Allow k order approximation in Auxiliary Particle Filter.
Ref. dynare#1673remove-submodule^2
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@ -1,9 +1,9 @@
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function [LIK,lik] = auxiliary_particle_filter(ReducedForm,Y,start,ParticleOptions,ThreadsOptions)
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function [LIK,lik] = auxiliary_particle_filter(ReducedForm,Y,start,ParticleOptions,ThreadsOptions, DynareOptions, Model)
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% Evaluates the likelihood of a nonlinear model with the auxiliary particle filter
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% allowing eventually resampling.
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%
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% Copyright (C) 2011-2017 Dynare Team
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% Copyright (C) 2011-2019 Dynare Team
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%
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% This file is part of Dynare (particles module).
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%
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@ -20,9 +20,6 @@ function [LIK,lik] = auxiliary_particle_filter(ReducedForm,Y,start,ParticleOptio
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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persistent init_flag mf0 mf1 number_of_particles
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persistent sample_size number_of_observed_variables number_of_structural_innovations
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% Set default
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if isempty(start)
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start = 1;
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@ -36,24 +33,24 @@ steadystate = ReducedForm.steadystate;
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constant = ReducedForm.constant;
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state_variables_steady_state = ReducedForm.state_variables_steady_state;
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% Set persistent variables.
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if isempty(init_flag)
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mf0 = ReducedForm.mf0;
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mf1 = ReducedForm.mf1;
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sample_size = size(Y,2);
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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 = ParticleOptions.number_of_particles;
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init_flag = 1;
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end
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mf0 = ReducedForm.mf0;
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mf1 = ReducedForm.mf1;
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sample_size = size(Y,2);
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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 = ParticleOptions.number_of_particles;
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% Set local state space model (first order approximation).
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ghx = ReducedForm.ghx;
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ghu = ReducedForm.ghu;
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% Set local state space model (second order approximation).
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ghxx = ReducedForm.ghxx;
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ghuu = ReducedForm.ghuu;
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ghxu = ReducedForm.ghxu;
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if ReducedForm.use_k_order_solver
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dr = ReducedForm.dr;
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else
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% Set local state space model (first order approximation).
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ghx = ReducedForm.ghx;
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ghu = ReducedForm.ghu;
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% Set local state space model (second order approximation).
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ghxx = ReducedForm.ghxx;
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ghuu = ReducedForm.ghuu;
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ghxu = ReducedForm.ghxu;
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end
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% Get covariance matrices
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Q = ReducedForm.Q;
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@ -81,19 +78,6 @@ if pruning
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StateVectors_ = StateVectors;
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end
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% Uncomment for building the mean average predictions based on a sparse
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% grids of structural shocks. Otherwise, all shocks are set to 0 in the
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% prediction.
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% if ParticleOptions.proposal_approximation.cubature
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% [nodes,nodes_weights] = spherical_radial_sigma_points(number_of_structural_innovations) ;
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% nodes_weights = ones(size(nodes,1),1)*nodes_weights ;
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% elseif ParticleOptions.proposal_approximation.unscented
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% [nodes,nodes_weights,nodes_weights_c] = unscented_sigma_points(number_of_structural_innovations,ParticleOptions);
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% else
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% error('Estimation: This approximation for the proposal is not implemented or unknown!')
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% end
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% nodes = (Q_lower_triangular_cholesky*(nodes'))' ;
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nodes = zeros(1,number_of_structural_innovations) ;
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nodes_weights = ones(number_of_structural_innovations,1) ;
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@ -109,15 +93,19 @@ for t=1:sample_size
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tmp_ = tmp_ + nodes_weights(i)*tmp1_ ;
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end
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else
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tmp = 0 ;
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for i=1:size(nodes)
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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);
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if ReducedForm.use_k_order_solver
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tmp = 0;
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for i=1:size(nodes)
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tmp = tmp + nodes_weights(i)*local_state_space_iteration_k(yhat, nodes(i,:)'*ones(1,number_of_particles), dr, Model, DynareOptions);
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end
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else
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tmp = 0;
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for i=1:size(nodes)
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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);
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end
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end
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end
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PredictionError = bsxfun(@minus,Y(:,t),tmp(mf1,:));
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%tau_tilde = weights.*(exp(-.5*(const_lik+sum(PredictionError.*(H\PredictionError),1))) + 1e-99) ;
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% Replace Gaussian density with a Student density with 3 degrees of
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% freedom for fat tails.
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z = sum(PredictionError.*(H\PredictionError),1) ;
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tau_tilde = weights.*(tpdf(z,3*ones(size(z)))+1e-99) ;
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tau_tilde = tau_tilde/sum(tau_tilde) ;
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@ -132,7 +120,11 @@ for t=1:sample_size
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[tmp, tmp_] = local_state_space_iteration_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,yhat_,steadystate,ThreadsOptions.local_state_space_iteration_2);
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StateVectors_ = tmp_(mf0,:);
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else
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tmp = local_state_space_iteration_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,ThreadsOptions.local_state_space_iteration_2);
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if ReducedForm.use_k_order_solver
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tmp = local_state_space_iteration_k(yhat, epsilon, dr, Model, DynareOptions);
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else
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tmp = local_state_space_iteration_2(yhat, epsilon, ghx, ghu, constant, ghxx, ghuu, ghxu, ThreadsOptions.local_state_space_iteration_2);
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end
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end
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StateVectors = tmp(mf0,:);
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PredictionError = bsxfun(@minus,Y(:,t),tmp(mf1,:));
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@ -151,5 +143,4 @@ for t=1:sample_size
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end
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end
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%plot(lik) ;
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LIK = -sum(lik(start:end));
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LIK = -sum(lik(start:end));
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