stepan
/
dynare
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Always display value of posterior/likelihood at the mode 

Also clarifies that minus the posterior/likelihood is displayed
time-shift
Johannes Pfeifer 2014-10-31 11:58:17 +01:00 committed by Michel Juillard
parent 747f90d89b
commit 4b1e815728
1 changed files with 12 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

15
matlab/dynare_estimation_1.m
View File

@ -256,6 +256,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
end
[xparam1,fval,exitflag,output,lamdba,grad,hessian_fmincon] = ...
fmincon(objective_function,xparam1,[],[],[],[],bounds.lb,bounds.ub,[],optim_options,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
fprintf('\nFinal value of minus the log posterior (or likelihood):%f \n', fval);
case 2
error('ESTIMATION: mode_compute=2 option (Lester Ingber''s Adaptive Simulated Annealing) is no longer available')
case 3
@ -279,6 +280,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
func = @(x) objective_function(x, dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
[xparam1,fval,exitflag] = fminunc(func,xparam1,optim_options);
end
fprintf('\nFinal value of minus the log posterior (or likelihood):%f \n', fval);
case 4
% Set default options.
H0 = 1e-4*eye(nx);
@ -316,7 +318,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
[fval,xparam1,grad,hessian_csminwel,itct,fcount,retcodehat] = ...
csminwel1(objective_function, xparam1, H0, analytic_grad, crit, nit, numgrad, epsilon, dataset_, dataset_info, options_, M_, estim_params_, bayestopt_,bounds, oo_);
% Disp value at the mode.
disp(sprintf('Objective function at mode: %f',fval))
fprintf('\nFinal value of minus the log posterior (or likelihood):%f \n', fval);
case 5
if isfield(options_,'hess')
flag = options_.hess;
@ -361,6 +363,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
end
parameter_names = bayestopt_.name;
save([M_.fname '_mode.mat'],'xparam1','hh','parameter_names');
fprintf('\nFinal value of minus the log posterior (or likelihood):%f \n', fval);
case 6
% Set default options
gmhmaxlikOptions = options_.gmhmaxlik;
@ -479,7 +482,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
skipline()
disp(['Optimal value of the scale parameter = ' num2str(Scale)])
skipline()
disp(['Final value of the log posterior (or likelihood): ' num2str(fval)])
fprintf('\nFinal value of minus the log posterior (or likelihood):%f \n', fval);
skipline()
parameter_names = bayestopt_.name;
save([M_.fname '_mode.mat'],'xparam1','hh','parameter_names');
@ -495,6 +498,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
eval(['optim_options = optimset(optim_options,' options_.optim_opt ');']);
end
[xparam1,fval,exitflag] = fminsearch(objective_function,xparam1,optim_options,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
fprintf('\nFinal value of minus the log posterior (or likelihood):%f \n', fval);
case 8
% Dynare implementation of the simplex algorithm.
simplexOptions = options_.simplex;
@ -520,6 +524,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
end
end
[xparam1,fval,exitflag] = simplex_optimization_routine(objective_function,xparam1,simplexOptions,bayestopt_.name,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
fprintf('\nFinal value of minus the log posterior (or likelihood):%f \n', fval);
case 9
% Set defaults
H0 = 1e-4*ones(nx,1);
@ -546,7 +551,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
warning('off','CMAES:InitialSigma');
[x, fval, COUNTEVAL, STOPFLAG, OUT, BESTEVER] = cmaes(func2str(objective_function),xparam1,H0,cmaesOptions,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
xparam1=BESTEVER.x;
disp(sprintf('\n Objective function at mode: %f',fval))
fprintf('\nFinal value of minus the log posterior (or likelihood):%f \n', BESTEVER.f);
case 10
simpsaOptions = options_.simpsa;
if isfield(options_,'optim_opt')
@ -576,6 +581,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
simpsaOptionsList = options2cell(simpsaOptions);
simpsaOptions = simpsaset(simpsaOptionsList{:});
[xparam1, fval, exitflag] = simpsa(func2str(objective_function),xparam1,bounds.lb,bounds.ub,simpsaOptions,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
fprintf('\nFinal value of minus the log posterior (or likelihood):%f \n', fval);
case 11
options_.cova_compute = 0 ;
[xparam1,stdh,lb_95,ub_95,med_param] = online_auxiliary_filter(xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_) ;
@ -585,6 +591,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
myoptions(3)=1e-6; %accuracy of function (see Solvopt p.29)
myoptions(5)= 1.0;
[xparam1,fval]=solvopt(xparam1,objective_function,[],myoptions,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
fprintf('\nFinal value of minus the log posterior (or likelihood):%f \n', fval);
case 102
%simulating annealing
% LB=zeros(size(xparam1))-20;
@ -622,9 +629,11 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
[xparam1, fval, nacc, nfcnev, nobds, ier, t, vm] = sa(objective_function,xparam1,maxy,rt_,epsilon,ns,nt ...
,neps,maxevl,LB,UB,c,idisp ,t,vm,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
fprintf('\nFinal value of minus the log posterior (or likelihood):%f \n', fval);
otherwise
if ischar(options_.mode_compute)
[xparam1, fval, retcode ] = feval(options_.mode_compute,objective_function,xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
fprintf('\nFinal value of minus the log posterior (or likelihood):%f \n', fval);
else
error(['dynare_estimation:: mode_compute = ' int2str(options_.mode_compute) ' option is unknown!'])
end