Speed up testsuite

(manually cherry picked from commit 01e3f513)

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).
 

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
 

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" 
 

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" 

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" 
 

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" 

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" 

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" 

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" 

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" 

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);