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