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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
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