diff --git a/matlab/PosteriorIRF_core1.m b/matlab/PosteriorIRF_core1.m
index 002d77ba8..cbfe2ee31 100644
--- a/matlab/PosteriorIRF_core1.m
+++ b/matlab/PosteriorIRF_core1.m
@@ -194,7 +194,7 @@ while fpar<B
     end
     if MAX_nirfs_dsgevar
         IRUN = IRUN+1;
-        [fval,junk1,junk2,cost_flag,SteadyState,junk3,info,PHI,SIGMAu,iXX] =  dsge_var_likelihood(deep',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
+        [fval,junk1,junk2,cost_flag,info,PHI,SIGMAu,iXX] =  dsge_var_likelihood(deep',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
         dsge_prior_weight = M_.params(strmatch('dsge_prior_weight',M_.param_names));
         DSGE_PRIOR_WEIGHT = floor(dataset_.nobs*(1+dsge_prior_weight));
         SIGMA_inv_upper_chol = chol(inv(SIGMAu*dataset_.nobs*(dsge_prior_weight+1)));
diff --git a/matlab/dsge_var_likelihood.m b/matlab/dsge_var_likelihood.m
index d0a5a8b4d..ddd30bbe4 100644
--- a/matlab/dsge_var_likelihood.m
+++ b/matlab/dsge_var_likelihood.m
@@ -1,4 +1,4 @@
-function [fval,grad,hess,exit_flag,SteadyState,trend_coeff,info,PHI,SIGMAu,iXX,prior] = dsge_var_likelihood(xparam1,DynareDataset,DynareInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults)
+function [fval,grad,hess,exit_flag,info,PHI,SIGMAu,iXX,prior] = dsge_var_likelihood(xparam1,DynareDataset,DynareInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults)
 % Evaluates the posterior kernel of the bvar-dsge model.
 %
 % INPUTS
@@ -8,10 +8,6 @@ function [fval,grad,hess,exit_flag,SteadyState,trend_coeff,info,PHI,SIGMAu,iXX,p
 % OUTPUTS
 %   o fval          [double]     Value of the posterior kernel at xparam1.
 %   o cost_flag     [integer]    Zero if the function returns a penalty, one otherwise.
-%   o SteadyState   [double]     Steady state vector possibly recomputed
-%                                by call to dynare_results()
-%   o trend_coeff   [double]     place holder for trend coefficients,
-%                                currently not supported by dsge_var
 %   o info          [integer]    Vector of informations about the penalty.
 %   o PHI           [double]     Stacked BVAR-DSGE autoregressive matrices (at the mode associated to xparam1).
 %   o SIGMAu        [double]     Covariance matrix of the BVAR-DSGE (at the mode associated to xparam1).
@@ -38,18 +34,17 @@ function [fval,grad,hess,exit_flag,SteadyState,trend_coeff,info,PHI,SIGMAu,iXX,p
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
+global objective_function_penalty_base
 persistent dsge_prior_weight_idx
 
 grad=[];
 hess=[];
 exit_flag = [];
-info = 0;
+info = [];
 PHI = [];
 SIGMAu = [];
 iXX = [];
 prior = [];
-SteadyState = [];
-trend_coeff = [];
 
 % Initialization of of the index for parameter dsge_prior_weight in Model.params.
 if isempty(dsge_prior_weight_idx)
@@ -87,21 +82,19 @@ exit_flag = 1;
 
 % Return, with endogenous penalty, if some dsge-parameters are smaller than the lower bound of the prior domain.
 if DynareOptions.mode_compute ~= 1 && any(xparam1 < BoundsInfo.lb)
-    fval = Inf;
-    exit_flag = 0;
-    info(1) = 41;
     k = find(xparam1 < BoundsInfo.lb);
-    info(2) = sum((BoundsInfo.lb(k)-xparam1(k)).^2);
+    fval = objective_function_penalty_base+sum((BoundsInfo.lb(k)-xparam1(k)).^2);
+    exit_flag = 0;
+    info = 41;
     return;
 end
 
 % Return, with endogenous penalty, if some dsge-parameters are greater than the upper bound of the prior domain.
 if DynareOptions.mode_compute ~= 1 && any(xparam1 > BoundsInfo.ub)
-    fval = Inf;
-    exit_flag = 0;
-    info(1) = 42;
     k = find(xparam1 > BoundsInfo.ub);
-    info(2) = sum((xparam1(k)-BoundsInfo.ub(k)).^2);
+    fval = objective_function_penalty_base+sum((xparam1(k)-BoundsInfo.ub(k)).^2);
+    exit_flag = 0;
+    info = 42;
     return;
 end
 
@@ -121,14 +114,12 @@ Model.Sigma_e = Q;
 dsge_prior_weight = Model.params(dsge_prior_weight_idx);
 
 % Is the dsge prior proper?
-if dsge_prior_weight<(NumberOfParameters+NumberOfObservedVariables)/ ...
-        NumberOfObservations;
-    fval = Inf;
+if dsge_prior_weight<(NumberOfParameters+NumberOfObservedVariables)/NumberOfObservations;
+    fval = objective_function_penalty_base+abs(NumberOfObservations*dsge_prior_weight-(NumberOfParameters+NumberOfObservedVariables));
     exit_flag = 0;
-    info(1) = 51;
-    info(2) = abs(NumberOfObservations*dsge_prior_weight-(NumberOfParameters+NumberOfObservedVariables));
-    %    info(2)=dsge_prior_weight;
-    %    info(3)=(NumberOfParameters+NumberOfObservedVariables)/DynareDataset.nobs;
+    info = 51;
+    info(2)=dsge_prior_weight;
+    info(3)=(NumberOfParameters+NumberOfObservedVariables)/DynareDataset.nobs;
     return
 end
 
@@ -143,13 +134,13 @@ end
 % Return, with endogenous penalty when possible, if dynare_resolve issues an error code (defined in resol).
 if info(1) == 1 || info(1) == 2 || info(1) == 5 || info(1) == 7 || info(1) == 8 || ...
             info(1) == 22 || info(1) == 24 || info(1) == 25 || info(1) == 10
-    fval = Inf;
-    info(2) = 0.1;
+    fval = objective_function_penalty_base+1;
+    info = info(1);
     exit_flag = 0;
     return
 elseif info(1) == 3 || info(1) == 4 || info(1) == 19 || info(1) == 20 || info(1) == 21
-    fval = Inf;
-    info(2) = 0.1;
+    fval = objective_function_penalty_base+info(2);
+    info = info(1);
     exit_flag = 0;
     return
 end
@@ -218,9 +209,8 @@ if ~isinf(dsge_prior_weight)% Evaluation of the likelihood of the dsge-var model
     SIGMAu = tmp0 - tmp1*tmp2*tmp1'; clear('tmp0');
     [SIGMAu_is_positive_definite, penalty] = ispd(SIGMAu);
     if ~SIGMAu_is_positive_definite
-        fval = Inf;
-        info(1) = 52;
-        info(2) = penalty;
+        fval = objective_function_penalty_base + penalty;
+        info = 52;
         exit_flag = 0;
         return;
     end
@@ -250,17 +240,15 @@ else% Evaluation of the likelihood of the dsge-var model when the dsge prior wei
 end
 
 if isnan(lik)
-    info(1) = 45;
-    info(2) = 0.1;
-    fval = Inf;
+    info = 45;
+    fval = objective_function_penalty_base + 100;
     exit_flag = 0;
     return
 end
 
 if imag(lik)~=0
-    info(1) = 46;
-    info(2) = 0.1;
-    fval = Inf;
+    info = 46;
+    fval = objective_function_penalty_base + 100;
     exit_flag = 0;
     return
 end
@@ -270,22 +258,20 @@ lnprior = priordens(xparam1,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo
 fval = (lik-lnprior);
 
 if isnan(fval)
-    info(1) = 47;
-    info(2) = 0.1;
-    fval = Inf;
+    info = 47;
+    fval = objective_function_penalty_base + 100;
     exit_flag = 0;
     return
 end
 
 if imag(fval)~=0
-    info(1) = 48;
-    info(2) = 0.1;
-    fval = Inf;
+    info = 48;
+    fval = objective_function_penalty_base + 100;
     exit_flag = 0;
     return
 end
 
-if (nargout == 10)
+if (nargout == 8)
     if isinf(dsge_prior_weight)
         iXX = iGXX;
     else
@@ -293,7 +279,7 @@ if (nargout == 10)
     end
 end
 
-if (nargout==11)
+if (nargout==9)
     if isinf(dsge_prior_weight)
         iXX = iGXX;
     else
@@ -305,8 +291,4 @@ if (nargout==11)
     prior.ArtificialSampleSize = fix(dsge_prior_weight*NumberOfObservations);
     prior.DF = prior.ArtificialSampleSize - NumberOfParameters - NumberOfObservedVariables;
     prior.iGXX = iGXX;
-end
-
-if fval == Inf
-    pause
 end
\ No newline at end of file
diff --git a/matlab/dsgevar_posterior_density.m b/matlab/dsgevar_posterior_density.m
index 422aa2c62..85eb67532 100644
--- a/matlab/dsgevar_posterior_density.m
+++ b/matlab/dsgevar_posterior_density.m
@@ -47,7 +47,7 @@ if ~options_.noconstant
         bvar.NumberOfVariables;
 end
 
-[fval,grad,hess,cost_flag,SteadyState,trend_coeff,info,Steady,PHI,SIGMAu,iXX,prior] =  dsge_var_likelihood(deep',DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+[fval,cost_flag,info,PHI,SIGMAu,iXX,prior] =  dsge_var_likelihood(deep',DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
 
 % Conditionnal posterior density of the lagged matrices (given Sigma) ->
 % Matric-variate normal distribution.
diff --git a/matlab/optimization/numgrad3.m b/matlab/optimization/numgrad3.m
index 7ddba68d8..79336bf21 100644
--- a/matlab/optimization/numgrad3.m
+++ b/matlab/optimization/numgrad3.m
@@ -38,9 +38,9 @@ badg=0;
 for i=1:n
     xiold = x(i);
     x(i) = xiold+h;
-    f1 = penalty_objective_function(x, fcn, penalty, varargin{:});
+    f1 = penalty_objective_function(x, fcn, penatly, varargin{:});
     x(i) = xiold-h;
-    f2 = penalty_objective_function(x, fcn, penalty, varargin{:});
+    f2 = penalty_objective_function(x, fcn, penatly, varargin{:});
     g0 = (f1-f2)/H;
     if abs(g0)< 1e15
         g(i)=g0;
diff --git a/matlab/optimization/penalty_objective_function.m b/matlab/optimization/penalty_objective_function.m
index ef4d0183e..0129dbeb1 100644
--- a/matlab/optimization/penalty_objective_function.m
+++ b/matlab/optimization/penalty_objective_function.m
@@ -1,8 +1,5 @@
 function [fval,DLIK,Hess,exit_flag] = objective_function_penalty(x0,fcn,penalty,varargin)
     [fval,DLIK,Hess,exit_flag,SteadyState,trend_coeff,info] = fcn(x0,varargin{:});
-
-    
-    
     if info(1) ~= 0
         fval = penalty + info(2);
     end