278 lines
8.2 KiB
Matlab
278 lines
8.2 KiB
Matlab
function [logged_prior_density, dlprior, d2lprior, info] = priordens(x, pshape, p6, p7, p3, p4, initialization)
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% Computes a prior density for the structural parameters of DSGE models
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%
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% INPUTS
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% x [double] vector with n elements.
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% pshape [integer] vector with n elements (bayestopt_.pshape).
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% p6: [double] vector with n elements, first parameter of the prior distribution (bayestopt_.p6).
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% p7: [double] vector with n elements, second parameter of the prior distribution (bayestopt_.p7).
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% p3: [double] vector with n elements, lower bounds of the untruncated standard or generalized distribution
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% p4: [double] vector with n elements, upper bound of the untruncated standard or generalized distribution
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% initialization [integer] if 1: initialize persistent variables
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%
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% OUTPUTS
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% logged_prior_density [double] scalar, log of the prior density evaluated at x.
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% info [double] error code for index of Inf-prior parameter
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%
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% Copyright © 2003-2023 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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persistent id1 id2 id3 id4 id5 id6 id8
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persistent tt1 tt2 tt3 tt4 tt5 tt6 tt8
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info=0;
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if nargin > 6 && initialization
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% Beta indices.
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tt1 = true;
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id1 = find(pshape==1);
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if isempty(id1)
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tt1 = false;
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end
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% Gamma indices.
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tt2 = true;
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id2 = find(pshape==2);
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if isempty(id2)
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tt2 = false;
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end
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% Gaussian indices.
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tt3 = true;
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id3 = find(pshape==3);
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if isempty(id3)
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tt3 = false;
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end
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% Inverse-Gamma-1 indices.
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tt4 = true;
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id4 = find(pshape==4);
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if isempty(id4)
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tt4 = false;
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end
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% Uniform indices.
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tt5 = true;
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id5 = find(pshape==5);
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if isempty(id5)
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tt5 = false;
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end
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% Inverse-Gamma-2 indices.
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tt6 = true;
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id6 = find(pshape==6);
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if isempty(id6)
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tt6 = false;
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end
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% Weibull indices.
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tt8 = true;
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id8 = find(pshape==8);
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if isempty(id8)
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tt8 = false;
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end
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end
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logged_prior_density = 0.0;
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dlprior = zeros(1,length(x));
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d2lprior = dlprior;
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if tt1
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logged_prior_density = logged_prior_density + sum(lpdfgbeta(x(id1),p6(id1),p7(id1),p3(id1),p4(id1))) ;
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if isinf(logged_prior_density)
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if nargout ==4
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info=id1(isinf(lpdfgbeta(x(id1),p6(id1),p7(id1),p3(id1),p4(id1))));
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end
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return
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end
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if nargout == 2
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[tmp, dlprior(id1)]=lpdfgbeta(x(id1),p6(id1),p7(id1),p3(id1),p4(id1));
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elseif nargout == 3
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[tmp, dlprior(id1), d2lprior(id1)]=lpdfgbeta(x(id1),p6(id1),p7(id1),p3(id1),p4(id1));
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end
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end
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if tt2
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logged_prior_density = logged_prior_density + sum(lpdfgam(x(id2)-p3(id2),p6(id2),p7(id2))) ;
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if isinf(logged_prior_density)
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if nargout ==4
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info=id2(isinf(lpdfgam(x(id2)-p3(id2),p6(id2),p7(id2))));
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end
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return
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end
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if nargout == 2
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[tmp, dlprior(id2)]=lpdfgam(x(id2)-p3(id2),p6(id2),p7(id2));
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elseif nargout == 3
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[tmp, dlprior(id2), d2lprior(id2)]=lpdfgam(x(id2)-p3(id2),p6(id2),p7(id2));
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end
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end
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if tt3
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logged_prior_density = logged_prior_density + sum(lpdfnorm(x(id3),p6(id3),p7(id3))) ;
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if nargout == 2
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[tmp, dlprior(id3)]=lpdfnorm(x(id3),p6(id3),p7(id3));
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elseif nargout == 3
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[tmp, dlprior(id3), d2lprior(id3)]=lpdfnorm(x(id3),p6(id3),p7(id3));
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end
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end
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if tt4
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logged_prior_density = logged_prior_density + sum(lpdfig1(x(id4)-p3(id4),p6(id4),p7(id4))) ;
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if isinf(logged_prior_density)
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if nargout ==4
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info=id4(isinf(lpdfig1(x(id4)-p3(id4),p6(id4),p7(id4))));
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end
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return
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end
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if nargout == 2
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[tmp, dlprior(id4)]=lpdfig1(x(id4)-p3(id4),p6(id4),p7(id4));
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elseif nargout == 3
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[tmp, dlprior(id4), d2lprior(id4)]=lpdfig1(x(id4)-p3(id4),p6(id4),p7(id4));
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end
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end
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if tt5
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if any(x(id5)-p3(id5)<0) || any(x(id5)-p4(id5)>0)
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logged_prior_density = -Inf ;
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if nargout ==4
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info=id5((x(id5)-p3(id5)<0) || (x(id5)-p4(id5)>0));
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end
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return
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end
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logged_prior_density = logged_prior_density + sum(log(1./(p4(id5)-p3(id5)))) ;
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if nargout >1
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dlprior(id5)=zeros(length(id5),1);
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end
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if nargout == 3
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d2lprior(id5)=zeros(length(id5),1);
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end
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end
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if tt6
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logged_prior_density = logged_prior_density + sum(lpdfig2(x(id6)-p3(id6),p6(id6),p7(id6))) ;
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if isinf(logged_prior_density)
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if nargout ==4
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info=id6(isinf(lpdfig2(x(id6)-p3(id6),p6(id6),p7(id6))));
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end
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return
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end
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if nargout == 2
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[tmp, dlprior(id6)]=lpdfig2(x(id6)-p3(id6),p6(id6),p7(id6));
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elseif nargout == 3
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[tmp, dlprior(id6), d2lprior(id6)]=lpdfig2(x(id6)-p3(id6),p6(id6),p7(id6));
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end
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end
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if tt8
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logged_prior_density = logged_prior_density + sum(lpdfgweibull(x(id8),p6(id8),p7(id8)));
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if isinf(logged_prior_density)
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if nargout ==4
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info=id8(isinf(log(lpdfgweibull(x(id8),p6(id8),p7(id8)))));
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end
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return
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end
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if nargout==2
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[tmp, dlprior(id8)] = lpdfgweibull(x(id8),p6(id8),p7(id8));
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elseif nargout==3
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[tmp, dlprior(id8), d2lprior(id8)] = lpdfgweibull(x(id8),p6(id8),p7(id8));
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end
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end
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if nargout==3
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d2lprior = diag(d2lprior);
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end
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return % --*-- Unit tests --*--
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%@test:1
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% Fill global structures with required fields...
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prior_trunc = 1e-10;
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p0 = repmat([1; 2; 3; 4; 5; 6; 8], 2, 1); % Prior shape
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p1 = .4*ones(14,1); % Prior mean
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p2 = .2*ones(14,1); % Prior std.
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p3 = NaN(14,1);
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p4 = NaN(14,1);
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p5 = NaN(14,1);
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p6 = NaN(14,1);
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p7 = NaN(14,1);
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for i=1:14
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switch p0(i)
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case 1
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% Beta distribution
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p3(i) = 0;
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p4(i) = 1;
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[p6(i), p7(i)] = beta_specification(p1(i), p2(i)^2, p3(i), p4(i));
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p5(i) = compute_prior_mode([p6(i) p7(i)], 1);
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case 2
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% Gamma distribution
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p3(i) = 0;
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p4(i) = Inf;
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[p6(i), p7(i)] = gamma_specification(p1(i), p2(i)^2, p3(i), p4(i));
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p5(i) = compute_prior_mode([p6(i) p7(i)], 2);
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case 3
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% Normal distribution
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p3(i) = -Inf;
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p4(i) = Inf;
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p6(i) = p1(i);
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p7(i) = p2(i);
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p5(i) = p1(i);
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case 4
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% Inverse Gamma (type I) distribution
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p3(i) = 0;
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p4(i) = Inf;
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[p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 1, false);
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p5(i) = compute_prior_mode([p6(i) p7(i)], 4);
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case 5
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% Uniform distribution
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[p1(i), p2(i), p6(i), p7(i)] = uniform_specification(p1(i), p2(i), p3(i), p4(i));
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p3(i) = p6(i);
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p4(i) = p7(i);
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p5(i) = compute_prior_mode([p6(i) p7(i)], 5);
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case 6
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% Inverse Gamma (type II) distribution
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p3(i) = 0;
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p4(i) = Inf;
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[p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 2, false);
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p5(i) = compute_prior_mode([p6(i) p7(i)], 6);
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case 8
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% Weibull distribution
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p3(i) = 0;
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p4(i) = Inf;
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[p6(i), p7(i)] = weibull_specification(p1(i), p2(i)^2, p3(i));
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p5(i) = compute_prior_mode([p6(i) p7(i)], 8);
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otherwise
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error('This density is not implemented!')
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end
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end
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% Call the tested routine
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try
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% Initialization of priordens.
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lpdstar = priordens(p5, p0, p6, p7, p3, p4, true);
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% Do simulations in a loop and estimate recursively the mean and teh variance.
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LPD = NaN(10000,1);
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for i = 1:10000
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draw = p5+randn(size(p5))*.02;
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LPD(i) = priordens(p5, p0, p6, p7, p3, p4);
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end
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t(1) = true;
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catch
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t(1) = false;
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end
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if t(1)
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t(2) = all(LPD<=lpdstar);
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end
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T = all(t);
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%@eof:1
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