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Dóra Kocsis
dynare
Commits
4725a205
Commit
4725a205
authored
Nov 16, 2012
by
Frédéric Karamé
Browse files
compute incremental weights for gaussian filters.
parent
a96850a0
Changes
1
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Inline
Sidebyside
matlab/particle/gaussian_densities.m
0 → 100644
View file @
4725a205
function
IncrementalWeights
=
gaussian_densities
(
obs
,
mut_t
,
sqr_Pss_t_t
,
st_t_1
,
sqr_Pss_t_t_1
,
particles
,
H
,
normconst
,
weigths1
,
weigths2
,
ReducedForm
,
DynareOptions
)
%
% Elements to calculate the importance sampling ratio
%
% INPUTS
% reduced_form_model [structure] Matlab's structure describing the reduced form model.
% reduced_form_model.measurement.H [double] (pp x pp) variance matrix of measurement errors.
% reduced_form_model.state.Q [double] (qq x qq) variance matrix of state errors.
% reduced_form_model.state.dr [structure] output of resol.m.
% Y [double] pp*smpl matrix of (detrended) data, where pp is the maximum number of observed variables.
% start [integer] scalar, likelihood evaluation starts at 'start'.
% smolyak_accuracy [integer] scalar.
%
% OUTPUTS
% LIK [double] scalar, likelihood
% lik [double] vector, density of observations in each period.
%
% REFERENCES
%
% NOTES
% The vector "lik" is used to evaluate the jacobian of the likelihood.
% Copyright (C) 20092010 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 <http://www.gnu.org/licenses/>.
% proposal density
proposal
=
probability2
(
mut_t
,
sqr_Pss_t_t
,
particles
)
;
% prior density
prior
=
probability2
(
st_t_1
,
sqr_Pss_t_t_1
,
particles
)
;
% likelihood
yt_t_1_i
=
measurement_equations
(
particles
,
ReducedForm
,
DynareOptions
)
;
eta_t_i
=
bsxfun
(
@
minus
,
obs
,
yt_t_1_i
)
'
;
yt_t_1
=
sum
(
yt_t_1_i
*
weigths1
,
2
)
;
tmp
=
bsxfun
(
@
minus
,
yt_t_1_i
,
yt_t_1
)
;
Pyy
=
bsxfun
(
@
times
,
weigths2
',tmp)*tmp'
+
H
;
sqr_det
=
sqrt
(
det
(
Pyy
))
;
foo
=
(
eta_t_i
/
Pyy
)
.*
eta_t_i
;
likelihood
=
exp
(

0.5
*
sum
(
foo
,
2
))/(
normconst
*
sqr_det
)
+
1e99
;
IncrementalWeights
=
likelihood
.*
prior
.
/
proposal
;
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