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function [ProposalStateVector, Weights, flag] = conditional_filter_proposal(ReducedForm, y, StateVectors, SampleWeights, Q_lower_triangular_cholesky, H_lower_triangular_cholesky, ...
H, ParticleOptions, ThreadsOptions, options_, M_)
% Computes the proposal for each past particle using Gaussian approximations
% for the state errors and the Kalman filter
%
% INPUTS
% - ReducedForm [structure] Matlab's structure describing the reduced form model.
% - y [double] p×1 vector, current observation (p is the number of observed variables).
% - StateVectors
% - SampleWeights
% - Q_lower_triangular_cholesky
% - H_lower_triangular_cholesky
% - H
% - ParticleOptions
% - ThreadsOptions
% - options_
% - M_
%
% OUTPUTS
% - ProposalStateVector
% - Weights
% - flag
% Copyright © 2012-2022 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
flag = false;
order = options_.order;
if ReducedForm.use_k_order_solver
dr = ReducedForm.dr;
udr = ReducedForm.udr;
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;
ghs2 = ReducedForm.ghs2;
if order == 3
% Set local state space model (third order approximation).
ghxxx = ReducedForm.ghxxx;
ghuuu = ReducedForm.ghuuu;
ghxxu = ReducedForm.ghxxu;
ghxuu = ReducedForm.ghxuu;
ghxss = ReducedForm.ghxss;
ghuss = ReducedForm.ghuss;
end
end
constant = ReducedForm.constant;
steadystate = ReducedForm.steadystate;
state_variables_steady_state = ReducedForm.state_variables_steady_state;
mf0 = ReducedForm.mf0;
mf1 = ReducedForm.mf1;
number_of_state_variables = length(mf0);
number_of_observed_variables = length(mf1);
number_of_structural_innovations = length(ReducedForm.Q);
if ParticleOptions.proposal_approximation.montecarlo
nodes = randn(ParticleOptions.number_of_particles/10, number_of_structural_innovations);
weights = 1.0/ParticleOptions.number_of_particles;
weights_c = weights;
elseif ParticleOptions.proposal_approximation.cubature
[nodes, weights] = spherical_radial_sigma_points(number_of_structural_innovations);
weights_c = weights;
elseif ParticleOptions.proposal_approximation.unscented
[nodes, weights, weights_c] = unscented_sigma_points(number_of_structural_innovations, ParticleOptions);
else
error('Estimation: This approximation for the proposal is not implemented or unknown!')
end
epsilon = Q_lower_triangular_cholesky*nodes';
yhat = repmat(StateVectors-state_variables_steady_state, 1, size(epsilon, 2));
if ReducedForm.use_k_order_solver
tmp = local_state_space_iteration_k(yhat, epsilon, dr, M_, options_, udr);
else
if order == 2
tmp = local_state_space_iteration_2(yhat, epsilon, ghx, ghu, constant, ghxx, ghuu, ghxu, ThreadsOptions.local_state_space_iteration_2);
elseif order == 3
tmp = local_state_space_iteration_3(yhat, epsilon, ghx, ghu, ghxx, ghuu, ghxu, ghs2, ghxxx, ghuuu, ghxxu, ghxuu, ghxss, ghuss, steadystate, ThreadsOptions.local_state_space_iteration_3, false);
else
error('Order > 3: use_k_order_solver should be set to true');
end
end
PredictedStateMean = tmp(mf0,:)*weights;
PredictedObservedMean = tmp(mf1,:)*weights;
if ParticleOptions.proposal_approximation.cubature || ParticleOptions.proposal_approximation.montecarlo
PredictedStateMean = sum(PredictedStateMean, 2);
PredictedObservedMean = sum(PredictedObservedMean, 2);
dState = bsxfun(@minus, tmp(mf0,:), PredictedStateMean)'.*sqrt(weights);
dObserved = bsxfun(@minus, tmp(mf1,:), PredictedObservedMean)'.*sqrt(weights);
PredictedStateVariance = dState*dState';
big_mat = [dObserved dState; H_lower_triangular_cholesky zeros(number_of_observed_variables,number_of_state_variables)];
[~, mat] = qr2(big_mat,0);
mat = mat';
PredictedObservedVarianceSquareRoot = mat(1:number_of_observed_variables, 1:number_of_observed_variables);
CovarianceObservedStateSquareRoot = mat(number_of_observed_variables+(1:number_of_state_variables),1:number_of_observed_variables);
StateVectorVarianceSquareRoot = mat(number_of_observed_variables+(1:number_of_state_variables),number_of_observed_variables+(1:number_of_state_variables));
Error = y-PredictedObservedMean;
StateVectorMean = PredictedStateMean+(CovarianceObservedStateSquareRoot/PredictedObservedVarianceSquareRoot)*Error;
if ParticleOptions.cpf_weights_method.amisanotristani
Weights = SampleWeights.*probability2(zeros(number_of_observed_variables,1), PredictedObservedVarianceSquareRoot, Error);
end
else
dState = bsxfun(@minus, tmp(mf0,:), PredictedStateMean);
dObserved = bsxfun(@minus, tmp(mf1,:), PredictedObservedMean);
PredictedStateVariance = dState*diag(weights_c)*dState';
PredictedObservedVariance = dObserved*diag(weights_c)*dObserved'+H;
PredictedStateAndObservedCovariance = dState*diag(weights_c)*dObserved';
KalmanFilterGain = PredictedStateAndObservedCovariance/PredictedObservedVariance;
Error = y-PredictedObservedMean;
StateVectorMean = PredictedStateMean+KalmanFilterGain*Error;
StateVectorVariance = PredictedStateVariance-KalmanFilterGain*PredictedObservedVariance*KalmanFilterGain';
StateVectorVariance = 0.5*(StateVectorVariance+StateVectorVariance');
[StateVectorVarianceSquareRoot, p] = chol(StateVectorVariance, 'lower') ;
if p
flag = true;
ProposalStateVector = zeros(number_of_state_variables, 1);
Weights = 0.0;
return
end
if ParticleOptions.cpf_weights_method.amisanotristani
Weights = SampleWeights.*probability2(zeros(number_of_observed_variables, 1), chol(PredictedObservedVariance)', Error);
end
end
ProposalStateVector = StateVectorVarianceSquareRoot*randn(size(StateVectorVarianceSquareRoot, 2), 1)+StateVectorMean;
if ParticleOptions.cpf_weights_method.murrayjonesparslow
PredictedStateVariance = 0.5*(PredictedStateVariance+PredictedStateVariance');
[PredictedStateVarianceSquareRoot, p] = chol(PredictedStateVariance, 'lower');
if p
flag = true;
ProposalStateVector = zeros(number_of_state_variables,1);
Weights = 0.0;
return
end
Prior = probability2(PredictedStateMean, PredictedStateVarianceSquareRoot, ProposalStateVector);
Posterior = probability2(StateVectorMean, StateVectorVarianceSquareRoot, ProposalStateVector);
Likelihood = probability2(y, H_lower_triangular_cholesky, measurement_equations(ProposalStateVector, ReducedForm, ThreadsOptions, options_, M_));
Weights = SampleWeights.*Likelihood.*(Prior./Posterior);
end
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