59 lines
2.8 KiB
Matlab
59 lines
2.8 KiB
Matlab
function bayestopt_ = transform_prior_to_laplace_prior(bayestopt_)
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% bayestopt_ = transform_prior_to_laplace_prior(bayestopt_)
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% -------------------------------------------------------------------------
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% Transforms the prior specification to a Laplace type of approximation:
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% only the prior mean and standard deviation are relevant, all other shape
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% information, except for the parameter bounds, is ignored.
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% -------------------------------------------------------------------------
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% INPUTS
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% bayestopt_ [structure] prior information
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% -------------------------------------------------------------------------
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% OUTPUT
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% bayestopt_ [structure] Laplace prior information
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% -------------------------------------------------------------------------
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% This function is called by
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% o mom.run
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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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if any(setdiff([0;bayestopt_.pshape],[0,3]))
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fprintf('\nNon-normal priors specified. Penalized estimation uses a Laplace type of approximation:');
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fprintf('\nOnly the prior mean and standard deviation are relevant, all other shape information, except for the parameter bounds, is ignored.\n\n');
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non_normal_priors = (bayestopt_.pshape~=3);
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bayestopt_.pshape(non_normal_priors) = 3;
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bayestopt_.p3(non_normal_priors) = -Inf*ones(sum(non_normal_priors),1);
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bayestopt_.p4(non_normal_priors) = Inf*ones(sum(non_normal_priors),1);
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bayestopt_.p6(non_normal_priors) = bayestopt_.p1(non_normal_priors);
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bayestopt_.p7(non_normal_priors) = bayestopt_.p2(non_normal_priors);
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bayestopt_.p5(non_normal_priors) = bayestopt_.p1(non_normal_priors);
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end
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if any(isinf(bayestopt_.p2)) % find infinite variance priors
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inf_var_pars = bayestopt_.name(isinf(bayestopt_.p2));
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disp_string = [inf_var_pars{1,:}];
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for ii = 2:size(inf_var_pars,1)
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disp_string = [disp_string,', ',inf_var_pars{ii,:}];
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end
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fprintf('The parameter(s) %s have infinite prior variance. This implies a flat prior.\n',disp_string);
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fprintf('Dynare disables the matrix singularity warning\n');
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if isoctave
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warning('off','Octave:singular-matrix');
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else
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warning('off','MATLAB:singularMatrix');
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end
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end |