver_less_than('4.4') && ~user_has_octave_forge_package('statistics')) ...
+        || (~isoctave && ~user_has_matlab_license('statistics_toolbox'))
+    p{end+1} = '/missing/stats/';
+    if ~isoctave
+        p{end+1} = '/missing/stats-matlab/';
+    end
+end
+
 % Check if struct2array is available.
 if ~exist('struct2array')
     p{end+1} = '/missing/struct2array';
 end
 
+% isfile is missing in Octave < 5 and Matlab < R2017b
+if (isoctave && octave_ver_less_than('5')) || (~isoctave && matlab_ver_less_than('9.3'))
+    p{end+1} = '/missing/isfile';
+end
+
+% strsplit is missing in Matlab<R2013a
+if ~isoctave && matlab_ver_less_than('8.1')
+    p{end+1} = '/missing/strsplit';
+end
+
+% isrow, iscolumn and ismatrix are missing in Matlab<R2010b
+if ~isoctave && matlab_ver_less_than('7.11')
+    p{end+1} = '/missing/is-row-column-matrix';
+end
+
 P = cellfun(@(c)[dynareroot(1:end-1) c], p, 'uni',false);
 
 % Get mex files folder(s)

matlab/dynare_estimation_1.m

@@ -235,7 +235,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
                 [junk1, junk2,junk3, junk4, hh] = feval(objective_function,xparam1, ...
                                                         dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
                 options_.analytic_derivation = ana_deriv_old;
-            elseif ~isnumeric(options_.mode_compute) || ~(isequal(options_.mode_compute,5) && newratflag~=1)
+            elseif ~isnumeric(options_.mode_compute) || ~(isequal(options_.mode_compute,5) && newratflag~=1 && strcmp(func2str(objective_function),'dsge_likelihood'))
                 % with flag==0, we force to use the hessian from outer product gradient of optimizer 5
                 if options_.hessian.use_penalized_objective
                     penalized_objective_function = str2func('penalty_objective_function');
@@ -419,6 +419,7 @@ end
 
 if (any(bayestopt_.pshape  >0 ) && options_.mh_replic) || ...
         (any(bayestopt_.pshape >0 ) && options_.load_mh_file)  %% not ML estimation
+    bounds = prior_bounds(bayestopt_, options_.prior_trunc); %reset bounds as lb and ub must only be operational during mode-finding
     outside_bound_pars=find(xparam1 < bounds.lb | xparam1 > bounds.ub);
     if ~isempty(outside_bound_pars)
         for ii=1:length(outside_bound_pars)
@@ -519,7 +520,7 @@ if (any(bayestopt_.pshape  >0 ) && options_.mh_replic) || ...
             end
             prior_posterior_statistics('posterior',dataset_,dataset_info);
         end
-        xparam1 = get_posterior_parameters('mean');
+        xparam1 = get_posterior_parameters('mean',M_,estim_params_,oo_,options_);
         M_ = set_all_parameters(xparam1,estim_params_,M_);
     end
 end
@@ -609,10 +610,10 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
     if nvn
         number_of_plots_to_draw = 0;
         index = [];
-        for i=1:n_varobs
-            if max(abs(measurement_error)) > 0.000000001
+        for obs_iter=1:n_varobs
+            if max(abs(measurement_error(obs_iter,:))) > 0.0000001
                 number_of_plots_to_draw = number_of_plots_to_draw + 1;
-                index = cat(1,index,i);
+                index = cat(1,index,obs_iter);
             end
         end
         if ~options_.nograph
@@ -627,7 +628,7 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
                 fh = dyn_figure(options_.nodisplay,'Name','Smoothed observation errors');
                 NAMES = [];
                 if options_.TeX, TeXNAMES = []; end
-                nstar0=min(nstar,number_of_plots_to_draw-(nbplt-1)*nstar);
+                nstar0=min(nstar,number_of_plots_to_draw-(plt-1)*nstar);
                 if gend==1
                     marker_string{1,1}='-ro';
                     marker_string{2,1}='-ko';

matlab/dynare_estimation_init.m

@@ -50,6 +50,7 @@ function [dataset_, dataset_info, xparam1, hh, M_, options_, oo_, estim_params_,
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
 hh = [];
+xparam1 = [];
 
 if isempty(gsa_flag)
     gsa_flag = 0;
@@ -311,6 +312,10 @@ if options_.use_calibration_initialization %set calibration as starting values
     end
 end
 
+if ~isempty(bayestopt_) && all(bayestopt_.pshape==0) && any(isnan(xparam1))
+    error('ML estimation requires all estimated parameters to be initialized, either in an estimated_params or estimated_params_init-block ')
+end
+
 if ~isempty(estim_params_) && ~(all(strcmp(fieldnames(estim_params_),'full_calibration_detected'))  || (isfield(estim_params_,'nvx') && sum(estim_params_.nvx+estim_params_.nvn+estim_params_.ncx+estim_params_.ncn+estim_params_.np)==0))
     if ~isempty(bayestopt_) && any(bayestopt_.pshape > 0)
         % Plot prior densities.
@@ -556,7 +561,7 @@ end
 [oo_.steady_state, params,info] = evaluate_steady_state(oo_.steady_state,M,options_,oo_,steadystate_check_flag);
 
 if info(1)
-    fprintf('\ndynare_estimation_init:: The steady state at the initial parameters cannot be computed.')
+    fprintf('\ndynare_estimation_init:: The steady state at the initial parameters cannot be computed.\n')
     print_info(info, 0, options_);
 end
 

matlab/dynare_identification.m

@@ -271,15 +271,15 @@ if iload <=0
               case 'posterior_mode'
                 parameters_TeX = 'Posterior mode';
                 disp('Testing posterior mode')
-                params(1,:) = get_posterior_parameters('mode');
+                params(1,:) = get_posterior_parameters('mode',M_,estim_params_,oo_,options_);
               case 'posterior_mean'
                 parameters_TeX = 'Posterior mean';
                 disp('Testing posterior mean')
-                params(1,:) = get_posterior_parameters('mean');
+                params(1,:) = get_posterior_parameters('mean',M_,estim_params_,oo_,options_);
               case 'posterior_median'
                 parameters_TeX = 'Posterior median';
                 disp('Testing posterior median')
-                params(1,:) = get_posterior_parameters('median');
+                params(1,:) = get_posterior_parameters('median',M_,estim_params_,oo_,options_);
               case 'prior_mode'
                 parameters_TeX = 'Prior mode';
                 disp('Testing prior mode')

matlab/dynare_sensitivity.m

@@ -93,6 +93,9 @@ if ~isempty(options_gsa.datafile) || isempty(bayestopt_) || options_gsa.rmse
         disp('must be specified for RMSE analysis!');
         error('Sensitivity anaysis error!')
     end
+    if ~isempty(options_.nobs) && length(options_.nobs)~=1
+        error('dynare_sensitivity does not support recursive estimation. Please specify nobs as a scalar, not a vector.')
+    end
     options_.datafile = options_gsa.datafile;
     if isfield(options_gsa,'first_obs')
         options_.first_obs=options_gsa.first_obs;

matlab/ep/setup_integration_nodes.m

 function [nodes,weights,nnodes] = setup_integration_nodes(EpOptions,pfm)
 if EpOptions.stochastic.order
     % Compute weights and nodes for the stochastic version of the extended path.
-    switch EpOptions.IntegrationAlgorithm
+    switch EpOptions.stochastic.IntegrationAlgorithm
       case 'Tensor-Gaussian-Quadrature'
         % Get the nodes and weights from a univariate Gauss-Hermite quadrature.
         [nodes0,weights0] = gauss_hermite_weights_and_nodes(EpOptions.stochastic.quadrature.nodes);

matlab/evaluate_likelihood.m

-function [llik,parameters] = evaluate_likelihood(parameters)
+function [llik,parameters] = evaluate_likelihood(parameters,M_,estim_params_,oo_,options_,bayestopt_)
 % Evaluate the logged likelihood at parameters.
 %
 % INPUTS
 %    o parameters  a string ('posterior mode','posterior mean','posterior median','prior mode','prior mean') or a vector of values for
 %                  the (estimated) parameters of the model.
-%
+%    o M_          [structure]  Definition of the model
+%    o estim_params_ [structure] characterizing parameters to be estimated
+%    o oo_         [structure]  Storage of results
+%    o options_    [structure]  Options
+%    o bayestopt_  [structure]  describing the priors
 %
 % OUTPUTS
 %    o ldens       [double]  value of the sample logged density at parameters.
@@ -35,8 +39,6 @@ function [llik,parameters] = evaluate_likelihood(parameters)
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
-global options_ M_ bayestopt_ oo_ estim_params_
-
 persistent dataset dataset_info
 
 if nargin==0
@@ -46,11 +48,11 @@ end
 if ischar(parameters)
     switch parameters
       case 'posterior mode'
-        parameters = get_posterior_parameters('mode');
+        parameters = get_posterior_parameters('mode',M_,estim_params_,oo_,options_);
       case 'posterior mean'
-        parameters = get_posterior_parameters('mean');
+        parameters = get_posterior_parameters('mean',M_,estim_params_,oo_,options_);
       case 'posterior median'
-        parameters = get_posterior_parameters('median');
+        parameters = get_posterior_parameters('median',M_,estim_params_,oo_,options_);
       case 'prior mode'
         parameters = bayestopt_.p5(:);
       case 'prior mean'
@@ -72,5 +74,5 @@ end
 options_=select_qz_criterium_value(options_);
 
 llik = -dsge_likelihood(parameters,dataset,dataset_info,options_,M_,estim_params_,bayestopt_,prior_bounds(bayestopt_,options_.prior_trunc),oo_);
-ldens = evaluate_prior(parameters);
+ldens = evaluate_prior(parameters,M_,estim_params_,oo_,options_,bayestopt_);
 llik = llik - ldens;
\ No newline at end of file

matlab/evaluate_posterior_kernel.m

-function lpkern = evaluate_posterior_kernel(parameters,llik)
-% Evaluate the prior density at parameters.
+function lpkern = evaluate_posterior_kernel(parameters,M_,estim_params_,oo_,options_,bayestopt_,llik)
+% Evaluate the evaluate_posterior_kernel at parameters.
 %
 % INPUTS
 %    o parameters  a string ('posterior mode','posterior mean','posterior median','prior mode','prior mean') or a vector of values for
 %                  the (estimated) parameters of the model.
-%
+%    o M_         [structure]     Definition of the model
+%    o estim_params_ [structure]  characterizing parameters to be estimated
+%    o oo_         [structure]    Storage of results
+%    o options_    [structure]    Options
+%    o bayestopt_  [structure]    describing the priors
+%    o llik        [double]       value of the logged likelihood if it
+%                                   should not be computed
 %
 % OUTPUTS
 %    o lpkern      [double]  value of the logged posterior kernel.
@@ -34,8 +40,8 @@ function lpkern = evaluate_posterior_kernel(parameters,llik)
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
-[ldens,parameters] = evaluate_prior(parameters);
-if nargin==1
-    llik = evaluate_likelihood(parameters);
+[ldens,parameters] = evaluate_prior(parameters,M_,estim_params_,oo_,options_,bayestopt_);
+if nargin==6 %llik provided as an input
+    llik = evaluate_likelihood(parameters,M_,estim_params_,oo_,options_,bayestopt_);
 end
 lpkern = ldens+llik;
\ No newline at end of file

matlab/evaluate_prior.m

-function [ldens,parameters] = evaluate_prior(parameters)
+function [ldens,parameters] = evaluate_prior(parameters,M_,estim_params_,oo_,options_,bayestopt_)
 % Evaluate the prior density at parameters.
 %
 % INPUTS
 %    o parameters  a string ('posterior mode','posterior mean','posterior median','prior mode','prior mean') or a vector of values for
 %                  the (estimated) parameters of the model.
+%    o M_          [structure]  Definition of the model
+%    o oo_         [structure]  Storage of results
+%    o options_    [structure]  Options
+%    o bayestopt_  [structure]  describing the priors
+%    o estim_params_ [structure] characterizing parameters to be estimated
 %
 %
 % OUTPUTS
@@ -33,8 +38,6 @@ function [ldens,parameters] = evaluate_prior(parameters)
 % 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_
-
 if nargin==0
     parameters = 'posterior mode';
 end
@@ -42,11 +45,11 @@ end
 if ischar(parameters)
     switch parameters
       case 'posterior mode'
-        parameters = get_posterior_parameters('mode');
+        parameters = get_posterior_parameters('mode',M_,estim_params_,oo_,options_);
       case 'posterior mean'
-        parameters = get_posterior_parameters('mean');
+        parameters = get_posterior_parameters('mean',M_,estim_params_,oo_,options_);
       case 'posterior median'
-        parameters = get_posterior_parameters('median');
+        parameters = get_posterior_parameters('median',M_,estim_params_,oo_,options_);
       case 'prior mode'
         parameters = bayestopt_.p5(:);
       case 'prior mean'

matlab/evaluate_smoother.m

@@ -73,13 +73,13 @@ end
 if ischar(parameters)
     switch parameters
       case 'posterior_mode'
-        parameters = get_posterior_parameters('mode');
+        parameters = get_posterior_parameters('mode',M_,estim_params_,oo_,options_);
       case 'posterior_mean'
-        parameters = get_posterior_parameters('mean');
+        parameters = get_posterior_parameters('mean',M_,estim_params_,oo_,options_);
       case 'posterior_median'
-        parameters = get_posterior_parameters('median');
+        parameters = get_posterior_parameters('median',M_,estim_params_,oo_,options_);
       case 'mle_mode'
-        parameters = get_posterior_parameters('mode','mle_');
+        parameters = get_posterior_parameters('mode',M_,estim_params_,oo_,options_,'mle_');
       case 'prior_mode'
         parameters = bayestopt_.p5(:);
       case 'prior_mean'