diff --git a/matlab/estimation/online/online_auxiliary_filter.m b/matlab/estimation/online/online_auxiliary_filter.m
index f4945f491f57cf98ff706546f32519b8e976d73f..461cb888e7abc1dd2ab0e3f87d01dbf4694d6a52 100644
--- a/matlab/estimation/online/online_auxiliary_filter.m
+++ b/matlab/estimation/online/online_auxiliary_filter.m
@@ -31,6 +31,7 @@ function online_auxiliary_filter(xparam1, dataset_, options_, M_, estim_params_,
 
 % Set seed for randn().
 options_ = set_dynare_seed_local_options(options_,'default');
+options_.verbosity=0; %particularly suppress warning messages during k_order_pert within the loop
 pruning = options_.particle.pruning;
 variance_update = true;
 online_opt = options_.posterior_sampler_options.current_options;
@@ -78,10 +79,10 @@ xparam = zeros(number_of_parameters,number_of_particles);
 Prior = dprior(bayestopt_, options_.prior_trunc);
 for i=1:number_of_particles
     info = 12042009;
-    while info
+    while info(1)
         candidate = Prior.draw();
         [info] = solve_model_for_online_filter(false, candidate, dataset_, options_, M_, estim_params_, bayestopt_, bounds, oo_.dr , oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
-        if ~info
+        if ~info(1)
             xparam(:,i) = candidate(:);
         end
     end
diff --git a/tests/estimation/univariate/bayesian.mod b/tests/estimation/univariate/bayesian.mod
index d5dcce24f7dc3616e9a69e567acac0f2642dbe07..0a21e59196e6b574a0ad671fb776899ac10c6734 100644
--- a/tests/estimation/univariate/bayesian.mod
+++ b/tests/estimation/univariate/bayesian.mod
@@ -47,7 +47,7 @@ s2df = 1;
 // form expression for the joint posterior marginal distribution of β, a Gibbs
 // sampling algorithm is used (the prior for β and the inverse of σ² are independent).
 
-gibbslength = 1000000; // Set the number of iterations in Gibbs 
+gibbslength = 300000; // Set the number of iterations in Gibbs 
 burnin = 10000;        // Set the number of iterations to be discarded (try to remove the effects of the initial condition).
 steps = 10;            // Do not record all iterations (try to remove the dependence between the draws).
 
diff --git a/tests/estimation/univariate/bayesian_param_names.mod b/tests/estimation/univariate/bayesian_param_names.mod
index 54e042a08cb7ec01a604fc3f38a690e3281c8c57..a148754dfce286cb87f1bfc99073c089ba4ebcd8 100644
--- a/tests/estimation/univariate/bayesian_param_names.mod
+++ b/tests/estimation/univariate/bayesian_param_names.mod
@@ -50,14 +50,14 @@ s2df = 1;
 // form expression for the joint posterior marginal distribution of β, a Gibbs
 // sampling algorithm is used (the prior for β and the inverse of σ² are independent).
 
-gibbslength = 1000000; // Set the number of iterations in Gibbs 
+gibbslength = 100000; // Set the number of iterations in Gibbs 
 burnin = 10000;        // Set the number of iterations to be discarded (try to remove the effects of the initial condition).
 steps = 10;            // Do not record all iterations (try to remove the dependence between the draws).
 
 ds = olsgibbs(ds, 'eqols', beta0, V0, s2priormean, s2df, gibbslength, burnin, steps, {'eqols', 'eqols_olsgibbs_fit'}, 'olsgibbs_eq',{'b2'; 'b3'});
 
 // Since we use a diffuse prior for β, the posterior mean of β should be close to the OLS estimate.
-if max(abs(oo_.ols.ols_eq.beta-oo_.olsgibbs.olsgibbs_eq.posterior.mean.beta))>.1
+if max(abs(oo_.ols.ols_eq.beta-oo_.olsgibbs.olsgibbs_eq.posterior.mean.beta))>.01
    error('Something is wrong in the Gibbs sampling routine (univariate model)')
 end
 
diff --git a/tests/observation_trends_and_prefiltering/MCMC/Trend_loglin_no_prefilt_first_obs_MC.mod b/tests/observation_trends_and_prefiltering/MCMC/Trend_loglin_no_prefilt_first_obs_MC.mod
index 05f602b22c3d48c83cd68eb5dc0957b55acd30bc..1287d2915d12078c3dab25d2e367c181a3a2a37c 100644
--- a/tests/observation_trends_and_prefiltering/MCMC/Trend_loglin_no_prefilt_first_obs_MC.mod
+++ b/tests/observation_trends_and_prefiltering/MCMC/Trend_loglin_no_prefilt_first_obs_MC.mod
@@ -7,7 +7,7 @@ estimation(order=1,datafile='Trend_loglin_no_prefilt_first_obs_MC_Exp_AR1_trend_
     mode_compute=4,silent_optimizer,first_obs=1000,loglinear,smoother,forecast=100,prefilter=0,
     mcmc_jumping_covariance='Trend_loglin_no_prefilt_first_obs_MC_MCMC_jump_covar',
     filtered_vars, filter_step_ahead = [1,2,4],
-    mh_nblocks=1,mh_jscale=0.3,no_posterior_kernel_density) P_obs Y_obs junk2;
+    mh_nblocks=1,mh_jscale=0.3,no_posterior_kernel_density,nograph,sub_draws=100) P_obs Y_obs junk2;
 load('Trend_loglin_no_prefilt_first_obs_MC_Exp_AR1_trend_data_with_constant');
 @#include "../Trend_load_data_common.inc" 
 
diff --git a/tests/observation_trends_and_prefiltering/MCMC/Trend_loglin_prefilt_first_obs_MC.mod b/tests/observation_trends_and_prefiltering/MCMC/Trend_loglin_prefilt_first_obs_MC.mod
index c50534c9b4e27dfde3091ae5b1e61a16c2abaea7..e68ceef6622d9102fc411aef68a920e451db7d50 100644
--- a/tests/observation_trends_and_prefiltering/MCMC/Trend_loglin_prefilt_first_obs_MC.mod
+++ b/tests/observation_trends_and_prefiltering/MCMC/Trend_loglin_prefilt_first_obs_MC.mod
@@ -7,7 +7,7 @@ estimation(order=1,datafile='Trend_loglin_prefilt_first_obs_MC_Exp_AR1_trend_dat
     mode_compute=4,silent_optimizer,first_obs=1000,loglinear,smoother,forecast=100,prefilter=1,
     mcmc_jumping_covariance='Trend_loglin_prefilt_first_obs_MC_MCMC_jump_covar_prefilter',
     filtered_vars, filter_step_ahead = [1,2,4],
-    mh_nblocks=1,mh_jscale=1e-4,no_posterior_kernel_density) P_obs Y_obs junk2;
+    mh_nblocks=1,mh_jscale=1e-4,no_posterior_kernel_density,nograph,sub_draws=100) P_obs Y_obs junk2;
     
 load('Trend_loglin_prefilt_first_obs_MC_Exp_AR1_trend_data_with_constant');
 @#include "../Trend_load_data_common.inc" 
diff --git a/tests/observation_trends_and_prefiltering/MCMC/Trend_loglinear_no_prefilter_MC.mod b/tests/observation_trends_and_prefiltering/MCMC/Trend_loglinear_no_prefilter_MC.mod
index 03d99442ff86f93993b05016ab6ab7f3cdfe1499..39869dd1e2a43b5078b0a4014309d4d260716685 100644
--- a/tests/observation_trends_and_prefiltering/MCMC/Trend_loglinear_no_prefilter_MC.mod
+++ b/tests/observation_trends_and_prefiltering/MCMC/Trend_loglinear_no_prefilter_MC.mod
@@ -7,7 +7,7 @@ estimation(order=1,datafile='Trend_loglinear_no_prefilter_MC_Exp_AR1_trend_data_
     mode_compute=4,silent_optimizer,first_obs=1,loglinear,diffuse_filter,smoother,forecast=100,prefilter=0,
     mcmc_jumping_covariance='Trend_loglinear_no_prefilter_MC_MCMC_jump_covar',
     filtered_vars, filter_step_ahead = [1,2,4],
-    mh_nblocks=1,mh_jscale=0.3) P_obs Y_obs junk2;
+    mh_nblocks=1,mh_jscale=0.3,no_posterior_kernel_density,nograph,sub_draws=100) P_obs Y_obs junk2;
 load('Trend_loglinear_no_prefilter_MC_Exp_AR1_trend_data_with_constant');
 @#include "../Trend_load_data_common.inc" 
 
diff --git a/tests/observation_trends_and_prefiltering/MCMC/Trend_loglinear_prefilter_MC.mod b/tests/observation_trends_and_prefiltering/MCMC/Trend_loglinear_prefilter_MC.mod
index e74801bf0e2b79cd9e3b7443645411676338d83f..80c0a0a878982e5024f9464fa1c55d81e20141c7 100644
--- a/tests/observation_trends_and_prefiltering/MCMC/Trend_loglinear_prefilter_MC.mod
+++ b/tests/observation_trends_and_prefiltering/MCMC/Trend_loglinear_prefilter_MC.mod
@@ -7,7 +7,7 @@ estimation(order=1,datafile='Trend_loglinear_prefilter_MC_Exp_AR1_trend_data_wit
     mode_compute=4,silent_optimizer,first_obs=1,loglinear,smoother,forecast=100,prefilter=1,
     mcmc_jumping_covariance='Trend_loglinear_prefilter_MC_MCMC_jump_covar_prefilter',
     filtered_vars, filter_step_ahead = [1,2,4],
-    mh_nblocks=1,mh_jscale=1e-4) P_obs Y_obs junk2;
+    mh_nblocks=1,mh_jscale=1e-4,no_posterior_kernel_density,nograph,sub_draws=100) P_obs Y_obs junk2;
     
 load('Trend_loglinear_prefilter_MC_Exp_AR1_trend_data_with_constant');
 @#include "../Trend_load_data_common.inc" 
diff --git a/tests/observation_trends_and_prefiltering/MCMC/Trend_no_prefilter_MC.mod b/tests/observation_trends_and_prefiltering/MCMC/Trend_no_prefilter_MC.mod
index 9c89cf9e035022b79f9fdf3185aa7b4e9aa2ff9b..07928c9f8ad2aa603cf3cc8eb5cc2522be1c90f0 100644
--- a/tests/observation_trends_and_prefiltering/MCMC/Trend_no_prefilter_MC.mod
+++ b/tests/observation_trends_and_prefiltering/MCMC/Trend_no_prefilter_MC.mod
@@ -6,7 +6,7 @@ generate_trend_stationary_AR1(M_.fname);
 estimation(order=1,datafile='Trend_no_prefilter_MC_AR1_trend_data_with_constant',mh_replic=400,silent_optimizer,
             mode_compute=4,first_obs=1,smoother,mh_nblocks=1,mh_jscale=0.3,
             filtered_vars, filter_step_ahead = [1,2,4],
-            mcmc_jumping_covariance='Trend_no_prefilter_MC_MCMC_jump_covar',forecast=100,prefilter=0) P_obs Y_obs junk2;
+            mcmc_jumping_covariance='Trend_no_prefilter_MC_MCMC_jump_covar',forecast=100,prefilter=0,no_posterior_kernel_density,nograph,sub_draws=100) P_obs Y_obs junk2;
             
 load('Trend_no_prefilter_MC_AR1_trend_data_with_constant');
 @#include "../Trend_load_data_common.inc" 
diff --git a/tests/observation_trends_and_prefiltering/MCMC/Trend_no_prefilter_first_obs_MC.mod b/tests/observation_trends_and_prefiltering/MCMC/Trend_no_prefilter_first_obs_MC.mod
index 58fc82ad3757d9eb41a6326e976679717ea876de..b48dbf3639e61b7ddfe2f332a6d178848c281980 100644
--- a/tests/observation_trends_and_prefiltering/MCMC/Trend_no_prefilter_first_obs_MC.mod
+++ b/tests/observation_trends_and_prefiltering/MCMC/Trend_no_prefilter_first_obs_MC.mod
@@ -7,7 +7,7 @@ estimation(order=1,datafile='Trend_no_prefilter_first_obs_MC_AR1_trend_data_with
         mh_replic=400,mode_compute=4,silent_optimizer,first_obs=1000,smoother,forecast=100,prefilter=0,
         mcmc_jumping_covariance='Trend_no_prefilter_first_obs_MC_MCMC_jump_covar',
         filtered_vars, filter_step_ahead = [1,2,4],
-        mh_nblocks=1,mh_jscale=0.3,no_posterior_kernel_density) P_obs Y_obs junk2;
+        mh_nblocks=1,mh_jscale=0.3,no_posterior_kernel_density,nograph,sub_draws=100) P_obs Y_obs junk2;
 
 load('Trend_no_prefilter_first_obs_MC_AR1_trend_data_with_constant');
 @#include "../Trend_load_data_common.inc" 
diff --git a/tests/observation_trends_and_prefiltering/MCMC/Trend_prefilter_MC.mod b/tests/observation_trends_and_prefiltering/MCMC/Trend_prefilter_MC.mod
index 2153093ce25163e1371f166c9311353435c7af6b..8b6ad28ecc304598a89e9c300c61b74d561fb272 100644
--- a/tests/observation_trends_and_prefiltering/MCMC/Trend_prefilter_MC.mod
+++ b/tests/observation_trends_and_prefiltering/MCMC/Trend_prefilter_MC.mod
@@ -7,7 +7,7 @@ estimation(order=1,datafile='Trend_prefilter_MC_AR1_trend_data_with_constant',mh
     first_obs=1,smoother,prefilter=1,
     mh_nblocks=1,mh_jscale=1e-4,
     filtered_vars, filter_step_ahead = [1,2,4],
-    mcmc_jumping_covariance='Trend_prefilter_MC_MCMC_jump_covar_prefilter',forecast=100) P_obs Y_obs junk2;
+    mcmc_jumping_covariance='Trend_prefilter_MC_MCMC_jump_covar_prefilter',forecast=100,no_posterior_kernel_density,nograph,sub_draws=100) P_obs Y_obs junk2;
 
 load('Trend_prefilter_MC_AR1_trend_data_with_constant');
 @#include "../Trend_load_data_common.inc" 
diff --git a/tests/observation_trends_and_prefiltering/MCMC/Trend_prefilter_first_obs_MC.mod b/tests/observation_trends_and_prefiltering/MCMC/Trend_prefilter_first_obs_MC.mod
index 79668c5764396350a261c14bd9b11823aa74b71f..669bf6e7d7bfaee9a943c987ccdfc82fd42334ab 100644
--- a/tests/observation_trends_and_prefiltering/MCMC/Trend_prefilter_first_obs_MC.mod
+++ b/tests/observation_trends_and_prefiltering/MCMC/Trend_prefilter_first_obs_MC.mod
@@ -7,7 +7,7 @@ estimation(order=1,datafile='Trend_prefilter_first_obs_MC_AR1_trend_data_with_co
         first_obs=1000,smoother,prefilter=1,
         mh_nblocks=1,mh_jscale=1e-4,
         filtered_vars, filter_step_ahead = [1,2,4],
-        mcmc_jumping_covariance='Trend_prefilter_first_obs_MC_MCMC_jump_covar_prefilter',forecast=100,no_posterior_kernel_density) P_obs Y_obs junk2;
+        mcmc_jumping_covariance='Trend_prefilter_first_obs_MC_MCMC_jump_covar_prefilter',forecast=100,no_posterior_kernel_density,nograph,sub_draws=100) P_obs Y_obs junk2;
 
 load('Trend_prefilter_first_obs_MC_AR1_trend_data_with_constant');
 @#include "../Trend_load_data_common.inc" 
diff --git a/tests/particle/dsge_base2.mod b/tests/particle/dsge_base2.mod
index 473e9366e750f8426650e30a4ae1544dad7ffa68..93e275ef441b27673420ebeb7322c5f43eb1da4c 100644
--- a/tests/particle/dsge_base2.mod
+++ b/tests/particle/dsge_base2.mod
@@ -105,7 +105,7 @@ varobs y l i ;
 %datatomfile('mysample')
 %return;
 
-data(file='./mysample.m',first_obs=801Y,nobs=200); %no measurement errors added in the simulated data
+data(file='./mysample.m',first_obs=801Y,nobs=50); %no measurement errors added in the simulated data
 
 @#if LINEAR_KALMAN
 	estimation(nograph,order=1,mode_compute=8,silent_optimizer,mh_replic=0,additional_optimizer_steps=[8 4],mode_check);
@@ -155,7 +155,7 @@ estimation(order=3,nograph,filter_algorithm=gf,proposal_approximation=montecarlo
 %  estimation(order=3,nograph,number_of_particles=10000,mode_compute=11,mh_replic=0,particle_filter_options=('liu_west_delta',0.9));
   estimation(order=1,posterior_sampling_method='online',posterior_sampler_options=('particles',1000));
   estimation(order=2,posterior_sampling_method='online',posterior_sampler_options=('particles',1000));
-  estimation(order=3,posterior_sampling_method='online',filter_algorithm=nlkf,proposal_approximation=montecarlo,number_of_particles=500,posterior_sampler_options=('particles',500));
+  estimation(order=3,posterior_sampling_method='online',filter_algorithm=nlkf,proposal_approximation=montecarlo,number_of_particles=100,posterior_sampler_options=('particles',100));
 @#endif
 
 @#if MCMC