diff --git a/src/conditional_filter_proposal.m b/src/conditional_filter_proposal.m
index aa82a449a..0ef5aedab 100644
--- a/src/conditional_filter_proposal.m
+++ b/src/conditional_filter_proposal.m
@@ -1,4 +1,4 @@
-function [ProposalStateVector,Weights] = conditional_filter_proposal(ReducedForm,obs,StateVectors,SampleWeights,Q_lower_triangular_cholesky,H_lower_triangular_cholesky,H,DynareOptions,normconst2)
+function [ProposalStateVector,Weights] = conditional_filter_proposal(ReducedForm,obs,StateVectors,SampleWeights,Q_lower_triangular_cholesky,H_lower_triangular_cholesky,H,ParticleOptions,ThreadsOptions,normconst2)
 %
 % Computes the proposal for each past particle using Gaussian approximations
 % for the state errors and the Kalman filter
@@ -73,11 +73,11 @@ if isempty(init_flag2)
     init_flag2 = 1;
 end
 
-if DynareOptions.particle.proposal_approximation.cubature || DynareOptions.particle.proposal_approximation.montecarlo
+if ParticleOptions.proposal_approximation.cubature || ParticleOptions.proposal_approximation.montecarlo
     [nodes,weights] = spherical_radial_sigma_points(number_of_structural_innovations);
     weights_c = weights ;
-elseif DynareOptions.particle.proposal_approximation.unscented
-    [nodes,weights,weights_c] = unscented_sigma_points(number_of_structural_innovations,DynareOptions);
+elseif ParticleOptions.proposal_approximation.unscented
+    [nodes,weights,weights_c] = unscented_sigma_points(number_of_structural_innovations,ParticleOptions);
 else
     error('Estimation: This approximation for the proposal is not implemented or unknown!')
 end
@@ -85,12 +85,12 @@ end
 epsilon = Q_lower_triangular_cholesky*(nodes') ;
 yhat = repmat(StateVectors-state_variables_steady_state,1,size(epsilon,2)) ;
 
-tmp = local_state_space_iteration_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,DynareOptions.threads.local_state_space_iteration_2);
+tmp = local_state_space_iteration_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,ThreadsOptions.local_state_space_iteration_2);
 
 PredictedStateMean = tmp(mf0,:)*weights ;
 PredictedObservedMean = tmp(mf1,:)*weights;
 
-if DynareOptions.particle.proposal_approximation.cubature || DynareOptions.particle.proposal_approximation.montecarlo
+if ParticleOptions.proposal_approximation.cubature || ParticleOptions.proposal_approximation.montecarlo
     PredictedStateMean = sum(PredictedStateMean,2) ;
     PredictedObservedMean = sum(PredictedObservedMean,2) ;
     dState = bsxfun(@minus,tmp(mf0,:),PredictedStateMean)'.*sqrt(weights) ;
@@ -117,7 +117,7 @@ else
 end
 
 ProposalStateVector = StateVectorVarianceSquareRoot*randn(size(StateVectorVarianceSquareRoot,2),1)+StateVectorMean ;
-ypred = measurement_equations(ProposalStateVector,ReducedForm,DynareOptions) ;
+ypred = measurement_equations(ProposalStateVector,ReducedForm,ThreadsOptions) ;
 foo = H_lower_triangular_cholesky \ (obs - ypred) ;
 likelihood = exp(-0.5*(foo)'*foo)/normconst2 + 1e-99 ;
 Weights = SampleWeights.*likelihood;
diff --git a/src/conditional_particle_filter.m b/src/conditional_particle_filter.m
index 3f365f85c..4aef9f392 100644
--- a/src/conditional_particle_filter.m
+++ b/src/conditional_particle_filter.m
@@ -1,4 +1,4 @@
-function [LIK,lik] = conditional_particle_filter(ReducedForm,Y,start,DynareOptions)
+function [LIK,lik] = conditional_particle_filter(ReducedForm,Y,start,ParticleOptions,ThreadsOptions)
 % 
 % Evaluates the likelihood of a non-linear model with a particle filter
 % - the proposal is built using the Kalman updating step for each particle. 
@@ -74,7 +74,7 @@ if isempty(init_flag)
     number_of_state_variables = length(mf0);
     number_of_observed_variables = length(mf1);
     init_flag = 1;
-    number_of_particles = DynareOptions.particle.number_of_particles ;
+    number_of_particles = ParticleOptions.number_of_particles ;
 end
 
 % Get covariance matrices
@@ -108,14 +108,14 @@ SampleWeights = ones(1,number_of_particles)/number_of_particles ;
 for t=1:sample_size
     for i=1:number_of_particles 
       [StateParticles(:,i),SampleWeights(i)] = ...
-          conditional_filter_proposal(ReducedForm,Y(:,t),StateParticles(:,i),SampleWeights(i),Q_lower_triangular_cholesky,H_lower_triangular_cholesky,H,DynareOptions,normconst2) ;
+          conditional_filter_proposal(ReducedForm,Y(:,t),StateParticles(:,i),SampleWeights(i),Q_lower_triangular_cholesky,H_lower_triangular_cholesky,H,ParticleOptions,ThreadsOptions,normconst2) ;
     end
     SumSampleWeights = sum(SampleWeights) ;
     lik(t) = log(SumSampleWeights) ; 
     SampleWeights = SampleWeights./SumSampleWeights ;		
-    if (DynareOptions.particle.resampling.status.generic && neff(SampleWeights)<DynareOptions.particle.resampling.threshold*sample_size) || DynareOptions.particle.resampling.status.systematic
+    if (ParticleOptions.resampling.status.generic && neff(SampleWeights)<ParticleOptions.resampling.threshold*sample_size) || ParticleOptions.resampling.status.systematic
         ks = ks + 1 ;
-        StateParticles = resample(StateParticles',SampleWeights',DynareOptions)';
+        StateParticles = resample(StateParticles',SampleWeights',ParticleOptions)';
         SampleWeights = ones(1,number_of_particles)/number_of_particles ;
     end
 end
diff --git a/src/measurement_equations.m b/src/measurement_equations.m
index c00d8ebc4..d38c59aea 100644
--- a/src/measurement_equations.m
+++ b/src/measurement_equations.m
@@ -1,4 +1,4 @@
-function measure = measurement_equations(StateVectors,ReducedForm,DynareOptions) 
+function measure = measurement_equations(StateVectors,ReducedForm,ThreadsOptions) 
 
 % Copyright (C) 2013 Dynare Team
 %
@@ -27,7 +27,7 @@ constant = ReducedForm.constant(mf1,:);
 state_variables_steady_state = ReducedForm.state_variables_steady_state;
 number_of_structural_innovations = length(ReducedForm.Q);
 yhat = bsxfun(@minus,StateVectors,state_variables_steady_state) ;
-measure = local_state_space_iteration_2(yhat,zeros(number_of_structural_innovations,size(yhat,2)),ghx,ghu,constant,ghxx,ghuu,ghxu,DynareOptions.threads.local_state_space_iteration_2);
+measure = local_state_space_iteration_2(yhat,zeros(number_of_structural_innovations,size(yhat,2)),ghx,ghu,constant,ghxx,ghuu,ghxu,ThreadsOptions.local_state_space_iteration_2);
 
 
 
diff --git a/src/unscented_sigma_points.m b/src/unscented_sigma_points.m
index 26fcc4b5c..38771758b 100644
--- a/src/unscented_sigma_points.m
+++ b/src/unscented_sigma_points.m
@@ -1,4 +1,4 @@
-function [nodes,W_m,W_c] = unscented_sigma_points(n,DynareOptions) 
+function [nodes,W_m,W_c] = unscented_sigma_points(n,ParticleOptions) 
 %
 % Computes nodes and weigths for a scaled unscented transform cubature 
 % INPUTS
@@ -31,10 +31,10 @@ function [nodes,W_m,W_c] = unscented_sigma_points(n,DynareOptions)
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
-lambda = (DynareOptions.particle.unscented.alpha^2)*(n+DynareOptions.particle.unscented.kappa) - n ; 
+lambda = (ParticleOptions.unscented.alpha^2)*(n+ParticleOptions.unscented.kappa) - n ; 
 nodes = [ zeros(n,1) ( sqrt(n+lambda).*([ eye(n) -eye(n)]) ) ]' ;
 W_m = lambda/(n+lambda) ;
-W_c = W_m + (1-DynareOptions.particle.unscented.alpha^2+DynareOptions.particle.unscented.beta) ;
+W_c = W_m + (1-ParticleOptions.unscented.alpha^2+ParticleOptions.unscented.beta) ;
 temp = ones(2*n,1)/(2*(n+lambda)) ;
 W_m = [W_m ; temp] ; 
 W_c = [W_c ; temp]  ;