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Moving loading of MCMC_jumping_covariance to after display of standard errors and computation of Laplace approximation 

Closes #1255
time-shift
Johannes Pfeifer 2016-12-27 12:07:22 +01:00
parent 4a93d0f9f0
commit 7b4fc9ec4a
1 changed files with 33 additions and 29 deletions
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62
matlab/dynare_estimation_1.m
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@ -263,35 +263,6 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
end
end
switch options_.MCMC_jumping_covariance
case 'hessian' %Baseline
%do nothing and use hessian from mode_compute
case 'prior_variance' %Use prior variance
if any(isinf(bayestopt_.p2))
error('Infinite prior variances detected. You cannot use the prior variances as the proposal density, if some variances are Inf.')
else
hh = diag(1./(bayestopt_.p2.^2));
end
case 'identity_matrix' %Use identity
hh = eye(nx);
otherwise %user specified matrix in file
try
load(options_.MCMC_jumping_covariance,'jumping_covariance')
hh=jumping_covariance;
catch
error(['No matrix named ''jumping_covariance'' could be found in ',options_.MCMC_jumping_covariance,'.mat'])
end
[nrow, ncol]=size(hh);
if ~isequal(nrow,ncol) && ~isequal(nrow,nx) %check if square and right size
error(['jumping_covariance matrix must be square and have ',num2str(nx),' rows and columns'])
end
try %check for positive definiteness
chol(hh);
catch
error(['Specified jumping_covariance is not positive definite'])
end
end
if ~options_.mh_posterior_mode_estimation && options_.cova_compute
try
chol(hh);
@ -393,6 +364,39 @@ if np > 0
save([M_.fname '_params.mat'],'pindx');
end
switch options_.MCMC_jumping_covariance
case 'hessian' %Baseline
%do nothing and use hessian from mode_compute
case 'prior_variance' %Use prior variance
if any(isinf(bayestopt_.p2))
error('Infinite prior variances detected. You cannot use the prior variances as the proposal density, if some variances are Inf.')
else
hh = diag(1./(bayestopt_.p2.^2));
end
hsd = sqrt(diag(hh));
invhess = inv(hh./(hsd*hsd'))./(hsd*hsd');
case 'identity_matrix' %Use identity
invhess = eye(nx);
otherwise %user specified matrix in file
try
load(options_.MCMC_jumping_covariance,'jumping_covariance')
hh=jumping_covariance;
catch
error(['No matrix named ''jumping_covariance'' could be found in ',options_.MCMC_jumping_covariance,'.mat'])
end
[nrow, ncol]=size(hh);
if ~isequal(nrow,ncol) && ~isequal(nrow,nx) %check if square and right size
error(['jumping_covariance matrix must be square and have ',num2str(nx),' rows and columns'])
end
try %check for positive definiteness
chol(hh);
hsd = sqrt(diag(hh));
invhess = inv(hh./(hsd*hsd'))./(hsd*hsd');
catch
error(['Specified jumping_covariance is not positive definite'])
end
end
if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
(any(bayestopt_.pshape >0 ) && options_.load_mh_file) %% not ML estimation
bounds = prior_bounds(bayestopt_, options_.prior_trunc);