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Replaced disp(' ') by skipline().

time-shift
Stéphane Adjemian (Charybdis) 2013-07-10 16:16:32 +02:00
parent 964b7580d8
commit 184c403375
1 changed files with 10 additions and 10 deletions
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20
matlab/dynare_estimation_1.m
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@ -34,9 +34,9 @@ global M_ options_ oo_ estim_params_ bayestopt_ dataset_
% Set particle filter flag.
if options_.order > 1
if options_.particle.status && options_.order==2
disp(' ')
skipline()
disp('Estimation using a non linear filter!')
disp(' ')
skipline()
if ~options_.nointeractive && ismember(options_.mode_compute,[1,3,4]) % Known gradient-based optimizers
disp('You are using a gradient-based mode-finder. Particle filtering introduces discontinuities in the')
disp('objective function w.r.t the parameters. Thus, should use a non-gradient based optimizer.')
@ -357,7 +357,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
fval = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
options_.mh_jscale = Scale;
mouvement = max(max(abs(PostVar-OldPostVar)));
disp(' ')
skipline()
disp('========================================================== ')
disp([' Change in the covariance matrix = ' num2str(mouvement) '.'])
disp([' Mode improvement = ' num2str(abs(OldMode-fval))])
@ -378,7 +378,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
options_.mh_jscale = Scale;
mouvement = max(max(abs(PostVar-OldPostVar)));
fval = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
disp(' ')
skipline()
disp('========================================================== ')
disp([' Change in the covariance matrix = ' num2str(mouvement) '.'])
disp([' Mode improvement = ' num2str(abs(OldMode-fval))])
@ -391,11 +391,11 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
save([M_.fname '_optimal_mh_scale_parameter.mat'],'Scale');
bayestopt_.jscale = ones(length(xparam1),1)*Scale;
end
disp(' ')
skipline()
disp(['Optimal value of the scale parameter = ' num2str(Scale)])
disp(' ')
skipline()
disp(['Final value of the log posterior (or likelihood): ' num2str(fval)])
disp(' ')
skipline()
parameter_names = bayestopt_.name;
save([M_.fname '_mode.mat'],'xparam1','hh','parameter_names');
case 7
@ -507,7 +507,7 @@ if ~options_.mh_posterior_mode_estimation && options_.cova_compute
try
chol(hh);
catch
disp(' ')
skipline()
disp('POSTERIOR KERNEL OPTIMIZATION PROBLEM!')
disp(' (minus) the hessian matrix at the "mode" is not positive definite!')
disp('=> posterior variance of the estimated parameters are not positive.')
@ -556,9 +556,9 @@ if any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
log_det_invhess = -estim_params_nbr*log(scale_factor)+log(det(scale_factor*invhess));
likelihood = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
oo_.MarginalDensity.LaplaceApproximation = .5*estim_params_nbr*log(2*pi) + .5*log_det_invhess - likelihood;
disp(' ')
skipline()
disp(sprintf('Log data density [Laplace approximation] is %f.',oo_.MarginalDensity.LaplaceApproximation))
disp(' ')
skipline()
end
elseif ~any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
oo_=display_estimation_results_table(xparam1,stdh,M_,options_,estim_params_,bayestopt_,oo_,pnames,'Maximum Likelihood','mle');