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* Added number of particles (10;-) when calling monte_carlo_gaussian_particle_filter from particle/DsgeLiklihood. 

* Cosmetic changes.


git-svn-id: https://www.dynare.org/svn/dynare/trunk@3233 ac1d8469-bf42-47a9-8791-bf33cf982152
time-shift
stepan 2009-12-11 22:25:56 +00:00
parent b8db5a23a1
commit 90f5ab741b
3 changed files with 4 additions and 10 deletions
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2
matlab/particle/DsgeLikelihood.m
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@ -255,7 +255,7 @@ function [fval,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend,data
rfm.measurement.H = H;
number_of_particles = 10;
LIK = monte_carlo_gaussian_particle_filter(rfm,Y);
LIK = monte_carlo_gaussian_particle_filter(rfm,Y,[],number_of_particles);
% ------------------------------------------------------------------------------

3
matlab/particle/local_state_space_iteration_2.m
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@ -16,8 +16,7 @@ function y = local_state_space_iteration_2(yhat,epsilon,ghx,ghu,constant,half_gh
% constant [double] m*1 vector (steady state + second order correction).
% half_ghxx [double] m*n² matrix, subset of .5*dr.ghxx.
% half_ghuu [double] m*q² matrix, subset of .5*dr.ghuu.
% ghxu [double] m*nq matrix, subset of dr.ghxu.
% index [integer]
% ghxu [double] m*nq matrix, subset of dr.ghxu.
%
% OUTPUTS
% y [double] stochastic simulations results

9
matlab/particle/monte_carlo_gaussian_particle_filter.m
View File

@ -39,7 +39,7 @@ function [LIK,lik] = monte_carlo_gaussian_particle_filter(reduced_form_model,Y,s
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
global M_ bayestopt_
global M_ bayestopt_ oo_
persistent init_flag
persistent restrict_variables_idx observed_variables_idx state_variables_idx mf0 mf1
persistent sample_size number_of_state_variables number_of_observed_variables number_of_structural_innovations
@ -63,7 +63,7 @@ if isempty(init_flag)
restrict_variables_idx = bayestopt_.restrict_var_list;
observed_variables_idx = restrict_variables_idx(mf1);
state_variables_idx = restrict_variables_idx(mf0);
sample_size = size(Y,2);
sample_size = size(Y,2);
number_of_state_variables = length(mf0);
number_of_observed_variables = length(mf1);
number_of_structural_innovations = length(Q);
@ -85,12 +85,8 @@ StateVectorVariance = lyapunov_symm(ghx(mf0,:),ghu(mf0,:)*Q*ghu(mf0,:)',1e-12,1e
StateVectorVarianceSquareRoot = reduced_rank_cholesky(StateVectorVariance)';
state_variance_rank = size(StateVectorVarianceSquareRoot,2);
%state_idx = 1:state_variance_rank;
%innovation_idx = 1+state_variance_rank:state_variance_rank+number_of_structural_innovations;
Q_lower_triangular_cholesky = chol(Q)';
% Set seed for randn().
seed = [ 362436069 ; 521288629 ];
randn('state',seed);
@ -98,7 +94,6 @@ randn('state',seed);
const_lik = log(2*pi)*number_of_observed_variables;
lik = NaN(sample_size,1);
for t=1:sample_size
PredictedStateMean = zeros(number_of_state_variables,1);
PredictedObservedMean = zeros(number_of_observed_variables,1);