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Bug fixes.

time-shift
Stéphane Adjemian (Gpm) 2010-02-17 18:27:20 +01:00
parent cbfd0fd562
commit de1ba423b5
2 changed files with 3 additions and 4 deletions
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5
matlab/moment_function.m
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@ -1,11 +1,10 @@
function [g,flag] = moment_function(xparams,sample_moments,weighting_matrix,options,parallel)
function [g,flag] = moment_function(xparams,sample_moments,dataset,options,parallel)
% Evaluates the moment function of the Simulated Moments Method (discrepancy between sample and
% ).
%
% INPUTS:
% xparams [double] p*1 vector of estimated parameters.
% sample_moments [double] n*1 vector of sample moments (n>=p).
% weighting_matrix [double] n*n symetric, positive definite matrix.
% options [ ] Structure defining options for SMM.
% parallel [ ] Structure defining the parallel mode settings (optional).
%
@ -81,7 +80,7 @@ clear perfect_foresight_simulation;
if nargin<5
for s = 1:options.number_of_simulated_sample
time_series = extended_path([],options.simulated_sample_size,1);
data = time_series(options.observed_variables_idx,options.burn_in_periods+1:options.simulated_sample_size);
data = time_series(dataset.observed_variables_idx,options.burn_in_periods+1:options.simulated_sample_size);
eval(['tmp = ' options.moments_file_name '(data);'])
simulated_moments = simulated_moments + tmp;
simulated_moments = simulated_moments / options.number_of_simulated_sample;

2
matlab/simulated_moments_estimation.m
View File

@ -190,7 +190,7 @@ elseif options.optimization_routine==0% Compute the variance of the SMM estimato
tmp = sortrows(estimated_parameters_optimization_path',1);
param = tmp(1,2:end)';
% Compute gradient of the moment function (distance between sample and simulated moments).
[F,G] = dynare_gradient('moment_function',param,options_.gradient_epsilon,dataset,options,parallel);
[F,G] = dynare_gradient('moment_function',param,options_.gradient_epsilon,sample_moments,dataset,options,parallel);
V = (1+1/options.number_of_simulated_sample)*G'*long_run_covariance*G;
[param,diag(V)]
end