modification in the call of resampling: only one call now

matlab/particle/auxiliary_initialization.m

+function initial_distribution = auxiliary_initialization(ReducedForm,Y,start,DynareOptions)
+% Evaluates the likelihood of a nonlinear model with a particle filter allowing eventually resampling.
+%
+% INPUTS
+%    ReducedForm     [structure] Matlab's structure describing the reduced form model.
+%                                       ReducedForm.measurement.H   [double]   (pp x pp) variance matrix of measurement errors.
+%                                       ReducedForm.state.Q         [double]   (qq x qq) variance matrix of state errors.
+%                                       ReducedForm.state.dr        [structure] output of resol.m.
+%    Y                      [double]    pp*smpl matrix of (detrended) data, where pp is the maximum number of observed variables.
+%    start                  [integer]   scalar, likelihood evaluation starts at 'start'.
+%    mf                     [integer]   pp*1 vector of indices.
+%    number_of_particles    [integer]   scalar.
+%
+% OUTPUTS
+%    LIK        [double]    scalar, likelihood
+%    lik        [double]    vector, density of observations in each period.
+%
+% REFERENCES
+%
+% NOTES
+%   The vector "lik" is used to evaluate the jacobian of the likelihood.
+
+% Copyright (C) 2011, 2012 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+persistent init_flag mf0 mf1 number_of_particles
+persistent number_of_observed_variables number_of_structural_innovations
+
+% Set default
+if isempty(start)
+    start = 1;
+end
+
+% Set flag for prunning
+%pruning = DynareOptions.particle.pruning;
+
+% Get steady state and mean.
+%steadystate = ReducedForm.steadystate;
+constant = ReducedForm.constant;
+state_variables_steady_state = ReducedForm.state_variables_steady_state;
+
+% Set persistent variables.
+if isempty(init_flag)
+    mf0 = ReducedForm.mf0;
+    mf1 = ReducedForm.mf1;
+    number_of_observed_variables = length(mf1);
+    number_of_structural_innovations = length(ReducedForm.Q);
+    number_of_particles = DynareOptions.particle.number_of_particles;
+    init_flag = 1;
+end
+
+% Set local state space model (first order approximation).
+ghx  = ReducedForm.ghx;
+ghu  = ReducedForm.ghu;
+% Set local state space model (second order approximation).
+ghxx = ReducedForm.ghxx;
+ghuu = ReducedForm.ghuu;
+ghxu = ReducedForm.ghxu;
+
+% Get covariance matrices
+Q = ReducedForm.Q;
+H = ReducedForm.H;
+if isempty(H)
+    H = 0;
+end
+
+% Get initial condition for the state vector.
+StateVectorMean = ReducedForm.StateVectorMean;
+StateVectorVarianceSquareRoot = reduced_rank_cholesky(ReducedForm.StateVectorVariance)';
+state_variance_rank = size(StateVectorVarianceSquareRoot,2);
+%Q_lower_triangular_cholesky = chol(Q)';
+%if pruning
+%    StateVectorMean_ = StateVectorMean;
+%    StateVectorVarianceSquareRoot_ = StateVectorVarianceSquareRoot;
+%end
+
+% Set seed for randn().
+set_dynare_seed('default');
+
+% Initialization of the likelihood.
+const_lik = log(2*pi)*number_of_observed_variables;
+
+% Initialization of the weights across particles.
+weights = ones(1,number_of_particles)/number_of_particles ;
+StateVectors = bsxfun(@plus,StateVectorVarianceSquareRoot*randn(state_variance_rank,number_of_particles),StateVectorMean);
+%if pruning
+%    StateVectors_ = StateVectors;
+%end
+yhat = bsxfun(@minus,StateVectors,state_variables_steady_state);
+%if pruning
+%    yhat_ = bsxfun(@minus,StateVectors_,state_variables_steady_state);
+%    [tmp, tmp_] = local_state_space_iteration_2(yhat,zeros(number_of_structural_innovations,number_of_particles),ghx,ghu,constant,ghxx,ghuu,ghxu,yhat_,steadystate,DynareOptions.threads.local_state_space_iteration_2);
+%else
+    tmp = local_state_space_iteration_2(yhat,zeros(number_of_structural_innovations,number_of_particles),ghx,ghu,constant,ghxx,ghuu,ghxu,DynareOptions.threads.local_state_space_iteration_2);
+%end
+PredictedObservedMean = weights*(tmp(mf1,:)');
+PredictionError = bsxfun(@minus,Y(:,t),tmp(mf1,:));
+dPredictedObservedMean = bsxfun(@minus,tmp(mf1,:),PredictedObservedMean');
+PredictedObservedVariance = bsxfun(@times,weights,dPredictedObservedMean)*dPredictedObservedMean' + H;
+wtilde = exp(-.5*(const_lik+log(det(PredictedObservedVariance))+sum(PredictionError.*(PredictedObservedVariance\PredictionError),1))) ;
+tau_tilde = weights.*wtilde ;
+tau_tilde = tau_tilde/sum(tau_tilde);
+initial_distribution = resample(StateVectors',tau_tilde',DynareOptions)' ;
\ No newline at end of file

matlab/particle/auxiliary_particle_filter.m

@@ -37,7 +37,7 @@ function [LIK,lik] = auxiliary_particle_filter(ReducedForm,Y,start,DynareOptions
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 persistent init_flag mf0 mf1 number_of_particles
-persistent sample_size number_of_observed_variables number_of_structural_innovations
+persistent sample_size number_of_state_variables number_of_observed_variables number_of_structural_innovations
 
 % Set default
 if isempty(start)
@@ -57,6 +57,7 @@ if isempty(init_flag)
     mf0 = ReducedForm.mf0;
     mf1 = ReducedForm.mf1;
     sample_size = size(Y,2);
+    number_of_state_variables = length(mf0);
     number_of_observed_variables = length(mf1);
     number_of_structural_innovations = length(ReducedForm.Q);
     number_of_particles = DynareOptions.particle.number_of_particles;
@@ -121,12 +122,25 @@ for t=1:sample_size
     %var_wtilde = var_wtilde'*var_wtilde/(number_of_particles-1) ;
     lik(t) = log(sum_tau_tilde) ; %+ .5*var_wtilde/(number_of_particles*(sum_tau_tilde*sum_tau_tilde)) ;
     tau_tilde = tau_tilde/sum_tau_tilde;
-    indx_resmpl = resample(tau_tilde,DynareOptions.particle.resampling.method1,DynareOptions.particle.resampling.method2);
-    yhat = yhat(:,indx_resmpl);
-    wtilde = wtilde(indx_resmpl);
+    if pruning
+        temp = resample([yhat' yhat_'],tau_tilde',DynareOptions);
+        yhat = temp(:,1:number_of_state_variables)' ;
+        yhat_ = temp(:,number_of_state_variables+1:2*number_of_state_variables)' ;
+    else
+        yhat = resample(yhat',tau_tilde',DynareOptions)' ;
+    end
+    if pruning
+        [tmp, tmp_] = local_state_space_iteration_2(yhat,zeros(number_of_structural_innovations,number_of_particles),ghx,ghu,constant,ghxx,ghuu,ghxu,yhat_,steadystate,DynareOptions.threads.local_state_space_iteration_2);
+    else
+        tmp = local_state_space_iteration_2(yhat,zeros(number_of_structural_innovations,number_of_particles),ghx,ghu,constant,ghxx,ghuu,ghxu,DynareOptions.threads.local_state_space_iteration_2);
+    end
+    PredictedObservedMean = weights*(tmp(mf1,:)');
+    PredictionError = bsxfun(@minus,Y(:,t),tmp(mf1,:));
+    dPredictedObservedMean = bsxfun(@minus,tmp(mf1,:),PredictedObservedMean');
+    PredictedObservedVariance = bsxfun(@times,weights,dPredictedObservedMean)*dPredictedObservedMean' +H;
+    wtilde = exp(-.5*(const_lik+log(det(PredictedObservedVariance))+sum(PredictionError.*(PredictedObservedVariance\PredictionError),1))) ;
     epsilon = Q_lower_triangular_cholesky*randn(number_of_structural_innovations,number_of_particles);
     if pruning
-        yhat_ = yhat_(:,indx_resmpl);
         [tmp, tmp_] = local_state_space_iteration_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,yhat_,steadystate,DynareOptions.threads.local_state_space_iteration_2);
         StateVectors_ = tmp_(mf0,:);
     else

matlab/particle/gaussian_filter.m

@@ -149,12 +149,7 @@ for t=1:sample_size
         if (Neff<.5*sample_size && strcmpi(DynareOptions.particle.resampling.status,'generic')) || ... 
                                    strcmpi(DynareOptions.particle.resampling.status,'systematic')
             ks = ks + 1 ;
-            StateParticles = StateParticles(:,resample(SampleWeights',DynareOptions.particle.resampling.method1,DynareOptions.particle.resampling.method2)) ; 
-            StateVectorMean = mean(StateParticles,2) ;
-            StateVectorVarianceSquareRoot = reduced_rank_cholesky( (StateParticles*StateParticles')/(number_of_particles-1) - StateVectorMean*(StateVectorMean') )';
-            SampleWeights = 1/number_of_particles ;
-        elseif strcmp(DynareOptions.particle.resampling.status,'smoothed')
-            StateParticles = multivariate_smooth_resampling(SampleWeights,StateParticles',number_of_particles,DynareOptions.particle.resampling.number_of_partitions)';
+            StateParticles = resample(StateParticles',SampleWeights,DynareOptions)' ; 
             StateVectorMean = mean(StateParticles,2) ;
             StateVectorVarianceSquareRoot = reduced_rank_cholesky( (StateParticles*StateParticles')/(number_of_particles-1) - StateVectorMean*(StateVectorMean') )';
             SampleWeights = 1/number_of_particles ;

matlab/particle/sequential_importance_particle_filter.m

@@ -60,7 +60,7 @@ function [LIK,lik] = sequential_importance_particle_filter(ReducedForm,Y,start,D
 
 persistent init_flag
 persistent mf0 mf1
-persistent number_of_particles
+persistent number_of_particles number_of_state_variables
 persistent sample_size number_of_observed_variables number_of_structural_innovations
 
 % Set default value for start
@@ -81,6 +81,7 @@ if isempty(init_flag)
     mf0 = ReducedForm.mf0;
     mf1 = ReducedForm.mf1;
     sample_size = size(Y,2);
+    number_of_state_variables = length(mf0);
     number_of_observed_variables = length(mf1);
     number_of_structural_innovations = length(ReducedForm.Q);
     number_of_particles = DynareOptions.particle.number_of_particles;
@@ -151,17 +152,14 @@ for t=1:sample_size
     wtilde = weights.*exp(lnw-dfac);
     lik(t) = log(sum(wtilde))+dfac;
     weights = wtilde/sum(wtilde);
-    if (strcmp(DynareOptions.particle.resampling.status,'generic') && neff(weights)<DynareOptions.particle.resampling.neff_threshold*sample_size ) || strcmp(DynareOptions.particle.resampling.status,'systematic')
-        idx = resample(weights,DynareOptions.particle.resampling.method1,DynareOptions.particle.resampling.method2);
-        StateVectors = tmp(mf0,idx);
+    if (strcmp(DynareOptions.particle.resampling.status,'generic') && neff(weights)<DynareOptions.particle.resampling.neff_threshold*sample_size ) || ...
+        strcmp(DynareOptions.particle.resampling.status,'systematic')
         if pruning
-            StateVectors_ = tmp_(mf0,idx);
-        end
-        weights = ones(1,number_of_particles)/number_of_particles;
-    elseif strcmp(DynareOptions.particle.resampling.status,'smoothed')
-        StateVectors = multivariate_smooth_resampling(weights',tmp(mf0,:)',number_of_particles,DynareOptions.particle.resampling.number_of_partitions)';
-        if pruning
-            StateVectors_ = multivariate_smooth_resampling(weights',tmp_(mf0,:)',number_of_particles,DynareOptions.particle.resampling.number_of_partitions)';
+            temp = resample([tmp(mf0,:)' tmp_(mf0,:)'],weights,DynareOptions);
+            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)';
         end
         weights = ones(1,number_of_particles)/number_of_particles;
     elseif strcmp(DynareOptions.particle.resampling.status,'none')