Factorize estimation: set_mcmc_jumping_covariance
parent
aa99eff81d
commit
c356db4531
|
@ -376,38 +376,7 @@ if np > 0
|
|||
save([M_.dname filesep 'Output' filesep 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
|
||||
invhess = set_mcmc_jumping_covariance(invhess, nx, options_.MCMC_jumping_covariance, bayestopt_, 'dynare_estimation_1');
|
||||
|
||||
if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
|
||||
(any(bayestopt_.pshape >0 ) && options_.load_mh_file) %% not ML estimation
|
||||
|
|
|
@ -0,0 +1,67 @@
|
|||
function invhess = set_mcmc_jumping_covariance(invhess, xparam_nbr, MCMC_jumping_covariance, bayestopt_, stringForErrors)
|
||||
% function invhess = set_mcmc_jumping_covariance(invhess, xparam_nbr, MCMC_jumping_covariance, bayestopt_, stringForErrors)
|
||||
% -------------------------------------------------------------------------
|
||||
% sets the jumping covariance matrix for the MCMC algorithm
|
||||
% =========================================================================
|
||||
% INPUTS
|
||||
% o invhess: [matrix] already computed inverse of the hessian matrix
|
||||
% o xparam_nbr: [integer] number of estimated parameters
|
||||
% o MCMC_jumping_covariance: [string] name of option or file setting the jumping covariance matrix
|
||||
% o bayestopt_: [struct] information on priors
|
||||
% o stringForErrors: [string] string to be used in error messages
|
||||
% -------------------------------------------------------------------------
|
||||
% OUTPUTS
|
||||
% o invhess: [matrix] jumping covariance matrix
|
||||
% -------------------------------------------------------------------------
|
||||
% This function is called by
|
||||
% o dynare_estimation_1.m
|
||||
% -------------------------------------------------------------------------
|
||||
% Copyright © 2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
% Dynare is free software: you can redistribute it and/or modify
|
||||
% it under the terms of the GNU General Public License as published by
|
||||
% the Free Software Foundation, either version 3 of the License, or
|
||||
% (at your option) any later version.
|
||||
%
|
||||
% Dynare is distributed in the hope that it will be useful,
|
||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
% GNU General Public License for more details.
|
||||
%
|
||||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
||||
% =========================================================================
|
||||
|
||||
switch MCMC_jumping_covariance
|
||||
case 'hessian' % do nothing and use hessian from previous mode optimization
|
||||
case 'prior_variance' % use prior variance
|
||||
if any(isinf(bayestopt_.p2))
|
||||
error('%s: Infinite prior variances detected. You cannot use the prior variances as the proposal density, if some variances are Inf.',stringForErrors);
|
||||
else
|
||||
hess = diag(1./(bayestopt_.p2.^2));
|
||||
end
|
||||
hsd = sqrt(diag(hess));
|
||||
invhess = inv(hess./(hsd*hsd'))./(hsd*hsd');
|
||||
case 'identity_matrix' % use identity
|
||||
invhess = eye(xparam_nbr);
|
||||
otherwise % user specified matrix in file
|
||||
try
|
||||
load(MCMC_jumping_covariance,'jumping_covariance')
|
||||
hess = jumping_covariance;
|
||||
catch
|
||||
error(['%s: No matrix named ''jumping_covariance'' could be found in ',options_.MCMC_jumping_covariance,'.mat!'],stringForErrors);
|
||||
end
|
||||
[nrow, ncol] = size(hess);
|
||||
if ~isequal(nrow,ncol) && ~isequal(nrow,xparam_nbr) % check if square and right size
|
||||
error(['%s: ''jumping_covariance'' matrix (loaded from ',options_.MCMC_jumping_covariance,'.mat) must be square and have ',num2str(xparam_nbr),' rows and columns!'],stringForErrors);
|
||||
end
|
||||
try % check for positive definiteness
|
||||
chol(hess);
|
||||
hsd = sqrt(diag(hess));
|
||||
invhess = inv(hess./(hsd*hsd'))./(hsd*hsd');
|
||||
catch
|
||||
error('%s: Specified ''MCMC_jumping_covariance'' is not positive definite!',stringForErrors);
|
||||
end
|
||||
end
|
Loading…
Reference in New Issue