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fixed bug in calculation of observed mean and variance + add the possibility for smooth resampling

time-shift
Frédéric Karamé 2012-11-16 11:57:21 +01:00
parent 3a9eed00df
commit eb9bc512d7
1 changed files with 10 additions and 7 deletions
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17
matlab/particle/sequential_importance_particle_filter.m
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@ -38,7 +38,7 @@ function [LIK,lik] = sequential_importance_particle_filter(ReducedForm,Y,start,D
%! @end deftypefn
%@eod:
% Copyright (C) 2011-2012 Dynare Team
% Copyright (C) 2011, 2012 Dynare Team
%
% This file is part of Dynare.
%
@ -55,7 +55,7 @@ function [LIK,lik] = sequential_importance_particle_filter(ReducedForm,Y,start,D
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
% AUTHOR(S) frederic DOT karame AT univ DASH evry DOT fr
% AUTHOR(S) frederic DOT karame AT univ DASH lemans DOT fr
% stephane DOT adjemian AT univ DASH lemans DOT fr
persistent init_flag
@ -72,7 +72,7 @@ end
pruning = DynareOptions.particle.pruning;
% Get steady state and mean.
steadystate = ReducedForm.steadystate;
%steadystate = ReducedForm.steadystate;
constant = ReducedForm.constant;
state_variables_steady_state = ReducedForm.state_variables_steady_state;
@ -103,6 +103,10 @@ if isempty(H)
H = 0;
end
% Initialization of the likelihood.
const_lik = log(2*pi)*number_of_observed_variables;
lik = NaN(sample_size,1);
% Get initial condition for the state vector.
StateVectorMean = ReducedForm.StateVectorMean;
StateVectorVarianceSquareRoot = reduced_rank_cholesky(ReducedForm.StateVectorVariance)';
@ -116,14 +120,13 @@ state_variance_rank = size(StateVectorVarianceSquareRoot,2);
% Factorize the covariance matrix of the structural innovations
Q_lower_triangular_cholesky = chol(Q)';
[PredictedStateMean,PredictedStateVarianceSquareRoot,StateVectorMean,StateVectorVarianceSquareRoot] = ...
gaussian_filter_bank(ReducedForm,Y(:,1),StateVectorMean,StateVectorVarianceSquareRoot,Q_lower_triangular_cholesky,Q_lower_triangular_cholesky,H,DynareOptions) ;
StateVectors = bsxfun(@plus,StateVectorVarianceSquareRoot*randn(state_variance_rank,number_of_particles),StateVectorMean) ;
% Set seed for randn().
set_dynare_seed('default');
% Initialization of the likelihood.
const_lik = log(2*pi)*number_of_observed_variables;
lik = NaN(sample_size,1);
% Initialization of the weights across particles.
weights = ones(1,number_of_particles)/number_of_particles ;
StateVectors = bsxfun(@plus,StateVectorVarianceSquareRoot*randn(state_variance_rank,number_of_particles),StateVectorMean);