diff --git a/matlab/particle/DsgeLikelihood.m b/matlab/particle/DsgeLikelihood.m
index da94f6953..ff6f32641 100644
--- a/matlab/particle/DsgeLikelihood.m
+++ b/matlab/particle/DsgeLikelihood.m
@@ -255,7 +255,7 @@ function [fval,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend,data
   rfm.measurement.H = H;
   number_of_particles = 10;
   
-  LIK = monte_carlo_gaussian_particle_filter(rfm,Y);
+  LIK = monte_carlo_gaussian_particle_filter(rfm,Y,[],number_of_particles);
   
 
   % ------------------------------------------------------------------------------
diff --git a/matlab/particle/local_state_space_iteration_2.m b/matlab/particle/local_state_space_iteration_2.m
index 41eb55f50..89faa12d0 100644
--- a/matlab/particle/local_state_space_iteration_2.m
+++ b/matlab/particle/local_state_space_iteration_2.m
@@ -16,8 +16,7 @@ function y = local_state_space_iteration_2(yhat,epsilon,ghx,ghu,constant,half_gh
 %    constant      [double]     m*1 vector (steady state + second order correction).
 %    half_ghxx     [double]     m*n² matrix, subset of .5*dr.ghxx. 
 %    half_ghuu     [double]     m*q² matrix, subset of .5*dr.ghuu.
-%    ghxu          [double]     m*nq matrix, subset of dr.ghxu.
-%    index         [integer]    
+%    ghxu          [double]     m*nq matrix, subset of dr.ghxu.    
 %
 % OUTPUTS
 %    y             [double]     stochastic simulations results
diff --git a/matlab/particle/monte_carlo_gaussian_particle_filter.m b/matlab/particle/monte_carlo_gaussian_particle_filter.m
index 28e05a7ae..774d45a27 100644
--- a/matlab/particle/monte_carlo_gaussian_particle_filter.m
+++ b/matlab/particle/monte_carlo_gaussian_particle_filter.m
@@ -39,7 +39,7 @@ function [LIK,lik] = monte_carlo_gaussian_particle_filter(reduced_form_model,Y,s
 % 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_ bayestopt_
+global M_ bayestopt_ oo_
 persistent init_flag
 persistent restrict_variables_idx observed_variables_idx state_variables_idx mf0 mf1
 persistent sample_size number_of_state_variables number_of_observed_variables number_of_structural_innovations
@@ -63,7 +63,7 @@ if isempty(init_flag)
     restrict_variables_idx  = bayestopt_.restrict_var_list;
     observed_variables_idx  = restrict_variables_idx(mf1);
     state_variables_idx     = restrict_variables_idx(mf0);
-     sample_size = size(Y,2);
+    sample_size = size(Y,2);
     number_of_state_variables = length(mf0);
     number_of_observed_variables = length(mf1);
     number_of_structural_innovations = length(Q); 
@@ -85,12 +85,8 @@ StateVectorVariance = lyapunov_symm(ghx(mf0,:),ghu(mf0,:)*Q*ghu(mf0,:)',1e-12,1e
 StateVectorVarianceSquareRoot = reduced_rank_cholesky(StateVectorVariance)';
 state_variance_rank = size(StateVectorVarianceSquareRoot,2);
 
-%state_idx = 1:state_variance_rank;
-%innovation_idx = 1+state_variance_rank:state_variance_rank+number_of_structural_innovations;
-
 Q_lower_triangular_cholesky = chol(Q)';
 
-
 % Set seed for randn().
 seed  = [ 362436069 ; 521288629 ];
 randn('state',seed);
@@ -98,7 +94,6 @@ randn('state',seed);
 const_lik = log(2*pi)*number_of_observed_variables; 
 lik  = NaN(sample_size,1);
 
-
 for t=1:sample_size
     PredictedStateMean = zeros(number_of_state_variables,1);
     PredictedObservedMean = zeros(number_of_observed_variables,1);