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Allow k order approximation in Auxiliary Particle Filter. 

Ref. dynare#1673
rm-particles^2
Stéphane Adjemian (Charybdis) 2019-12-21 10:37:48 +01:00
parent 13d79cb438
commit 8eaccada38
Signed by: stepan
GPG Key ID: 295C1FE89E17EB3C
1 changed files with 35 additions and 44 deletions
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79
src/auxiliary_particle_filter.m
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@ -1,9 +1,9 @@
function [LIK,lik] = auxiliary_particle_filter(ReducedForm,Y,start,ParticleOptions,ThreadsOptions)
function [LIK,lik] = auxiliary_particle_filter(ReducedForm,Y,start,ParticleOptions,ThreadsOptions, DynareOptions, Model)
% Evaluates the likelihood of a nonlinear model with the auxiliary particle filter
% allowing eventually resampling.
%
% Copyright (C) 2011-2017 Dynare Team
% Copyright (C) 2011-2019 Dynare Team
%
% This file is part of Dynare (particles module).
%
@ -20,9 +20,6 @@ function [LIK,lik] = auxiliary_particle_filter(ReducedForm,Y,start,ParticleOptio
% 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
% Set default
if isempty(start)
start = 1;
@ -36,24 +33,24 @@ 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;
sample_size = size(Y,2);
number_of_observed_variables = length(mf1);
number_of_structural_innovations = length(ReducedForm.Q);
number_of_particles = ParticleOptions.number_of_particles;
init_flag = 1;
end
mf0 = ReducedForm.mf0;
mf1 = ReducedForm.mf1;
sample_size = size(Y,2);
number_of_observed_variables = length(mf1);
number_of_structural_innovations = length(ReducedForm.Q);
number_of_particles = ParticleOptions.number_of_particles;
% 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;
if ReducedForm.use_k_order_solver
dr = ReducedForm.dr;
else
% 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;
end
% Get covariance matrices
Q = ReducedForm.Q;
@ -81,19 +78,6 @@ if pruning
StateVectors_ = StateVectors;
end
% Uncomment for building the mean average predictions based on a sparse
% grids of structural shocks. Otherwise, all shocks are set to 0 in the
% prediction.
% if ParticleOptions.proposal_approximation.cubature
% [nodes,nodes_weights] = spherical_radial_sigma_points(number_of_structural_innovations) ;
% nodes_weights = ones(size(nodes,1),1)*nodes_weights ;
% elseif ParticleOptions.proposal_approximation.unscented
% [nodes,nodes_weights,nodes_weights_c] = unscented_sigma_points(number_of_structural_innovations,ParticleOptions);
% else
% error('Estimation: This approximation for the proposal is not implemented or unknown!')
% end
% nodes = (Q_lower_triangular_cholesky*(nodes'))' ;
nodes = zeros(1,number_of_structural_innovations) ;
nodes_weights = ones(number_of_structural_innovations,1) ;
@ -109,15 +93,19 @@ for t=1:sample_size
tmp_ = tmp_ + nodes_weights(i)*tmp1_ ;
end
else
tmp = 0 ;
for i=1:size(nodes)
tmp = tmp + nodes_weights(i)*local_state_space_iteration_2(yhat,nodes(i,:)'*ones(1,number_of_particles),ghx,ghu,constant,ghxx,ghuu,ghxu,ThreadsOptions.local_state_space_iteration_2);
if ReducedForm.use_k_order_solver
tmp = 0;
for i=1:size(nodes)
tmp = tmp + nodes_weights(i)*local_state_space_iteration_k(yhat, nodes(i,:)'*ones(1,number_of_particles), dr, Model, DynareOptions);
end
else
tmp = 0;
for i=1:size(nodes)
tmp = tmp + nodes_weights(i)*local_state_space_iteration_2(yhat,nodes(i,:)'*ones(1,number_of_particles),ghx,ghu,constant,ghxx,ghuu,ghxu,ThreadsOptions.local_state_space_iteration_2);
end
end
end
PredictionError = bsxfun(@minus,Y(:,t),tmp(mf1,:));
%tau_tilde = weights.*(exp(-.5*(const_lik+sum(PredictionError.*(H\PredictionError),1))) + 1e-99) ;
% Replace Gaussian density with a Student density with 3 degrees of
% freedom for fat tails.
z = sum(PredictionError.*(H\PredictionError),1) ;
tau_tilde = weights.*(tpdf(z,3*ones(size(z)))+1e-99) ;
tau_tilde = tau_tilde/sum(tau_tilde) ;
@ -132,7 +120,11 @@ for t=1:sample_size
[tmp, tmp_] = local_state_space_iteration_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,yhat_,steadystate,ThreadsOptions.local_state_space_iteration_2);
StateVectors_ = tmp_(mf0,:);
else
tmp = local_state_space_iteration_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,ThreadsOptions.local_state_space_iteration_2);
if ReducedForm.use_k_order_solver
tmp = local_state_space_iteration_k(yhat, epsilon, dr, Model, DynareOptions);
else
tmp = local_state_space_iteration_2(yhat, epsilon, ghx, ghu, constant, ghxx, ghuu, ghxu, ThreadsOptions.local_state_space_iteration_2);
end
end
StateVectors = tmp(mf0,:);
PredictionError = bsxfun(@minus,Y(:,t),tmp(mf1,:));
@ -151,5 +143,4 @@ for t=1:sample_size
end
end
%plot(lik) ;
LIK = -sum(lik(start:end));
LIK = -sum(lik(start:end));