Bug fixes.

matlab/particle/conditional_particle_filter.m

@@ -37,7 +37,7 @@ function [LIK,lik] = conditional_particle_filter(ReducedForm,Y,start,DynareOptio
 %
 % NOTES
 %   The vector "lik" is used to evaluate the jacobian of the likelihood.
-% Copyright (C) 2009-2013 Dynare Team
+% Copyright (C) 2009-2010 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -116,7 +116,7 @@ for t=1:sample_size
     if (strcmp(DynareOptions.particle.resampling.status,'generic') && neff(SampleWeights)<DynareOptions.particle.resampling.neff_threshold*sample_size ) || ...
         strcmp(DynareOptions.particle.resampling.status,'systematic')
         ks = ks + 1 ;
-        StateParticles = resample(StateParticles',SampleWeights,DynareOptions)';
+        StateParticles = resample(StateParticles',SampleWeights',DynareOptions)';
         SampleWeights = ones(1,number_of_particles)/number_of_particles ;
     end
 end

matlab/particle/gaussian_densities.m

@@ -19,7 +19,7 @@ function IncrementalWeights = gaussian_densities(obs,mut_t,sqr_Pss_t_t,st_t_1,sq
 %
 % NOTES
 %   The vector "lik" is used to evaluate the jacobian of the likelihood.
-% Copyright (C) 2009-2012 Dynare Team
+% Copyright (C) 2009-2010 Dynare Team
 %
 % This file is part of Dynare.
 %

matlab/particle/multivariate_smooth_resampling.m

@@ -59,7 +59,6 @@ function new_particles = multivariate_smooth_resampling(particles,weights)
 %           stephane DOT adjemian AT univ DASH lemans DOT fr
 
 number_of_particles = length(weights);
-weights = weights' ;
 number_of_states = size(particles,2);
 [P,D] = eig(particles'*(bsxfun(@times,1/number_of_particles,particles))) ;
 D = diag(D) ;

matlab/particle/online_auxiliary_filter.m

@@ -41,7 +41,6 @@ persistent Y init_flag mf0 mf1 bounds number_of_particles number_of_parameters l
 persistent start_param sample_size number_of_observed_variables number_of_structural_innovations
 
 % Set seed for randn().
-%set_dynare_seed('mt19937ar',1234) ;
 set_dynare_seed('default') ;
 pruning = DynareOptions.particle.pruning;
 second_resample = 1 ;
@@ -62,11 +61,10 @@ if isempty(init_flag)
     number_of_observed_variables = length(mf1);
     number_of_structural_innovations = length(ReducedForm.Q);
     liu_west_delta = DynareOptions.particle.liu_west_delta ;
-    liu_west_chol_sigma_bar = DynareOptions.particle.liu_west_chol_sigma_bar*eye(number_of_parameters) ;
-    %start_param = xparam1 ; 
-    % Conditions initiales 
-    %liu_west_chol_sigma_bar = bsxfun(@times,eye(number_of_parameters),BayesInfo.p2) ; 
-    start_param = BayesInfo.p1 ;
+    %liu_west_chol_sigma_bar = DynareOptions.particle.liu_west_chol_sigma_bar*eye(number_of_parameters) ;
+    start_param = xparam1 ; 
+    %liu_west_chol_sigma_bar = sqrt(bsxfun(@times,eye(number_of_parameters),BayesInfo.p2)) ; 
+    %start_param = BayesInfo.p1 ;
     bounds = [BayesInfo.lb BayesInfo.ub] ;
     init_flag = 1;
 end
@@ -87,11 +85,12 @@ small_a = sqrt(1-h_square) ;
 
 % Initialization of parameter particles 
 xparam = zeros(number_of_parameters,number_of_particles) ;
-stderr = sqrt(bsxfun(@power,bounds(:,2)+bounds(:,1),2)/12)/1000 ;
+stderr = sqrt(bsxfun(@power,bounds(:,2)+bounds(:,1),2)/12)/100 ;
+stderr = sqrt(bsxfun(@power,bounds(:,2)+bounds(:,1),2)/12)/50 ;
 i = 1 ;
 while i<=number_of_particles
-    candidate = start_param + 10*liu_west_chol_sigma_bar*randn(number_of_parameters,1) ;
-    %candidate = start_param + bsxfun(@times,stderr,randn(number_of_parameters,1)) ;
+    %candidate = start_param + .001*liu_west_chol_sigma_bar*randn(number_of_parameters,1) ;
+    candidate = start_param + bsxfun(@times,stderr,randn(number_of_parameters,1)) ;
     if all(candidate(:) >= bounds(:,1)) && all(candidate(:) <= bounds(:,2))
         xparam(:,i) = candidate(:) ;
         i = i+1 ;
@@ -113,7 +112,7 @@ ub95_xparam = zeros(number_of_parameters,sample_size) ;
 
 %% The Online filter 
 for t=1:sample_size
-    disp(t) 
+    disp(t)
     % Moments of parameters particles distribution 
     m_bar = xparam*(weights') ;
     temp = bsxfun(@minus,xparam,m_bar) ;
@@ -210,7 +209,7 @@ for t=1:sample_size
     if (variance_update==1) && (neff(weights)<DynareOptions.particle.resampling.neff_threshold*sample_size)
         variance_update = 0 ;
     end
-        % final resampling (advised)
+    % final resampling (advised)
     if second_resample==1 
         indx = index_resample(0,weights,DynareOptions);
         StateVectors = StateVectors(:,indx) ;
@@ -259,7 +258,8 @@ for t=1:sample_size
     end
     disp([lb95_xparam(:,t) mean_xparam(:,t) ub95_xparam(:,t)])
 end
-xparam = mean_xparam(:,sample_size) ; 
+distrib_param = xparam ;
+xparam = mean_xparam(:,sample_size) ;
 std_param = std_xparam(:,sample_size) ;
 lb_95 = lb95_xparam(:,sample_size) ;
 ub_95 = ub95_xparam(:,sample_size) ;
@@ -345,8 +345,8 @@ for plt = 1:nbplt,
                 TeXNAMES = char(TeXNAMES,texname);
             end
         end
-        optimal_bandwidth = mh_optimal_bandwidth(xparam(kk,:)',number_of_particles,bandwidth,kernel_function);
-        [density(:,1),density(:,2)] = kernel_density_estimate(xparam(kk,:)',number_of_grid_points,...
+        optimal_bandwidth = mh_optimal_bandwidth(distrib_param(kk,:)',number_of_particles,bandwidth,kernel_function);
+        [density(:,1),density(:,2)] = kernel_density_estimate(distrib_param(kk,:)',number_of_grid_points,...
                                                           number_of_particles,optimal_bandwidth,kernel_function);
         plot(density(:,1),density(:,2));
         hold on
@@ -370,3 +370,4 @@ for plt = 1:nbplt,
         fprintf(fidTeX,' \n');
     end
 end    
+    
\ No newline at end of file

matlab/particle/resample.m

@@ -54,6 +54,7 @@ function resampled_particles = resample(particles,weights,DynareOptions)
 % AUTHOR(S) frederic DOT karame AT univ DASH evry DOT fr
 %           stephane DOT adjemian AT univ DASH lemans DOT fr
 
+
 switch DynareOptions.particle.resampling.method1
   case 'residual'
     if strcmpi(DynareOptions.particle.resampling.method2,'kitagawa')

matlab/particle/sequential_importance_particle_filter.m

@@ -161,7 +161,7 @@ for t=1:sample_size
             StateVectors = temp(:,1:number_of_state_variables)' ;
             StateVectors_ = temp(:,number_of_state_variables+1:2*number_of_state_variables)';
         else
-            StateVectors = resample(tmp(mf0,:)',weights,DynareOptions)';
+            StateVectors = resample(tmp(mf0,:)',weights',DynareOptions)';
         end
         weights = ones(1,number_of_particles)/number_of_particles;
     elseif strcmp(DynareOptions.particle.resampling.status,'none')

matlab/particle/solve_model_for_online_filter.m

@@ -168,8 +168,7 @@ end
 offset = EstimatedParameters.nvx;
 if EstimatedParameters.nvn
     for i=1:EstimatedParameters.nvn
-        k = EstimatedParameters.var_endo(i,1);
-        H(k,k) = xparam1(i+offset)*xparam1(i+offset);
+        H(i,i) = xparam1(i+offset)*xparam1(i+offset);
     end
     offset = offset+EstimatedParameters.nvn;
 else

tests/particle/dsge_base2.mod

@@ -35,12 +35,12 @@ steady;
 //disp(oo_.mean) ;
 
 estimated_params;
-alp, uniform_pdf,,, 0.0001, 1;
-bet, uniform_pdf,,, 0.75, 0.999;
+alp, uniform_pdf,,, 0.0001, 0.5;
+bet, uniform_pdf,,, 0.0001, 0.99;
 tet, uniform_pdf,,, 0.0001, 1;
 tau, uniform_pdf,,, 0.0001, 100;
 delt, uniform_pdf,,, 0.0001, 0.05;
-rho, uniform_pdf,,, 0.0001, 0.999;
+rho, uniform_pdf,,, 0.8, 0.99;
 stderr e_a, uniform_pdf,,, 0.00001, 0.1;
 stderr y, uniform_pdf,,, 0.00001, 0.1;
 stderr l, uniform_pdf,,, 0.00001, 0.1;
@@ -49,11 +49,11 @@ end;
 
 estimated_params_init;
 alp, 0.4;
-bet, 0.99;
+bet, 0.97;
 tet, 0.357 ;
 tau, 50;
 delt, 0.02;
-rho, 0.95;
+rho, 0.9 ;
 stderr e_a, .035;
 stderr y, .0175;//.00158;
 stderr l, .00312;//.0011;
@@ -66,28 +66,42 @@ varobs y l i ;
 //options_.gstep(2) = .1;
 
 options_.particle.status = 1;
-options_.particle.algorithm = 'sequential_importance_particle_filter';
 options_.particle.initialization = 1;
-options_.particle.pruning = 1;
-options_.particle.number_of_particles = 2000;
+options_.particle.pruning = 0;
+options_.particle.number_of_particles = 1000 ;
 options_.particle.resampling.status = 'systematic';
-options_.particle.resampling.neff_threshold = .1;
+
+//options_.particle.resampling.method1 = 'traditional' ;
+//options_.particle.resampling.method1 = 'residual' ;
+options_.particle.resampling.method1 = 'smooth' ;
+
+options_.particle.reampling.method2 = 'kitagawa' ;
+//options_.particle.resampling.method2 = 'stratified' ;
+
+options_.particle.resampling.number_of_partitions = 1;
+options_.particle.resampling.neff_threshold = .5;
 
 set_dynare_threads('local_state_space_iteration_2',3);
 
 options_.particle.algorithm = 'sequential_importance_particle_filter';
 //options_.particle.algorithm = 'auxiliary_particle_filter';
 //options_.particle.algorithm = 'gaussian_mixture_filter';
-//options_.particle.algorithm = 'each_gaussian_filter';
+//options_.particle.algorithm = 'conditional_particle_filter';
 //options_.particle.algorithm = 'gaussian_filter';
 
 //options_.particle.IS_approximation_method = 'quadrature' ;
-//options_.particle.IS_approximation_method = 'cubature' ;
+options_.particle.IS_approximation_method = 'cubature' ;
 //options_.particle.IS_approximation_method = 'unscented' ;
 
 //options_.particle.approximation_method = 'quadrature' ;
-//options_.particle.approximation_method = 'cubature' ;
+options_.particle.approximation_method = 'cubature' ;
 //options_.particle.approximation_method = 'unscented' ;
 //options_.particle.approximation_method = 'MonteCarlo' ;
 
-estimation(datafile=data_risky_perturb2,nograph,order=2,nobs=100,mh_replic=0,mode_compute=7,mode_check);
+//options_.mh_posterior_mode_estimation=1 ;
+
+// online
+options_.particle.liu_west_delta = 0.9 ;
+options_.mode_check_node_number = 250 ;
+
+estimation(datafile=data_risky_perturb3,nograph,order=2,nobs=100,mh_replic=0,mode_compute=7,mode_check);