diff --git a/matlab/DsgeLikelihood.m b/matlab/DsgeLikelihood.m
index 8bc790734..c4ba26e0c 100644
--- a/matlab/DsgeLikelihood.m
+++ b/matlab/DsgeLikelihood.m
@@ -255,7 +255,7 @@ Model.H = H;
 %------------------------------------------------------------------------------
 
 % Linearize the model around the deterministic sdteadystate and extract the matrices of the state equation (T and R).
-[T,R,SteadyState,info,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults);
+[T,R,SteadyState,info,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults,'restrict');
 
 % 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) == 22 || info(1) == 24
diff --git a/matlab/DsgeVarLikelihood.m b/matlab/DsgeVarLikelihood.m
index 133f8757d..98c886439 100644
--- a/matlab/DsgeVarLikelihood.m
+++ b/matlab/DsgeVarLikelihood.m
@@ -1,18 +1,18 @@
-function [fval,cost_flag,info,PHI,SIGMAu,iXX,prior] = DsgeVarLikelihood(xparam1,gend)
-% Evaluates the posterior kernel of the bvar-dsge model. 
-% 
-% INPUTS 
+function [fval,exit_flag,info,PHI,SIGMAu,iXX,prior] = DsgeVarLikelihood(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
+% Evaluates the posterior kernel of the bvar-dsge model.
+%
+% INPUTS
 %   o xparam1       [double]     Vector of model's parameters.
 %   o gend          [integer]    Number of observations (without conditionning observations for the lags).
-%  
-% OUTPUTS 
+%
+% 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 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).
 %   o iXX           [double]     inv(X'X).
-%   o prior         [double]     a matlab structure describing the dsge-var prior.  
+%   o prior         [double]     a matlab structure describing the dsge-var prior.
 %
 % SPECIAL REQUIREMENTS
 %   None.
@@ -34,139 +34,180 @@ function [fval,cost_flag,info,PHI,SIGMAu,iXX,prior] = DsgeVarLikelihood(xparam1,
 % 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_ estim_params_ M_ options_ oo_
+% Declaration of the persistent variables.
+persistent penalty dsge_prior_weight_idx
 
-nvx = estim_params_.nvx;
-nvn = estim_params_.nvn;
-ncx = estim_params_.ncx;
-ncn = estim_params_.ncn;
-np  = estim_params_.np;
-nx = nvx+nvn+ncx+ncn+np;
-ns = nvx+nvn+ncx+ncn;
+% Initialization of the penalty
+if ~nargin || isempty(penalty)
+    penalty = 1e8;
+    if ~nargin, return, end
+end
+if nargin==1
+    penalty = xparam1;
+    return
+end
 
-NumberOfObservedVariables = size(options_.varobs,1);
-NumberOfLags = options_.dsge_varlag;
+% Initialization of of the index for parameter dsge_prior_weight in Model.params.
+if isempty(dsge_prior_weight_idx)
+    dsge_prior_weight_idx = strmatch('dsge_prior_weight',Model.param_names);
+end
+
+% Get the number of estimated (dsge) parameters.
+ns = EstimatedParameters.nvx + ...
+     EstimatedParameters.nvn + ...
+     EstimatedParameters.ncx + ...
+     EstimatedParameters.ncn;
+nx = ns + EstimatedParameters.np;
+
+% Get the number of observed variables in the VAR model.
+NumberOfObservedVariables = DynareDataset.info.nvobs;
+
+% Get the number of lags in the VAR model.
+NumberOfLags = DynareOptions.dsge_varlag;
+
+% Get the number of parameters in the VAR model.
 NumberOfParameters = NumberOfObservedVariables*NumberOfLags ;
-if ~options_.noconstant
+if ~DynareOptions.noconstant
     NumberOfParameters = NumberOfParameters + 1;
 end
 
+% Get empirical second order moments for the observed variables.
 mYY = evalin('base', 'mYY');
 mYX = evalin('base', 'mYX');
 mXY = evalin('base', 'mXY');
 mXX = evalin('base', 'mXX');
 
+% Initialize some of the output arguments.
 fval = [];
-cost_flag = 1;
+exit_flag = 1;
 
-if ~isequal(options_.mode_compute,1) && any(xparam1 < bayestopt_.lb)
-    k = find(xparam1 < bayestopt_.lb);
-    fval = bayestopt_.penalty+sum((bayestopt_.lb(k)-xparam1(k)).^2);
-    cost_flag = 0;
+% 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 < BayesInfo.lb)
+    k = find(xparam1 < BayesInfo.lb);
+    fval = penalty+sum((BayesInfo.lb(k)-xparam1(k)).^2);
+    exit_flag = 0;
     info = 41;
     return;
 end
 
-if ~isequal(options_.mode_compute,11) && any(xparam1 > bayestopt_.ub)
-    k = find(xparam1 > bayestopt_.ub);
-    fval = bayestopt_.penalty+sum((xparam1(k)-bayestopt_.ub(k)).^2);
-    cost_flag = 0;
+% 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 > BayesInfo.ub)
+    k = find(xparam1 > BayesInfo.ub);
+    fval = penalty+sum((xparam1(k)-BayesInfo.ub(k)).^2);
+    exit_flag = 0;
     info = 42;
     return;
 end
 
-Q = M_.Sigma_e;
-for i=1:estim_params_.nvx
-    k = estim_params_.var_exo(i,1);
+% Get the variance of each structural innovation.
+Q = Model.Sigma_e;
+for i=1:EstimatedParameters.nvx
+    k = EstimatedParameters.var_exo(i,1);
     Q(k,k) = xparam1(i)*xparam1(i);
 end
-offset = estim_params_.nvx;
-if estim_params_.nvn
+offset = EstimatedParameters.nvx;
+
+% Check that the user does not estimate measurment errors.
+% TODO Check that the user does not declare non estimated measurement errors...
+if EstimatedParameters.nvn
     disp('DsgeVarLikelihood :: Measurement errors are not implemented!')
     return
-end 
-if estim_params_.ncx
+end
+
+% Check that the user does not estimate off diagonal elements in the covariance matrix of the structural innovation.
+% TODO Check that Q is a diagonal matrix...
+if EstimatedParameters.ncx
     disp('DsgeVarLikelihood :: Correlated structural innovations are not implemented!')
     return
 end
 
-M_.params(estim_params_.param_vals(:,1)) = xparam1(offset+1:end);
-M_.Sigma_e = Q;
+% Update Model.params and Model.Sigma_e.
+Model.params(EstimatedParameters.param_vals(:,1)) = xparam1(offset+1:end);
+Model.Sigma_e = Q;
 
-%% Weight of the dsge prior:
-dsge_prior_weight = M_.params(strmatch('dsge_prior_weight',M_.param_names));
-% Is the DSGE prior proper?
-if dsge_prior_weight<(NumberOfParameters+NumberOfObservedVariables)/gend;
-    fval = bayestopt_.penalty+abs(gend*dsge_prior_weight-(NumberOfParameters+NumberOfObservedVariables));
-    cost_flag = 0;
+% Get the weight of the dsge prior.
+dsge_prior_weight = Model.params(dsge_prior_weight_idx);
+
+% Is the dsge prior proper?
+if dsge_prior_weight<(NumberOfParameters+NumberOfObservedVariables)/DynareDataset.info.ntobs;
+    fval = penalty+abs(DynareDataset.info.ntobs*dsge_prior_weight-(NumberOfParameters+NumberOfObservedVariables));
+    exit_flag = 0;
     info = 51;
-    return;
+    return
 end
 
 %------------------------------------------------------------------------------
 % 2. call model setup & reduction program
 %------------------------------------------------------------------------------
-[T,R,SteadyState,info,M_,options_,oo_] = dynare_resolve(M_,options_,oo_);
 
+% Solve the Dsge model and get the matrices of the reduced form solution. T and R are the matrices of the
+% state equation
+[T,R,SteadyState,info,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults,'restrict');
+
+% 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
-    fval = bayestopt_.penalty+1;
-    cost_flag = 0;
+    fval = penalty+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 = bayestopt_.penalty+info(2);
-    cost_flag = 0;
+    fval = penalty+info(2);
+    info = info(1);
+    exit_flag = 0;
     return
 end
 
-if ~options_.noconstant
-    if options_.loglinear 
-        constant = transpose(log(SteadyState(bayestopt_.mfys)));
+% Define the mean/steady state vector.
+if ~DynareOptions.noconstant
+    if DynareOptions.loglinear
+        constant = transpose(log(SteadyState(BayesInfo.mfys)));
     else
-        constant = transpose(SteadyState(bayestopt_.mfys));
-    end 
+        constant = transpose(SteadyState(BayesInfo.mfys));
+    end
 else
     constant = zeros(1,NumberOfObservedVariables);
 end
-if bayestopt_.with_trend == 1
-    disp('DsgeVarLikelihood :: Linear trend is not yet implemented!')
-    return
+
+% Dsge-VAR with deterministic trends is not implemented
+if BayesInfo.with_trend == 1
+    error('DsgeVarLikelihood :: Linear trend is not yet implemented!')
 end
 
 %------------------------------------------------------------------------------
 % 3. theoretical moments (second order)
 %------------------------------------------------------------------------------
-tmp0 = lyapunov_symm(T,R*Q*R',options_.qz_criterium,options_.lyapunov_complex_threshold);% I compute the variance-covariance matrix
-mf  = bayestopt_.mf1;          % of the restricted state vector.
+
+% Compute the theoretical second order moments
+tmp0 = lyapunov_symm(T,R*Q*R',DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold);
+mf  = BayesInfo.mf1;
 
 % Get the non centered second order moments
-TheoreticalAutoCovarianceOfTheObservedVariables = ...
-    zeros(NumberOfObservedVariables,NumberOfObservedVariables,NumberOfLags+1);
+TheoreticalAutoCovarianceOfTheObservedVariables = zeros(NumberOfObservedVariables,NumberOfObservedVariables,NumberOfLags+1);
 TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1) = tmp0(mf,mf)+constant'*constant;
 for lag = 1:NumberOfLags
     tmp0 = T*tmp0;
-    TheoreticalAutoCovarianceOfTheObservedVariables(:,:,lag+1) = tmp0(mf,mf) ...
-        + constant'*constant;
+    TheoreticalAutoCovarianceOfTheObservedVariables(:,:,lag+1) = tmp0(mf,mf) + constant'*constant;
 end
+
 % Build the theoretical "covariance" between Y and X
 GYX = zeros(NumberOfObservedVariables,NumberOfParameters);
 for i=1:NumberOfLags
-    GYX(:,(i-1)*NumberOfObservedVariables+1:i*NumberOfObservedVariables) = ...
-        TheoreticalAutoCovarianceOfTheObservedVariables(:,:,i+1);
+    GYX(:,(i-1)*NumberOfObservedVariables+1:i*NumberOfObservedVariables) = TheoreticalAutoCovarianceOfTheObservedVariables(:,:,i+1);
 end
-if ~options_.noconstant
+if ~DynareOptions.noconstant
     GYX(:,end) = constant';
 end
+
 % Build the theoretical "covariance" between X and X
-GXX = kron(eye(NumberOfLags), ...
-           TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1));
+GXX = kron(eye(NumberOfLags), TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1));
 for i = 1:NumberOfLags-1
-    tmp1 = diag(ones(NumberOfLags-i,1),i); 
+    tmp1 = diag(ones(NumberOfLags-i,1),i);
     tmp2 = diag(ones(NumberOfLags-i,1),-i);
     GXX = GXX + kron(tmp1,TheoreticalAutoCovarianceOfTheObservedVariables(:,:,i+1));
     GXX = GXX + kron(tmp2,TheoreticalAutoCovarianceOfTheObservedVariables(:,:,i+1)');
 end
 
-if ~options_.noconstant
+if ~DynareOptions.noconstant
     % Add one row and one column to GXX
     GXX = [GXX , kron(ones(NumberOfLags,1),constant') ; [  kron(ones(1,NumberOfLags),constant) , 1] ];
 end
@@ -177,45 +218,46 @@ assignin('base','GYY',GYY);
 assignin('base','GXX',GXX);
 assignin('base','GYX',GYX);
 
-if ~isinf(dsge_prior_weight)
-    tmp0 = dsge_prior_weight*gend*TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1) + mYY ;
-    tmp1 = dsge_prior_weight*gend*GYX + mYX;
-    tmp2 = inv(dsge_prior_weight*gend*GXX+mXX);
+if ~isinf(dsge_prior_weight)% Evaluation of the likelihood of the dsge-var model when the dsge prior weight is finite.
+    tmp0 = dsge_prior_weight*DynareDataset.info.ntobs*TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1) + mYY ;
+    tmp1 = dsge_prior_weight*DynareDataset.info.ntobs*GYX + mYX;
+    tmp2 = inv(dsge_prior_weight*DynareDataset.info.ntobs*GXX+mXX);
     SIGMAu = tmp0 - tmp1*tmp2*tmp1'; clear('tmp0');
     if ~ispd(SIGMAu)
         v = diag(SIGMAu);
         k = find(v<0);
-        fval = bayestopt_.penalty + sum(v(k).^2);
+        fval = penalty + sum(v(k).^2);
         info = 52;
-        cost_flag = 0;
+        exit_flag = 0;
         return;
     end
-    SIGMAu = SIGMAu / (gend*(1+dsge_prior_weight));
+    SIGMAu = SIGMAu / (DynareDataset.info.ntobs*(1+dsge_prior_weight));
     PHI = tmp2*tmp1'; clear('tmp1');
-    prodlng1 = sum(gammaln(.5*((1+dsge_prior_weight)*gend- ...
+    prodlng1 = sum(gammaln(.5*((1+dsge_prior_weight)*DynareDataset.info.ntobs- ...
                                NumberOfObservedVariables*NumberOfLags ...
                                +1-(1:NumberOfObservedVariables)')));
-    prodlng2 = sum(gammaln(.5*(dsge_prior_weight*gend- ...
+    prodlng2 = sum(gammaln(.5*(dsge_prior_weight*DynareDataset.info.ntobs- ...
                                NumberOfObservedVariables*NumberOfLags ...
-                               +1-(1:NumberOfObservedVariables)')));  
-    lik = .5*NumberOfObservedVariables*log(det(dsge_prior_weight*gend*GXX+mXX)) ...
-          + .5*((dsge_prior_weight+1)*gend-NumberOfParameters)*log(det((dsge_prior_weight+1)*gend*SIGMAu)) ...
-          - .5*NumberOfObservedVariables*log(det(dsge_prior_weight*gend*GXX)) ...
-          - .5*(dsge_prior_weight*gend-NumberOfParameters)*log(det(dsge_prior_weight*gend*(GYY-GYX*inv(GXX)*GYX'))) ...
-          + .5*NumberOfObservedVariables*gend*log(2*pi)  ...
-          - .5*log(2)*NumberOfObservedVariables*((dsge_prior_weight+1)*gend-NumberOfParameters) ...
-          + .5*log(2)*NumberOfObservedVariables*(dsge_prior_weight*gend-NumberOfParameters) ...
+                               +1-(1:NumberOfObservedVariables)')));
+    lik = .5*NumberOfObservedVariables*log(det(dsge_prior_weight*DynareDataset.info.ntobs*GXX+mXX)) ...
+          + .5*((dsge_prior_weight+1)*DynareDataset.info.ntobs-NumberOfParameters)*log(det((dsge_prior_weight+1)*DynareDataset.info.ntobs*SIGMAu)) ...
+          - .5*NumberOfObservedVariables*log(det(dsge_prior_weight*DynareDataset.info.ntobs*GXX)) ...
+          - .5*(dsge_prior_weight*DynareDataset.info.ntobs-NumberOfParameters)*log(det(dsge_prior_weight*DynareDataset.info.ntobs*(GYY-GYX*inv(GXX)*GYX'))) ...
+          + .5*NumberOfObservedVariables*DynareDataset.info.ntobs*log(2*pi)  ...
+          - .5*log(2)*NumberOfObservedVariables*((dsge_prior_weight+1)*DynareDataset.info.ntobs-NumberOfParameters) ...
+          + .5*log(2)*NumberOfObservedVariables*(dsge_prior_weight*DynareDataset.info.ntobs-NumberOfParameters) ...
           - prodlng1 + prodlng2;
-else
+else% Evaluation of the likelihood of the dsge-var model when the dsge prior weight is infinite.
     iGXX = inv(GXX);
     SIGMAu = GYY - GYX*iGXX*transpose(GYX);
     PHI = iGXX*transpose(GYX);
-    lik = gend * ( log(det(SIGMAu)) + NumberOfObservedVariables*log(2*pi) +  ...
-                   trace(inv(SIGMAu)*(mYY - transpose(mYX*PHI) - mYX*PHI + transpose(PHI)*mXX*PHI)/gend));
+    lik = DynareDataset.info.ntobs * ( log(det(SIGMAu)) + NumberOfObservedVariables*log(2*pi) +  ...
+                   trace(inv(SIGMAu)*(mYY - transpose(mYX*PHI) - mYX*PHI + transpose(PHI)*mXX*PHI)/DynareDataset.info.ntobs));
     lik = .5*lik;% Minus likelihood
-end      
+end
 
-lnprior = priordens(xparam1,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
+% Add the (logged) prior density for the dsge-parameters.
+lnprior = priordens(xparam1,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
 fval = (lik-lnprior);
 
 if (nargout == 6)
@@ -232,10 +274,10 @@ if (nargout==7)
     else
         iXX = tmp2;
     end
-    iGXX = inv(GXX); 
+    iGXX = inv(GXX);
     prior.SIGMAstar = GYY - GYX*iGXX*GYX';
     prior.PHIstar = iGXX*transpose(GYX);
-    prior.ArtificialSampleSize = fix(dsge_prior_weight*gend);
+    prior.ArtificialSampleSize = fix(dsge_prior_weight*DynareDataset.info.ntobs);
     prior.DF = prior.ArtificialSampleSize - NumberOfParameters - NumberOfObservedVariables;
     prior.iGXX = iGXX;
 end
\ No newline at end of file
diff --git a/matlab/PosteriorIRF.m b/matlab/PosteriorIRF.m
index 9796f112a..fdbf10141 100644
--- a/matlab/PosteriorIRF.m
+++ b/matlab/PosteriorIRF.m
@@ -34,7 +34,7 @@ function PosteriorIRF(type)
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
 
-global options_ estim_params_ oo_ M_ bayestopt_
+global options_ estim_params_ oo_ M_ bayestopt_ dataset_
 % Set the number of periods
 if isempty(options_.irf) || ~options_.irf 
     options_.irf = 40;
@@ -64,8 +64,8 @@ np   = estim_params_.np ;
 npar = nvx+nvn+ncx+ncn+np;
 offset = npar-np; clear('nvx','nvn','ncx','ncn','np');
 
-nvobs = size(options_.varobs,1);
-gend = options_.nobs;
+nvobs = dataset_.info.nvobs;
+gend = dataset_.info.ntobs;
 MaxNumberOfPlotPerFigure = 9;
 nn = sqrt(MaxNumberOfPlotPerFigure);
 MAX_nirfs_dsge = ceil(options_.MaxNumberOfBytes/(options_.irf*nvar*M_.exo_nbr)/8);
@@ -230,7 +230,8 @@ else
                         'options_', options_, ...
                         'bayestopt_', bayestopt_, ...
                         'estim_params_', estim_params_, ...
-                        'oo_', oo_);
+                        'oo_', oo_, ...
+                        'dataset_',dataset_);
     
     % which files have to be copied to run remotely
     NamFileInput(1,:) = {'',[M_.fname '_static.m']};
diff --git a/matlab/PosteriorIRF_core1.m b/matlab/PosteriorIRF_core1.m
index 118229257..cbcebbb71 100644
--- a/matlab/PosteriorIRF_core1.m
+++ b/matlab/PosteriorIRF_core1.m
@@ -40,7 +40,7 @@ function myoutput=PosteriorIRF_core1(myinputs,fpar,npar,whoiam, ThisMatlab)
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
 
-global options_ estim_params_ oo_ M_ bayestopt_
+global options_ estim_params_ oo_ M_ bayestopt_ dataset_
 
 if nargin<4,
     whoiam=0;
@@ -151,6 +151,7 @@ while fpar<npar
     stock_param(irun2,:) = deep;
     set_parameters(deep);
     [dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
+    oo_.dr = dr;
     if info(1)
         nosaddle = nosaddle + 1;
         fpar = fpar - 1;
@@ -188,24 +189,24 @@ while fpar<npar
     end
     if MAX_nirfs_dsgevar
         IRUN = IRUN+1;
-        [fval,cost_flag,info,PHI,SIGMAu,iXX] =  DsgeVarLikelihood(deep',gend);
+        [fval,cost_flag,info,PHI,SIGMAu,iXX] =  DsgeVarLikelihood(deep',dataset_,options_,M_,estim_params_,bayestopt_,oo_);
         dsge_prior_weight = M_.params(strmatch('dsge_prior_weight',M_.param_names));
-        DSGE_PRIOR_WEIGHT = floor(gend*(1+dsge_prior_weight));
-        SIGMA_inv_upper_chol = chol(inv(SIGMAu*gend*(dsge_prior_weight+1)));
+        DSGE_PRIOR_WEIGHT = floor(dataset_.info.ntobs*(1+dsge_prior_weight));
+        SIGMA_inv_upper_chol = chol(inv(SIGMAu*dataset_.info.ntobs*(dsge_prior_weight+1)));
         explosive_var  = 1;
         while explosive_var
             % draw from the marginal posterior of SIGMA
-            SIGMAu_draw = rand_inverse_wishart(nvobs, DSGE_PRIOR_WEIGHT-NumberOfParametersPerEquation, ...
+            SIGMAu_draw = rand_inverse_wishart(dataset_.info.nvobs, DSGE_PRIOR_WEIGHT-NumberOfParametersPerEquation, ...
                                                SIGMA_inv_upper_chol);
             % draw from the conditional posterior of PHI
-            PHI_draw = rand_matrix_normal(NumberOfParametersPerEquation,nvobs, PHI, ...
+            PHI_draw = rand_matrix_normal(NumberOfParametersPerEquation,dataset_.info.nvobs, PHI, ...
                                           chol(SIGMAu_draw)', chol(iXX)');
-            Companion_matrix(1:nvobs,:) = transpose(PHI_draw(1:NumberOfLagsTimesNvobs,:));
+            Companion_matrix(1:dataset_.info.nvobs,:) = transpose(PHI_draw(1:NumberOfLagsTimesNvobs,:));
             % Check for stationarity
             explosive_var = any(abs(eig(Companion_matrix))>1.000000001);
         end
         % Get the mean
-        mu = zeros(1,nvobs);
+        mu = zeros(1,dataset_.info.nvobs);
         % Get rotation
         if dsge_prior_weight > 0
             Atheta(oo_.dr.order_var,M_.exo_names_orig_ord) = oo_.dr.ghu*sqrt(M_.Sigma_e);
@@ -215,24 +216,24 @@ while fpar<npar
         SIGMAu_chol = chol(SIGMAu_draw)';
         SIGMAtrOMEGA = SIGMAu_chol*OMEGAstar';
         PHIpower = eye(NumberOfLagsTimesNvobs);
-        irfs = zeros (options_.irf,nvobs*M_.exo_nbr);
-        tmp3 = PHIpower(1:nvobs,1:nvobs)*SIGMAtrOMEGA;
+        irfs = zeros (options_.irf,dataset_.info.nvobs*M_.exo_nbr);
+        tmp3 = PHIpower(1:dataset_.info.nvobs,1:dataset_.info.nvobs)*SIGMAtrOMEGA;
         irfs(1,:) = tmp3(:)';
         for t = 2:options_.irf
             PHIpower = Companion_matrix*PHIpower;
-            tmp3 = PHIpower(1:nvobs,1:nvobs)*SIGMAtrOMEGA;
+            tmp3 = PHIpower(1:dataset_.info.nvobs,1:dataset_.info.nvobs)*SIGMAtrOMEGA;
             irfs(t,:)  = tmp3(:)'+kron(ones(1,M_.exo_nbr),mu);
         end
         tmp_dsgevar = kron(ones(options_.irf,1),mu);
-        for j = 1:(nvobs*M_.exo_nbr)
+        for j = 1:(dataset_.info.nvobs*M_.exo_nbr)
             if max(irfs(:,j)) - min(irfs(:,j)) > 1e-10
                 tmp_dsgevar(:,j) = (irfs(:,j));
             end
         end
         if IRUN < MAX_nirfs_dsgevar
-            stock_irf_bvardsge(:,:,:,IRUN) = reshape(tmp_dsgevar,options_.irf,nvobs,M_.exo_nbr);
+            stock_irf_bvardsge(:,:,:,IRUN) = reshape(tmp_dsgevar,options_.irf,dataset_.info.nvobs,M_.exo_nbr);
         else
-            stock_irf_bvardsge(:,:,:,IRUN) = reshape(tmp_dsgevar,options_.irf,nvobs,M_.exo_nbr);
+            stock_irf_bvardsge(:,:,:,IRUN) = reshape(tmp_dsgevar,options_.irf,dataset_.info.nvobs,M_.exo_nbr);
             instr = [MhDirectoryName '/' M_.fname '_irf_bvardsge' ...
                      int2str(NumberOfIRFfiles_dsgevar) '.mat stock_irf_bvardsge;'];,
             eval(['save ' instr]);
@@ -241,7 +242,7 @@ while fpar<npar
             end
             NumberOfIRFfiles_dsgevar = NumberOfIRFfiles_dsgevar+1;
             IRUN =0;
-            stock_irf_dsgevar = zeros(options_.irf,nvobs,M_.exo_nbr,MAX_nirfs_dsgevar);
+            stock_irf_dsgevar = zeros(options_.irf,dataset_.info.nvobs,M_.exo_nbr,MAX_nirfs_dsgevar);
         end
     end
     if irun == MAX_nirfs_dsge || irun == npar || fpar == npar
diff --git a/matlab/dsgevar_posterior_density.m b/matlab/dsgevar_posterior_density.m
index 63667bbbd..bf90834e7 100644
--- a/matlab/dsgevar_posterior_density.m
+++ b/matlab/dsgevar_posterior_density.m
@@ -1,20 +1,20 @@
-function bvar = dsgevar_posterior_density(deep)
-% This function characterizes the posterior distribution of a bvar with 
-% a dsge prior (as in Del Negro and Schorfheide 2003) for a given value 
-% of the deep parameters (structural parameters + the size of the 
+function bvar = dsgevar_posterior_density(deep,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
+% This function characterizes the posterior distribution of a bvar with
+% a dsge prior (as in Del Negro and Schorfheide 2003) for a given value
+% of the deep parameters (structural parameters + the size of the
 % shocks + dsge_prior_weight).
-%    
+%
 % INPUTS
 %   deep:      [double] a vector with the deep parameters.
-%  
+%
 % OUTPUTS
-%   bvar:      a matlab structure with prior and posterior densities. 
-%  
+%   bvar:      a matlab structure with prior and posterior densities.
+%
 % ALGORITHM
 %   ...
 % SPECIAL REQUIREMENTS
 %   none
-%  
+%
 
 % Copyright (C) 1996-2008 Dynare Team
 %
@@ -33,8 +33,6 @@ function bvar = dsgevar_posterior_density(deep)
 % 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_
-
 gend = options_.nobs;
 dsge_prior_weight = M_.params(strmatch('dsge_prior_weight',M_.param_names));
 DSGE_PRIOR_WEIGHT = floor(gend*(1+dsge_prior_weight));
@@ -42,14 +40,14 @@ DSGE_PRIOR_WEIGHT = floor(gend*(1+dsge_prior_weight));
 bvar.NumberOfLags = options_.varlag;
 bvar.NumberOfVariables = size(options_.varobs,1);
 bvar.Constant = 'no';
-bvar.NumberOfEstimatedParameters = bvar.NumberOfLags*bvar.NumberOfVariables;   
+bvar.NumberOfEstimatedParameters = bvar.NumberOfLags*bvar.NumberOfVariables;
 if ~options_.noconstant
     bvar.Constant = 'yes';
     bvar.NumberOfEstimatedParameters = bvar.NumberOfEstimatedParameters + ...
-        bvar.NumberOfVariables;        
+        bvar.NumberOfVariables;
 end
 
-[fval,cost_flag,info,PHI,SIGMAu,iXX,prior] =  DsgeVarLikelihood(deep',gend);
+[fval,cost_flag,info,PHI,SIGMAu,iXX,prior] =  DsgeVarLikelihood(deep',DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
 
 % Conditionnal posterior density of the lagged matrices (given Sigma) ->
 % Matric-variate normal distribution.
@@ -58,12 +56,12 @@ bvar.LaggedMatricesConditionalOnSigma.posterior.arg1 = PHI;
 bvar.LaggedMatricesConditionalOnSigma.posterior.arg2 = 'Sigma';
 bvar.LaggedMatricesConditionalOnSigma.posterior.arg3 = iXX;
 
-% Marginal posterior density of the covariance matrix -> Inverted Wishart. 
+% Marginal posterior density of the covariance matrix -> Inverted Wishart.
 bvar.Sigma.posterior.density = 'inverse wishart';
 bvar.Sigma.posterior.arg1 = SIGMAu*DSGE_PRIOR_WEIGHT;
 bvar.Sigma.posterior.arg2 = DSGE_PRIOR_WEIGHT-bvar.NumberOfEstimatedParameters;
 
-% Marginal posterior density of the lagged matrices -> Generalized 
+% Marginal posterior density of the lagged matrices -> Generalized
 % Student distribution (See appendix B.5 in Zellner (1971)).
 bvar.LaggedMatrices.posterior.density = 'matric-variate student';
 bvar.LaggedMatrices.posterior.arg1 = inv(iXX);%P
@@ -80,12 +78,12 @@ bvar.LaggedMatricesConditionalOnSigma.prior.arg1 = prior.PHIstar;
 bvar.LaggedMatricesConditionalOnSigma.prior.arg2 = 'Sigma';
 bvar.LaggedMatricesConditionalOnSigma.prior.arg3 = prior.iGXX;
 
-% Marginal posterior density of the covariance matrix -> Inverted Wishart. 
+% Marginal posterior density of the covariance matrix -> Inverted Wishart.
 bvar.Sigma.prior.density = 'inverse wishart';
 bvar.Sigma.prior.arg1 = prior.SIGMAstar*prior.ArtificialSampleSize;
 bvar.Sigma.prior.arg2 = prior.DF;
 
-% Marginal posterior density of the lagged matrices -> Generalized 
+% Marginal posterior density of the lagged matrices -> Generalized
 % Student distribution (See appendix B.5 in Zellner (1971)).
 bvar.LaggedMatrices.prior.density = 'matric-variate student';
 bvar.LaggedMatrices.prior.arg1 = inv(prior.iGXX);%P
diff --git a/matlab/dynare_estimation_1.m b/matlab/dynare_estimation_1.m
index 1e3711dde..464264918 100644
--- a/matlab/dynare_estimation_1.m
+++ b/matlab/dynare_estimation_1.m
@@ -29,7 +29,7 @@ function dynare_estimation_1(var_list_,dname)
 % 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_ options_ oo_ estim_params_ bayestopt_
+global M_ options_ oo_ estim_params_ bayestopt_ dataset_
 
 if ~options_.dsge_var
     objective_function = str2func('DsgeLikelihood');
@@ -208,11 +208,6 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
         else
             nit=1000;
         end
-        if ~options_.dsge_var
-            [xparam1,hh,gg,fval,invhess] = newrat('DsgeLikelihood',xparam1,hh,gg,igg,crit,nit,flag,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
-        else
-            [xparam1,hh,gg,fval,invhess] = newrat('DsgeVarLikelihood',xparam1,hh,gg,igg,crit,nit,flag,gend);
-        end
         [xparam1,hh,gg,fval,invhess] = newrat(objective_function,xparam1,hh,gg,igg,crit,nit,flag,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
         parameter_names = bayestopt_.name;
         save([M_.fname '_mode.mat'],'xparam1','hh','gg','fval','invhess','parameter_names');
@@ -362,7 +357,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
 end
 
 if options_.cova_compute == 0
-    hh = NaN(length(xparam1),length(xparam1));
+    hh = [];%NaN(length(xparam1),length(xparam1));
 end
 
 if ~options_.mh_posterior_mode_estimation && options_.cova_compute
@@ -380,11 +375,7 @@ if ~options_.mh_posterior_mode_estimation && options_.cova_compute
 end
 
 if options_.mode_check == 1 && ~options_.mh_posterior_mode_estimation
-    if options_.cova_compute
-        mode_check(xparam1,0,hh,gend,data,lb,ub,data_index,number_of_observations,no_more_missing_observations);
-    else
-        mode_check(xparam1,0,[],gend,data,lb,ub,data_index,number_of_observations,no_more_missing_observations);
-    end
+    mode_check('objective_function',xparam1,hh,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
 end
 
 if ~options_.mh_posterior_mode_estimation
@@ -509,16 +500,10 @@ if any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
         estim_params_nbr = size(xparam1,1);
         scale_factor = -sum(log10(diag(invhess)));
         log_det_invhess = -estim_params_nbr*log(scale_factor)+log(det(scale_factor*invhess));
-        if ~options_.dsge_var
-            md_Laplace = .5*estim_params_nbr*log(2*pi) + .5*log_det_invhess ...
-                - DsgeLikelihood(xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
-        else
-            md_Laplace = .5*estim_params_nbr*log(2*pi) + .5*log_det_invhess ...
-                - DsgeVarLikelihood(xparam1,gend);
-        end
-        oo_.MarginalDensity.LaplaceApproximation = md_Laplace;
+        likelihood = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+        oo_.MarginalDensity.LaplaceApproximation = .5*estim_params_nbr*log(2*pi) + .5*log_det_invhess - likelihood;
         disp(' ')
-        disp(sprintf('Log data density [Laplace approximation] is %f.',md_Laplace))
+        disp(sprintf('Log data density [Laplace approximation] is %f.',oo_.MarginalDensity.LaplaceApproximation))
         disp(' ')
     end
 elseif ~any(bayestopt_.pshape > 0) && options_.mh_posterior_mode_estimation
@@ -807,11 +792,7 @@ if (any(bayestopt_.pshape  >0 ) && options_.mh_replic) || ...
             invhess = compute_mh_covariance_matrix;
         end
         if options_.cova_compute
-            if options_.dsge_var
-                feval(options_.posterior_sampling_method,'DsgeVarLikelihood',options_.proposal_distribution,xparam1,invhess,bounds,gend);
-            else
-                feval(options_.posterior_sampling_method,'DsgeLikelihood',options_.proposal_distribution,xparam1,invhess,bounds,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
-            end
+            feval(options_.posterior_sampling_method,objective_function,options_.proposal_distribution,xparam1,invhess,bounds,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
         else
             error('I Cannot start the MCMC because the hessian of the posterior kernel at the mode was not computed.')
         end
diff --git a/matlab/dynare_resolve.m b/matlab/dynare_resolve.m
index ef6bc6379..1020180ea 100644
--- a/matlab/dynare_resolve.m
+++ b/matlab/dynare_resolve.m
@@ -1,4 +1,4 @@
-function [A,B,ys,info,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults)
+function [A,B,ys,info,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults,mode)
 % Computes the linear approximation and the matrices A and B of the transition equation.
 
 %@info:
@@ -80,15 +80,18 @@ if info(1) > 0
     return
 end
 
-if nargin == 0
+switch nargin
+  case 3
     endo_nbr = Model.endo_nbr;
     nstatic = DynareResults.dr.nstatic;
     npred = DynareResults.dr.npred;
     iv = (1:endo_nbr)';
     ic = [ nstatic+(1:npred) endo_nbr+(1:size(DynareResults.dr.ghx,2)-npred) ]';
-else
+  case 4
     iv = DynareResults.dr.restrict_var_list;
     ic = DynareResults.dr.restrict_columns;
+  otherwise
+    error('dynare_resolve:: Error in the calling sequence!')
 end
 
 if nargout==1
diff --git a/matlab/gsa/stab_map_.m b/matlab/gsa/stab_map_.m
index b828ac558..5198a6744 100644
--- a/matlab/gsa/stab_map_.m
+++ b/matlab/gsa/stab_map_.m
@@ -247,7 +247,7 @@ if fload==0,
         M_.params(estim_params_.param_vals(:,1)) = lpmat(j,:)';
         %try stoch_simul([]);
         try
-            [Tt,Rr,SteadyState,infox{j},M_,options_,oo_] = dynare_resolve(M_,options_,oo_);
+            [Tt,Rr,SteadyState,infox{j},M_,options_,oo_] = dynare_resolve(M_,options_,oo_,'restrict');
             if infox{j}(1)==0 && ~exist('T'),
                 dr_=oo_.dr;
                 T=zeros(size(dr_.ghx,1),size(dr_.ghx,2)+size(dr_.ghu,2),Nsam);
@@ -402,7 +402,7 @@ else
         for j=1:ntrans,
             M_.params(estim_params_.param_vals(:,1)) = lpmat(istable(j),:)';
             %stoch_simul([]);
-            [Tt,Rr,SteadyState,info,M_,options_,oo_] = dynare_resolve(M_,options_,oo_);
+            [Tt,Rr,SteadyState,info,M_,options_,oo_] = dynare_resolve(M_,options_,oo_,'restrict');
             % This syntax is not compatible with the current version of dynare_resolve [stepan].
             %[Tt,Rr,SteadyState,info] = dynare_resolve(bayestopt_.restrict_var_list,...
             %    bayestopt_.restrict_columns,...
diff --git a/matlab/gsa/thet2tau.m b/matlab/gsa/thet2tau.m
index c4f63f3dd..165c33d53 100644
--- a/matlab/gsa/thet2tau.m
+++ b/matlab/gsa/thet2tau.m
@@ -17,7 +17,6 @@ M_.params(indx) = params(length(indexo)+1:end);
 if ~isempty(indexo)
   M_.Sigma_e(indexo,indexo) = diag(params(1:length(indexo)).^2);
 end
-% [A(oo_.dr.order_var,oo_.dr.order_var),B(oo_.dr.order_var,:)]=dynare_resolve;
 [A,B,plouf,plouf,M_,options_,oo_] = dynare_resolve(M_,options_,oo_);
 if flagmoments==0,
 tau = [oo_.dr.ys(oo_.dr.order_var); A(:); dyn_vech(B*M_.Sigma_e*B')];
diff --git a/matlab/initial_estimation_checks.m b/matlab/initial_estimation_checks.m
index 51dd4a399..02638fc76 100644
--- a/matlab/initial_estimation_checks.m
+++ b/matlab/initial_estimation_checks.m
@@ -35,7 +35,7 @@ if DynareDataset.info.nvobs>Model.exo_nbr+EstimatedParameters.nvn
 end
 
 if DynareOptions.dsge_var
-    [fval,cost_flag,info] = DsgeVarLikelihood(xparam1,gend);
+    [fval,cost_flag,info] = DsgeVarLikelihood(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
 else
     [fval,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
 end
diff --git a/matlab/mode_check.m b/matlab/mode_check.m
index 172a05506..8e14a3e20 100644
--- a/matlab/mode_check.m
+++ b/matlab/mode_check.m
@@ -1,8 +1,8 @@
-function mode_check(x,fval,hessian,gend,data,lb,ub,data_index,number_of_observations,no_more_missing_observations)
+function mode_check(func,x,fval,hessian,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
 
 % function mode_check(x,fval,hessian,gend,data,lb,ub)
-% Checks the maximum likelihood mode 
-% 
+% Checks the maximum likelihood mode
+%
 % INPUTS
 %    x:       mode
 %    fval:    value at the maximum likelihood mode
@@ -14,7 +14,7 @@ function mode_check(x,fval,hessian,gend,data,lb,ub,data_index,number_of_observat
 %
 % OUTPUTS
 %    none
-%        
+%
 % SPECIAL REQUIREMENTS
 %    none
 
@@ -35,18 +35,12 @@ function mode_check(x,fval,hessian,gend,data,lb,ub,data_index,number_of_observat
 % 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_ M_ options_ estim_params_
-
-TeX = options_.TeX;
+TeX = DynareOptions.TeX;
 if ~isempty(hessian);
     [ s_min, k ] = min(diag(hessian));
 end
-if options_.dsge_var
-    fval = DsgeVarLikelihood(x,gend);
-else
-    fval = DsgeLikelihood(x,gend,data,data_index,number_of_observations,no_more_missing_observations);    
-end    
-bayestopt_.penalty=fval;
+
+fval = feval(fun,x,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
 
 if ~isempty(hessian);
     disp(' ')
@@ -55,14 +49,14 @@ if ~isempty(hessian);
     disp(sprintf('Fval obtained by the minimization routine: %f', fval))
     disp(' ')
     if s_min<eps
-        disp(sprintf('Most negative variance %f for parameter %d (%s = %f)', s_min, k , bayestopt_.name{k}, x(k)))
+        disp(sprintf('Most negative variance %f for parameter %d (%s = %f)', s_min, k , BayesInfo.name{k}, x(k)))
     end
 end
 
 [nbplt,nr,nc,lr,lc,nstar] = pltorg(length(x));
 
 if TeX
-    fidTeX = fopen([M_.fname '_CheckPlots.TeX'],'w');
+    fidTeX = fopen([Model.fname '_CheckPlots.TeX'],'w');
     fprintf(fidTeX,'%% TeX eps-loader file generated by mode_check.m (Dynare).\n');
     fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
     fprintf(fidTeX,' \n');
@@ -77,7 +71,7 @@ for plt = 1:nbplt,
     for k=1:min(nstar,length(x)-(plt-1)*nstar)
         subplot(nr,nc,k)
         kk = (plt-1)*nstar+k;
-        [name,texname] = get_the_name(kk,TeX,M_,estim_params_,options_);
+        [name,texname] = get_the_name(kk,TeX,Model,EstimatedParameters,DynareOptions);
         if TeX
             if isempty(NAMES)
                 NAMES = name;
@@ -88,32 +82,23 @@ for plt = 1:nbplt,
             end
         end
         xx = x;
-        l1 = max(lb(kk),0.5*x(kk));
-        l2 = min(ub(kk),1.5*x(kk));
+        l1 = max(BayesInfo.lb(kk),0.5*x(kk));
+        l2 = min(BayesInfo.ub(kk),1.5*x(kk));
         z = [l1:(l2-l1)/20:l2];
-        if options_.mode_check_nolik==0,
+        if DynareOptions.mode_check_nolik==0,
             y = zeros(length(z),2);
-            dy = priordens(xx,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
+            dy = priordens(xx,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
         end
         for i=1:length(z)
             xx(kk) = z(i);
-            if options_.dsge_var
-                [fval,cost_flag] = DsgeVarLikelihood(xx,gend);
-                if cost_flag
-                    y(i,1) = fval;
-                else
-                    y(i,1) = NaN;
-                end               
+            [fval, exit_flag] = feval(fun,x,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+            if exit_flag
+                y(i,1) = fval;
             else
-                [fval,cost_flag] = DsgeLikelihood(xx,gend,data,data_index,number_of_observations,no_more_missing_observations);
-                if cost_flag
-                    y(i,1) = fval;
-                else
-                    y(i,1) = NaN;
-                end               
+                y(i,1) = NaN;
             end
-            if options_.mode_check_nolik==0
-                lnprior = priordens(xx,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
+            if DynareOptions.mode_check_nolik==0
+                lnprior = priordens(xx,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
                 y(i,2)  = (y(i,1)+lnprior-dy);
             end
         end
@@ -130,7 +115,7 @@ for plt = 1:nbplt,
         axis tight
         drawnow
     end
-    if options_.mode_check_nolik==0,
+    if DynareOptions.mode_check_nolik==0,
         if exist('OCTAVE_VERSION'),
             axes('outerposition',[0.3 0.93 0.42 0.07],'box','on'),
         else
@@ -142,12 +127,12 @@ for plt = 1:nbplt,
         text(0.25,0.5,'log-post')
         text(0.69,0.5,'log-lik kernel')
     end
-    eval(['print -depsc2 ' M_.fname '_CheckPlots' int2str(plt) '.eps']);
+    eval(['print -depsc2 ' Model.fname '_CheckPlots' int2str(plt) '.eps']);
     if ~exist('OCTAVE_VERSION')
-        eval(['print -dpdf ' M_.fname '_CheckPlots' int2str(plt)]);
-        saveas(hh,[M_.fname '_CheckPlots' int2str(plt) '.fig']);
+        eval(['print -dpdf ' Model.fname '_CheckPlots' int2str(plt)]);
+        saveas(hh,[Model.fname '_CheckPlots' int2str(plt) '.fig']);
     end
-    if options_.nograph, close(hh), end
+    if DynareOptions.nograph, close(hh), end
     if TeX
         % TeX eps loader file
         fprintf(fidTeX,'\\begin{figure}[H]\n');
@@ -155,7 +140,7 @@ for plt = 1:nbplt,
             fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TeXNAMES(jj,:)));
         end
         fprintf(fidTeX,'\\centering \n');
-        fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_CheckPlots%s}\n',M_.fname,int2str(plt));
+        fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_CheckPlots%s}\n',Model.fname,int2str(plt));
         fprintf(fidTeX,'\\caption{Check plots.}');
         fprintf(fidTeX,'\\label{Fig:CheckPlots:%s}\n',int2str(plt));
         fprintf(fidTeX,'\\end{figure}\n');
diff --git a/matlab/thet2tau.m b/matlab/thet2tau.m
index 0c236fb8d..2ae5b2c5c 100644
--- a/matlab/thet2tau.m
+++ b/matlab/thet2tau.m
@@ -36,7 +36,6 @@ M_.params(indx) = params(length(indexo)+1:end);
 if ~isempty(indexo)
     M_.Sigma_e(indexo,indexo) = diag(params(1:length(indexo)).^2);
 end
-% [A(oo_.dr.order_var,oo_.dr.order_var),B(oo_.dr.order_var,:)]=dynare_resolve;
 [A,B,tele,tubbies,M_,options_,oo_] = dynare_resolve(M_,options_,oo_);
 if flagmoments==0,
     tau = [oo_.dr.ys(oo_.dr.order_var); A(:); dyn_vech(B*M_.Sigma_e*B')];