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Revert "More fixing of the same" 

This reverts commit a54dda165f.
time-shift
Michel Juillard 2015-10-09 14:19:15 +02:00
parent 063a548bd3
commit 9be5c31761
4 changed files with 17 additions and 17 deletions
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16
matlab/dsge_likelihood_1.m
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@ -347,14 +347,14 @@ if (kalman_algo == 2) || (kalman_algo == 4)
H = diag(H);
mmm = mm;
else
Z = [Z, eye(pp)];
T = blkdiag(T,zeros(pp));
Q = blkdiag(Q,H);
R = blkdiag(R,eye(pp));
% Pstar = blkdiag(Pstar,H);
% Pinf = blkdiag(Pinf,zeros(pp));
H = zeros(pp,1);
mmm = mm+pp;
Z = [Z, eye(pp)];
T = blkdiag(T,zeros(pp));
Q = blkdiag(Q,H);
R = blkdiag(R,eye(pp));
Pstar = blkdiag(Pstar,H);
Pinf = blkdiag(Pinf,zeros(pp));
H = zeros(pp,1);
mmm = mm+pp;
end
end
end

2
matlab/evaluate_likelihood.m
View File

@ -70,6 +70,6 @@ if isempty(dataset)
[dataset, dataset_info] = makedataset(options_);
end
llik = -dsge_likelihood_1(parameters,dataset,dataset_info,options_,M_,estim_params_,bayestopt_,prior_bounds(bayestopt_,options_),oo_);
llik = -dsge_likelihood(parameters,dataset,dataset_info,options_,M_,estim_params_,bayestopt_,prior_bounds(bayestopt_,options_),oo_);
ldens = evaluate_prior(parameters);
llik = llik - ldens;

4
matlab/osr1.m
View File

@ -77,7 +77,7 @@ inv_order_var = oo_.dr.inv_order_var;
%extract unique entries of covariance
i_var=unique(i_var);
%% do initial checks
[loss,info,exit_flag,vx]=osr_obj_1(t0,i_params,inv_order_var(i_var),weights(i_var,i_var));
[loss,info,exit_flag,vx]=osr_obj(t0,i_params,inv_order_var(i_var),weights(i_var,i_var));
if info~=0
print_info(info, options_.noprint, options_);
else
@ -99,7 +99,7 @@ elseif isequal(options_.osr.opt_algo,10)
error('OSR: OSR does not support opt_algo=10.')
else
%%do actual optimization
[p, f, exitflag] = dynare_minimize_objective(str2func('osr_obj_1'),t0,options_.osr.opt_algo,options_,[],cellstr(M_.param_names(i_params,:)),[],[], i_params,...
[p, f, exitflag] = dynare_minimize_objective(str2func('osr_obj'),t0,options_.osr.opt_algo,options_,[],cellstr(M_.param_names(i_params,:)),[],[], i_params,...
inv_order_var(i_var),weights(i_var,i_var));
end

12
tests/kalman/likelihood_from_dynare/fs2000_corr_ME.mod
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@ -117,16 +117,16 @@ options_.debug=1;
%%default
options_.lik_init=1;
//estimation(kalman_algo=0,mode_compute=4,order=1,datafile='../../fs2000/fsdat_simul',smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
//fval_algo_0=oo_.likelihood_at_initial_parameters;
estimation(kalman_algo=0,mode_compute=4,order=1,datafile='../../fs2000/fsdat_simul',smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
fval_algo_0=oo_.likelihood_at_initial_parameters;
%%Multivariate Kalman Filter
options_.lik_init=1;
//estimation(kalman_algo=1,mode_file=fs2000_corr_ME_mode,mode_compute=0,order=1,datafile='../../fs2000/fsdat_simul',smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
//fval_algo_1=oo_.likelihood_at_initial_parameters;
estimation(kalman_algo=1,mode_file=fs2000_corr_ME_mode,mode_compute=0,order=1,datafile='../../fs2000/fsdat_simul',smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
fval_algo_1=oo_.likelihood_at_initial_parameters;
%%Univariate Kalman Filter
options_.lik_init=1;
//estimation(kalman_algo=3,mode_file=fs2000_corr_ME_mode,mode_compute=0,order=1,datafile='../../fs2000/fsdat_simul',smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
//fval_algo_3=oo_.likelihood_at_initial_parameters;
estimation(kalman_algo=3,mode_file=fs2000_corr_ME_mode,mode_compute=0,order=1,datafile='../../fs2000/fsdat_simul',smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
fval_algo_3=oo_.likelihood_at_initial_parameters;
%%Diffuse Multivariate Kalman Filter
options_.lik_init=1;
estimation(kalman_algo=2,mode_file=fs2000_corr_ME_mode,mode_compute=0,datafile='../../fs2000/fsdat_simul',smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;