Replaced DynareOptions by ParticleOptions and ThreadsOptions.
parent
82e4d66bbf
commit
f02cd5a275
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@ -1,4 +1,4 @@
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function initial_distribution = auxiliary_initialization(ReducedForm,Y,start,DynareOptions)
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function initial_distribution = auxiliary_initialization(ReducedForm,Y,start,ParticleOptions,ThreadsOptions)
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% Evaluates the likelihood of a nonlinear model with a particle filter allowing eventually resampling.
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%
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% INPUTS
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@ -45,7 +45,7 @@ if isempty(start)
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end
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% Set flag for prunning
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%pruning = DynareOptions.particle.pruning;
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%pruning = ParticleOptions.pruning;
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% Get steady state and mean.
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%steadystate = ReducedForm.steadystate;
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@ -58,7 +58,7 @@ if isempty(init_flag)
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mf1 = ReducedForm.mf1;
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number_of_observed_variables = length(mf1);
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number_of_structural_innovations = length(ReducedForm.Q);
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number_of_particles = DynareOptions.particle.number_of_particles;
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number_of_particles = ParticleOptions.number_of_particles;
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init_flag = 1;
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end
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@ -102,9 +102,9 @@ StateVectors = bsxfun(@plus,StateVectorVarianceSquareRoot*randn(state_variance_r
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yhat = bsxfun(@minus,StateVectors,state_variables_steady_state);
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%if pruning
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% yhat_ = bsxfun(@minus,StateVectors_,state_variables_steady_state);
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% [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);
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% [tmp, tmp_] = local_state_space_iteration_2(yhat,zeros(number_of_structural_innovations,number_of_particles),ghx,ghu,constant,ghxx,ghuu,ghxu,yhat_,steadystate,ThreadsOptions.local_state_space_iteration_2);
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%else
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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);
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tmp = local_state_space_iteration_2(yhat,zeros(number_of_structural_innovations,number_of_particles),ghx,ghu,constant,ghxx,ghuu,ghxu,ThreadsOptions.local_state_space_iteration_2);
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%end
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PredictedObservedMean = weights*(tmp(mf1,:)');
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PredictionError = bsxfun(@minus,Y(:,t),tmp(mf1,:));
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@ -113,4 +113,4 @@ PredictedObservedVariance = bsxfun(@times,weights,dPredictedObservedMean)*dPredi
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wtilde = exp(-.5*(const_lik+log(det(PredictedObservedVariance))+sum(PredictionError.*(PredictedObservedVariance\PredictionError),1))) ;
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tau_tilde = weights.*wtilde ;
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tau_tilde = tau_tilde/sum(tau_tilde);
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initial_distribution = resample(StateVectors',tau_tilde',DynareOptions)' ;
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initial_distribution = resample(StateVectors',tau_tilde',ParticleOptions)' ;
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