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Correct the auxiliary filter part.

remove-submodule^2
Frédéric Karamé 2015-12-04 12:42:42 +01:00
parent 086396934c
commit eb975a621b
1 changed files with 19 additions and 20 deletions
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39
nonlinear-filters/src/online_auxiliary_filter.m
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@ -43,7 +43,7 @@ persistent start_param sample_size number_of_observed_variables number_of_struct
% Set seed for randn().
set_dynare_seed('default') ;
pruning = DynareOptions.particle.pruning;
second_resample = 1 ;
second_resample = 0 ;
variance_update = 1 ;
% initialization of state particles
@ -122,7 +122,7 @@ for t=1:sample_size
chol_sigma_bar = chol(h_square*sigma_bar)' ;
end
% Prediction (without shocks)
wtilde = zeros(1,number_of_particles) ;
tau_tilde = zeros(1,number_of_particles) ;
for i=1:number_of_particles
% model resolution
[ys,trend_coeff,exit_flag,info,Model,DynareOptions,BayesInfo,DynareResults,ReducedForm] = ...
@ -145,22 +145,23 @@ for t=1:sample_size
tmp = local_state_space_iteration_2(yhat,zeros(number_of_structural_innovations,1),ghx,ghu,constant,ghxx,ghuu,ghxu,DynareOptions.threads.local_state_space_iteration_2);
end
PredictionError = bsxfun(@minus,Y(t,:)',tmp(mf1,:));
wtilde(i) = exp(-.5*(const_lik+log(det(ReducedForm.H))+sum(PredictionError.*(ReducedForm.H\PredictionError),1))) ;
% Replace Gaussian density with a Student density with 3 degrees of
% freedom for fat tails.
z = sum(PredictionError.*(ReducedForm.H\PredictionError),1) ;
tau_tilde(i) = weights(i).*(tpdf(z,3*ones(size(z)))+1e-99) ;
%tau_tilde(i) = weights(i).*exp(-.5*(const_lik+log(det(ReducedForm.H))+sum(PredictionError.*(ReducedForm.H\PredictionError),1))) ;
end
% unormalized weights and observation likelihood contribution
tau_tilde = weights.*wtilde ;
sum_tau_tilde = sum(tau_tilde) ;
% particles selection
tau_tilde = tau_tilde/sum_tau_tilde ;
tau_tilde = tau_tilde/sum(tau_tilde) ;
indx = resample(0,tau_tilde',DynareOptions.particle);
StateVectors = StateVectors(:,indx) ;
if pruning
StateVectors_ = StateVectors_(:,indx) ;
end
xparam = bsxfun(@plus,(1-small_a).*m_bar,small_a.*xparam(:,indx)) ;
wtilde = wtilde(indx) ;
w_stage1 = weights(indx)./tau_tilde(indx) ;
% draw in the new distributions
lnw = zeros(1,number_of_particles) ;
wtilde = zeros(1,number_of_particles) ;
i = 1 ;
while i<=number_of_particles
candidate = xparam(:,i) + chol_sigma_bar*randn(number_of_parameters,1) ;
@ -191,18 +192,16 @@ for t=1:sample_size
end
StateVectors(:,i) = tmp(mf0,:) ;
PredictionError = bsxfun(@minus,Y(t,:)',tmp(mf1,:));
lnw(i) = exp(-.5*(const_lik+log(det(ReducedForm.H))+sum(PredictionError.*(ReducedForm.H\PredictionError),1)));
wtilde(i) = w_stage1(i)*exp(-.5*(const_lik+log(det(ReducedForm.H))+sum(PredictionError.*(ReducedForm.H\PredictionError),1)));
i = i+1 ;
end
end
% importance ratio
wtilde = lnw./wtilde ;
% normalization
weights = wtilde/sum(wtilde);
if (variance_update==1) && (neff(weights)<DynareOptions.particle.resampling.threshold*sample_size)
variance_update = 0 ;
end
% final resampling (advised)
% final resampling (not advised)
if second_resample==1
indx = resample(0,weights,DynareOptions.particle);
StateVectors = StateVectors(:,indx) ;
@ -234,16 +233,16 @@ for t=1:sample_size
pass2=1 ;
pass3=1 ;
for j=1:number_of_particles
if cumulated_weights(j)>0.025 && pass1==1
lb95_xparam(i,t) = (temp(j-1,1)+temp(j,1))/2 ;
if cumulated_weights(j)>=0.025 && pass1==1
lb95_xparam(i,t) = temp(j,1) ;
pass1 = 2 ;
end
if cumulated_weights(j)>0.5 && pass2==1
median_xparam(i,t) = (temp(j-1,1)+temp(j,1))/2 ;
if cumulated_weights(j)>=0.5 && pass2==1
median_xparam(i,t) = temp(j,1) ;
pass2 = 2 ;
end
if cumulated_weights(j)>0.975 && pass3==1
ub95_xparam(i,t) = (temp(j-1,1)+temp(j,1))/2 ;
if cumulated_weights(j)>=0.975 && pass3==1
ub95_xparam(i,t) = temp(j,1) ;
pass3 = 2 ;
end
end
@ -368,4 +367,4 @@ for plt = 1:nbplt,
fprintf(fidTeX,' \n');
end
end