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function [logged_prior_density, dlprior, d2lprior, info] = priordens(x, pshape, p6, p7, p3, p4, initialization) % --*-- Unitary tests --*--
% Computes a prior density for the structural parameters of DSGE models
%
% INPUTS
% x [double] vector with n elements.
% pshape [integer] vector with n elements (bayestopt_.pshape).
% p6: [double] vector with n elements, first parameter of the prior distribution (bayestopt_.p6).
% p7: [double] vector with n elements, second parameter of the prior distribution (bayestopt_.p7).
% p3: [double] vector with n elements, lower bounds of the untruncated standard or generalized distribution
% p4: [double] vector with n elements, upper bound of the untruncated standard or generalized distribution
% initialization [integer] if 1: initialize persistent variables
%
% OUTPUTS
% logged_prior_density [double] scalar, log of the prior density evaluated at x.
% info [double] error code for index of Inf-prior parameter
%
% Copyright (C) 2003-2017 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 <http://www.gnu.org/licenses/>.
persistent id1 id2 id3 id4 id5 id6 id8
persistent tt1 tt2 tt3 tt4 tt5 tt6 tt8
info=0;
if nargin > 6 && initialization
% Beta indices.
tt1 = true;
id1 = find(pshape==1);
if isempty(id1)
tt1 = false;
end
% Gamma indices.
tt2 = true;
id2 = find(pshape==2);
if isempty(id2)
tt2 = false;
end
% Gaussian indices.
tt3 = true;
id3 = find(pshape==3);
if isempty(id3)
tt3 = false;
end
% Inverse-Gamma-1 indices.
tt4 = true;
id4 = find(pshape==4);
if isempty(id4)
tt4 = false;
end
% Uniform indices.
tt5 = true;
id5 = find(pshape==5);
if isempty(id5)
tt5 = false;
end
% Inverse-Gamma-2 indices.
tt6 = true;
id6 = find(pshape==6);
if isempty(id6)
tt6 = false;
end
% Weibull indices.
tt8 = true;
id8 = find(pshape==8);
if isempty(id8)
tt8 = false;
end
end
logged_prior_density = 0.0;
dlprior = 0.0;
d2lprior = 0.0;
if tt1
logged_prior_density = logged_prior_density + sum(lpdfgbeta(x(id1),p6(id1),p7(id1),p3(id1),p4(id1))) ;
if isinf(logged_prior_density)
if nargout ==4
info=id1(isinf(lpdfgbeta(x(id1),p6(id1),p7(id1),p3(id1),p4(id1))));
end
return
end
if nargout == 2
[tmp, dlprior(id1)]=lpdfgbeta(x(id1),p6(id1),p7(id1),p3(id1),p4(id1));
elseif nargout == 3
[tmp, dlprior(id1), d2lprior(id1)]=lpdfgbeta(x(id1),p6(id1),p7(id1),p3(id1),p4(id1));
end
end
if tt2
logged_prior_density = logged_prior_density + sum(lpdfgam(x(id2)-p3(id2),p6(id2),p7(id2))) ;
if isinf(logged_prior_density)
if nargout ==4
info=id2(isinf(lpdfgam(x(id2)-p3(id2),p6(id2),p7(id2))));
end
return
end
if nargout == 2
[tmp, dlprior(id2)]=lpdfgam(x(id2)-p3(id2),p6(id2),p7(id2));
elseif nargout == 3
[tmp, dlprior(id2), d2lprior(id2)]=lpdfgam(x(id2)-p3(id2),p6(id2),p7(id2));
end
end
if tt3
logged_prior_density = logged_prior_density + sum(lpdfnorm(x(id3),p6(id3),p7(id3))) ;
if nargout == 2
[tmp, dlprior(id3)]=lpdfnorm(x(id3),p6(id3),p7(id3));
elseif nargout == 3
[tmp, dlprior(id3), d2lprior(id3)]=lpdfnorm(x(id3),p6(id3),p7(id3));
end
end
if tt4
logged_prior_density = logged_prior_density + sum(lpdfig1(x(id4)-p3(id4),p6(id4),p7(id4))) ;
if isinf(logged_prior_density)
if nargout ==4
info=id4(isinf(lpdfig1(x(id4)-p3(id4),p6(id4),p7(id4))));
end
return
end
if nargout == 2
[tmp, dlprior(id4)]=lpdfig1(x(id4)-p3(id4),p6(id4),p7(id4));
elseif nargout == 3
[tmp, dlprior(id4), d2lprior(id4)]=lpdfig1(x(id4)-p3(id4),p6(id4),p7(id4));
end
end
if tt5
if any(x(id5)-p3(id5)<0) || any(x(id5)-p4(id5)>0)
logged_prior_density = -Inf ;
if nargout ==4
info=id5((x(id5)-p3(id5)<0) || (x(id5)-p4(id5)>0));
end
return
end
logged_prior_density = logged_prior_density + sum(log(1./(p4(id5)-p3(id5)))) ;
if nargout >1
dlprior(id5)=zeros(length(id5),1);
end
if nargout == 3
d2lprior(id5)=zeros(length(id5),1);
end
end
if tt6
logged_prior_density = logged_prior_density + sum(lpdfig2(x(id6)-p3(id6),p6(id6),p7(id6))) ;
if isinf(logged_prior_density)
if nargout ==4
info=id6(isinf(lpdfig2(x(id6)-p3(id6),p6(id6),p7(id6))));
end
return
end
if nargout == 2
[tmp, dlprior(id6)]=lpdfig2(x(id6)-p3(id6),p6(id6),p7(id6));
elseif nargout == 3
[tmp, dlprior(id6), d2lprior(id6)]=lpdfig2(x(id6)-p3(id6),p6(id6),p7(id6));
end
end
if tt8
logged_prior_density = logged_prior_density + sum(lpdfgweibull(x(id8),p6(id8),p7(id8)));
if isinf(logged_prior_density)
if nargout ==4
info=id8(isinf(log(lpdfgweibull(x(id8),p6(id8),p7(id8)))));
end
return
end
if nargout==2
[tmp, dlprior(id8)] = lpdfgweibull(x(id8),p6(id8),p7(id8))
elseif nargout==3
[tmp, dlprior(id8), ds2lprior(id8)] = lpdfgweibull(x(id8),p6(id8),p7(id8))
end
end
if nargout==3
d2lprior = diag(d2lprior);
end
%@test:1
%$ % Fill global structures with required fields...
%$ prior_trunc = 1e-10;
%$ p0 = repmat([1; 2; 3; 4; 5; 6; 8], 2, 1); % Prior shape
%$ p1 = .4*ones(14,1); % Prior mean
%$ p2 = .2*ones(14,1); % Prior std.
%$ p3 = NaN(14,1);
%$ p4 = NaN(14,1);
%$ p5 = NaN(14,1);
%$ p6 = NaN(14,1);
%$ p7 = NaN(14,1);
%$
%$ for i=1:14
%$ switch p0(i)
%$ case 1
%$ % Beta distribution
%$ p3(i) = 0;
%$ p4(i) = 1;
%$ [p6(i), p7(i)] = beta_specification(p1(i), p2(i)^2, p3(i), p4(i));
%$ p5(i) = compute_prior_mode([p6(i) p7(i)], 1);
%$ case 2
%$ % Gamma distribution
%$ p3(i) = 0;
%$ p4(i) = Inf;
%$ [p6(i), p7(i)] = gamma_specification(p1(i), p2(i)^2, p3(i), p4(i));
%$ p5(i) = compute_prior_mode([p6(i) p7(i)], 2);
%$ case 3
%$ % Normal distribution
%$ p3(i) = -Inf;
%$ p4(i) = Inf;
%$ p6(i) = p1(i);
%$ p7(i) = p2(i);
%$ p5(i) = p1(i);
%$ case 4
%$ % Inverse Gamma (type I) distribution
%$ p3(i) = 0;
%$ p4(i) = Inf;
%$ [p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 1, false);
%$ p5(i) = compute_prior_mode([p6(i) p7(i)], 4);
%$ case 5
%$ % Uniform distribution
%$ [p1(i), p2(i), p6(i), p7(i)] = uniform_specification(p1(i), p2(i), p3(i), p4(i));
%$ p3(i) = p6(i);
%$ p4(i) = p7(i);
%$ p5(i) = compute_prior_mode([p6(i) p7(i)], 5);
%$ case 6
%$ % Inverse Gamma (type II) distribution
%$ p3(i) = 0;
%$ p4(i) = Inf;
%$ [p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 2, false);
%$ p5(i) = compute_prior_mode([p6(i) p7(i)], 6);
%$ case 8
%$ % Weibull distribution
%$ p3(i) = 0;
%$ p4(i) = Inf;
%$ [p6(i), p7(i)] = weibull_specification(p1(i), p2(i)^2, p3(i));
%$ p5(i) = compute_prior_mode([p6(i) p7(i)], 8);
%$ otherwise
%$ error('This density is not implemented!')
%$ end
%$ end
%$
%$ % Call the tested routine
%$ try
%$ % Initialization of priordens.
%$ lpdstar = priordens(p5, p0, p6, p7, p3, p4, true);
%$ % Do simulations in a loop and estimate recursively the mean and teh variance.
%$ LPD = NaN(10000,1);
%$ for i = 1:10000
%$ draw = p5+randn(size(p5))*.02;
%$ LPD(i) = priordens(p5, p0, p6, p7, p3, p4);
%$ end
%$ t(1) = true;
%$ catch
%$ t(1) = false;
%$ end
%$
%$ if t(1)
%$ t(2) = all(LPD<=lpdstar);
%$ end
%$ T = all(t);
%@eof:1
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