Replaced DynareOptions by ParticleOptions and ThreadsOptions.
ParticleOptions is DynareOptions.particles ThreadsOptions is DynareOptions.threadsremove-submodule^2
parent
fbf50da0b8
commit
ff4321e986
|
@ -1,4 +1,4 @@
|
|||
function resampled_particles = resample(particles,weights,DynareOptions)
|
||||
function resampled_particles = resample(particles,weights,ParticleOptions)
|
||||
% Resamples particles.
|
||||
|
||||
%@info:
|
||||
|
@ -54,19 +54,19 @@ function resampled_particles = resample(particles,weights,DynareOptions)
|
|||
defaultmethod = 1; % For residual based method set this variable equal to 0.
|
||||
|
||||
if defaultmethod
|
||||
if DynareOptions.particle.resampling.method.kitagawa
|
||||
if ParticleOptions.resampling.method.kitagawa
|
||||
resampled_particles = traditional_resampling(particles,weights,rand);
|
||||
elseif DynareOptions.particle.resampling.method.stratified
|
||||
elseif ParticleOptions.resampling.method.stratified
|
||||
resampled_particles = traditional_resampling(particles,weights,rand(size(weights)));
|
||||
elseif DynareOptions.particle.resampling.method.smooth
|
||||
elseif ParticleOptions.resampling.method.smooth
|
||||
resampled_particles = multivariate_smooth_resampling(particles,weights);
|
||||
else
|
||||
error('Unknow sampling method!')
|
||||
end
|
||||
else
|
||||
if DynareOptions.particle.resampling.method.kitagawa
|
||||
if ParticleOptions.resampling.method.kitagawa
|
||||
resampled_particles = residual_resampling(particles,weights,rand);
|
||||
elseif DynareOptions.particle.resampling.method.stratified
|
||||
elseif ParticleOptions.resampling.method.stratified
|
||||
resampled_particles = residual_resampling(particles,weights,rand(size(weights)));
|
||||
else
|
||||
error('Unknown sampling method!')
|
||||
|
|
|
@ -1,4 +1,4 @@
|
|||
function [LIK,lik] = sequential_importance_particle_filter(ReducedForm,Y,start,DynareOptions)
|
||||
function [LIK,lik] = sequential_importance_particle_filter(ReducedForm,Y,start,ParticleOptions,ThreadsOptions)
|
||||
|
||||
% Evaluates the likelihood of a nonlinear model with a particle filter (optionally with resampling).
|
||||
|
||||
|
@ -30,7 +30,7 @@ if isempty(start)
|
|||
end
|
||||
|
||||
% Set flag for prunning
|
||||
pruning = DynareOptions.particle.pruning;
|
||||
pruning = ParticleOptions.pruning;
|
||||
|
||||
% Get steady state and mean.
|
||||
steadystate = ReducedForm.steadystate;
|
||||
|
@ -45,7 +45,7 @@ if isempty(init_flag)
|
|||
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;
|
||||
number_of_particles = ParticleOptions.number_of_particles;
|
||||
init_flag = 1;
|
||||
end
|
||||
|
||||
|
@ -99,9 +99,9 @@ for t=1:sample_size
|
|||
epsilon = Q_lower_triangular_cholesky*randn(number_of_structural_innovations,number_of_particles);
|
||||
if pruning
|
||||
yhat_ = bsxfun(@minus,StateVectors_,state_variables_steady_state);
|
||||
[tmp, tmp_] = local_state_space_iteration_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,yhat_,steadystate,DynareOptions.threads.local_state_space_iteration_2);
|
||||
[tmp, tmp_] = local_state_space_iteration_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,yhat_,steadystate,ThreadsOptions.local_state_space_iteration_2);
|
||||
else
|
||||
tmp = local_state_space_iteration_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,DynareOptions.threads.local_state_space_iteration_2);
|
||||
tmp = local_state_space_iteration_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,ThreadsOptions.local_state_space_iteration_2);
|
||||
end
|
||||
PredictedObservedMean = tmp(mf1,:)*transpose(weights);
|
||||
PredictionError = bsxfun(@minus,Y(:,t),tmp(mf1,:));
|
||||
|
@ -117,16 +117,16 @@ for t=1:sample_size
|
|||
wtilde = weights.*exp(lnw-dfac);
|
||||
lik(t) = log(sum(wtilde))+dfac;
|
||||
weights = wtilde/sum(wtilde);
|
||||
if (DynareOptions.particle.resampling.status.generic && neff(weights)<DynareOptions.particle.resampling.threshold*sample_size) || DynareOptions.particle.resampling.status.systematic
|
||||
if (ParticleOptions.resampling.status.generic && neff(weights)<ParticleOptions.resampling.threshold*sample_size) || ParticleOptions.resampling.status.systematic
|
||||
if pruning
|
||||
temp = resample([tmp(mf0,:)' tmp_(mf0,:)'],weights',DynareOptions);
|
||||
temp = resample([tmp(mf0,:)' tmp_(mf0,:)'],weights',ParticleOptions);
|
||||
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',ParticleOptions)';
|
||||
end
|
||||
weights = ones(1,number_of_particles)/number_of_particles;
|
||||
elseif DynareOptions.particle.resampling.status.none
|
||||
elseif ParticleOptions.resampling.status.none
|
||||
StateVectors = tmp(mf0,:);
|
||||
if pruning
|
||||
StateVectors_ = tmp_(mf0,:);
|
||||
|
|
Loading…
Reference in New Issue