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| Author | SHA1 | Date |
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 Stéphane Adjemian (Ryûk)
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1839529288
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Stéphane Adjemian (Ryûk)
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b5cbc397a6
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Stéphane Adjemian (Ryûk)
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e05e9c5690
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Stéphane Adjemian (Ryûk)
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301a45ef59
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Stéphane Adjemian (Ryûk)
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5375070fa3
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Stéphane Adjemian (Ryûk)
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278b669a33
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Stéphane Adjemian (Ryûk)
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90f5fc72c9
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@ -0,0 +1,35 @@
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function lpd = densities(o, X)
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% Evaluate the logged prior densities at X (for each column).
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%
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% INPUTS
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% - o [dprior]
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% - X [double] m×n matrix, n points where the prior density is evaluated.
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%
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% OUTPUTS
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% - lpd [double] 1×n, values of the logged prior density at X.
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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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n = columns(X);
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lpd = NaN(1, n);
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parfor i=1:n
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lpd(i) = density(o, X(:,i));
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end
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@ -0,0 +1,384 @@
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function [lpd, dlpd, d2lpd, info] = density(o, x)
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% Evaluate the logged prior density at x.
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%
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% INPUTS
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% - o [dprior]
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% - x [double] m×1 vector, point where the prior density is evaluated.
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%
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% OUTPUTS
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% - lpd [double] scalar, value of the logged prior density at x.
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% - dlpd [double] m×1 vector, first order derivatives.
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% - d2lpd [double] m×1 vector, second order derivatives.
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%
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% REMARKS
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% Second order derivatives holder, d2lpd, has the same rank and shape than dlpd because the priors are
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% independent (we would have to use a matrix if non orthogonal priors were allowed in Dynare).
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%
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% EXAMPLE
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%
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% >> Prior = dprior(bayestopt_, options_.prior_trunc);
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% >> lpd = Prior.dsensity(x)
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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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lpd = 0.0;
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if nargout>1
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dlpd = zeros(1, length(x));
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if nargout>2
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d2lpd = dlpd;
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if nargout>3
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info = [];
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end
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end
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end
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if o.isuniform
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if any(x(o.iduniform)-o.p3(o.iduniform)<0) || any(x(o.iduniform)-o.p4(o.iduniform)>0)
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lpd = -Inf ;
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if nargout==4
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info = o.iduniform((x(o.iduniform)-o.p3(o.iduniform)<0) || (x(o.iduniform)-o.p4(o.iduniform)>0));
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end
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return
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end
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lpd = lpd - sum(log(o.p4(o.iduniform)-o.p3(o.iduniform))) ;
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if nargout>1
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dlpd(o.iduniform) = zeros(length(o.iduniform), 1);
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if nargout>2
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d2lpd(o.iduniform) = zeros(length(o.iduniform), 1);
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end
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end
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end
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if o.isgaussian
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switch nargout
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case 1
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lpd = lpd + sum(lpdfnorm(x(o.idgaussian), o.p6(o.idgaussian), o.p7(o.idgaussian)));
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case 2
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[tmp, dlpd(o.idgaussian)] = lpdfnorm(x(o.idgaussian), o.p6(o.idgaussian), o.p7(o.idgaussian));
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lpd = lpd + sum(tmp);
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case {3,4}
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[tmp, dlpd(o.idgaussian), d2lpd(o.idgaussian)] = lpdfnorm(x(o.idgaussian), o.p6(o.idgaussian), o.p7(o.idgaussian));
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lpd = lpd + sum(tmp);
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end
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end
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if o.isgamma
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switch nargout
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case 1
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lpd = lpd + sum(lpdfgam(x(o.idgamma)-o.p3(o.idgamma), o.p6(o.idgamma), o.p7(o.idgamma)));
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if isinf(lpd), return, end
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case 2
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[tmp, dlpd(o.idgamma)] = lpdfgam(x(o.idgamma)-o.p3(o.idgamma), o.p6(o.idgamma), o.p7(o.idgamma));
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lpd = lpd + sum(tmp);
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if isinf(lpd), return, end
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case 3
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[tmp, dlpd(o.idgamma), d2lpd(o.idgamma)] = lpdfgam(x(o.idgamma)-o.p3(o.idgamma), o.p6(o.idgamma), o.p7(o.idgamma));
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lpd = lpd + sum(tmp);
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if isinf(lpd), return, end
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case 4
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[tmp, dlpd(o.idgamma), d2lpd(o.idgamma)] = lpdfgam(x(o.idgamma)-o.p3(o.idgamma), o.p6(o.idgamma), o.p7(o.idgamma));
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lpd = lpd + sum(tmp);
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if isinf(lpd)
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info = o.idgamma(isinf(tmp));
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return
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end
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end
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end
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if o.isbeta
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switch nargout
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case 1
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lpd = lpd + sum(lpdfgbeta(x(o.idbeta), o.p6(o.idbeta), o.p7(o.idbeta), o.p3(o.idbeta), o.p4(o.idbeta)));
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if isinf(lpd), return, end
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case 2
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[tmp, dlpd(o.idbeta)] = lpdfgbeta(x(o.idbeta), o.p6(o.idbeta), o.p7(o.idbeta), o.p3(o.idbeta), o.p4(o.idbeta));
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lpd = lpd + sum(tmp);
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if isinf(lpd), return, end
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case 3
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[tmp, dlpd(o.idbeta), d2lpd(o.idbeta)] = lpdfgbeta(x(o.idbeta), o.p6(o.idbeta), o.p7(o.idbeta), o.p3(o.idbeta), o.p4(o.idbeta));
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lpd = lpd + sum(tmp);
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if isinf(lpd), return, end
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case 4
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[tmp, dlpd(o.idbeta), d2lpd(o.idbeta)] = lpdfgbeta(x(o.idbeta), o.p6(o.idbeta), o.p7(o.idbeta), o.p3(o.idbeta), o.p4(o.idbeta));
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lpd = lpd + sum(tmp);
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if isinf(lpd)
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info = o.idbeta(isinf(tmp));
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return
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end
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end
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end
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if o.isinvgamma1
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switch nargout
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case 1
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lpd = lpd + sum(lpdfig1(x(o.idinvgamma1)-o.p3(o.idinvgamma1), o.p6(o.idinvgamma1), o.p7(o.idinvgamma1)));
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if isinf(lpd), return, end
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case 2
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[tmp, dlpd(o.idinvgamma1)] = lpdfig1(x(o.idinvgamma1)-o.p3(o.idinvgamma1), o.p6(o.idinvgamma1), o.p7(o.idinvgamma1));
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lpd = lpd + sum(tmp);
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if isinf(lpd), return, end
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case 3
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[tmp, dlpd(o.idinvgamma1), d2lpd(o.idinvgamma1)] = lpdfig1(x(o.idinvgamma1)-o.p3(o.idinvgamma1), o.p6(o.idinvgamma1), o.p7(o.idinvgamma1));
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lpd = lpd + sum(tmp);
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if isinf(lpd), return, end
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case 4
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[tmp, dlpd(o.idinvgamma1), d2lpd(o.idinvgamma1)] = lpdfig1(x(o.idinvgamma1)-o.p3(o.idinvgamma1), o.p6(o.idinvgamma1), o.p7(o.idinvgamma1));
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lpd = lpd + sum(tmp);
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if isinf(lpd)
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info = o.idinvgamma1(isinf(tmp));
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return
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end
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end
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end
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if o.isinvgamma2
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switch nargout
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case 1
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lpd = lpd + sum(lpdfig2(x(o.idinvgamma2)-o.p3(o.idinvgamma2), o.p6(o.idinvgamma2), o.p7(o.idinvgamma2)));
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if isinf(lpd), return, end
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case 2
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[tmp, dlpd(o.idinvgamma2)] = lpdfig2(x(o.idinvgamma2)-o.p3(o.idinvgamma2), o.p6(o.idinvgamma2), o.p7(o.idinvgamma2));
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lpd = lpd + sum(tmp);
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if isinf(lpd), return, end
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case 3
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[tmp, dlpd(o.idinvgamma2), d2lpd(o.idinvgamma2)] = lpdfig2(x(o.idinvgamma2)-o.p3(o.idinvgamma2), o.p6(o.idinvgamma2), o.p7(o.idinvgamma2));
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lpd = lpd + sum(tmp);
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if isinf(lpd), return, end
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case 4
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[tmp, dlpd(o.idinvgamma2), d2lpd(o.idinvgamma2)] = lpdfig2(x(o.idinvgamma2)-o.p3(o.idinvgamma2), o.p6(o.idinvgamma2), o.p7(o.idinvgamma2));
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lpd = lpd + sum(tmp);
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if isinf(lpd)
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info = o.idinvgamma2(isinf(tmp));
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return
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end
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end
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end
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if o.isweibull
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switch nargout
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case 1
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lpd = lpd + sum(lpdfgweibull(x(o.idweibull), o.p6(o.idweibull), o.p7(o.idweibull)));
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if isinf(lpd), return, end
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case 2
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[tmp, dlpd(o.idweibull)] = lpdfgweibull(x(o.idweibull), o.p6(o.idweibull), o.p7(o.idweibull));
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lpd = lpd + sum(tmp);
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if isinf(lpd), return, end
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case 3
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[tmp, dlpd(o.idweibull), d2lpd(o.idweibull)] = lpdfgweibull(x(o.idweibull), o.p6(o.idweibull), o.p7(o.idweibull));
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lpd = lpd + sum(tmp);
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if isinf(lpd), return, end
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case 4
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[tmp, dlpd(o.idweibull), d2lpd(o.idweibull)] = lpdfgweibull(x(o.idweibull), o.p6(o.idweibull), o.p7(o.idweibull));
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lpd = lpd + sum(tmp);
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if isinf(lpd)
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info = o.idweibull(isinf(tmp));
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return
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end
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end
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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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BayesInfo.pshape = p0;
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BayesInfo.p1 = p1;
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BayesInfo.p2 = p2;
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BayesInfo.p3 = p3;
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BayesInfo.p4 = p4;
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BayesInfo.p5 = p5;
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BayesInfo.p6 = p6;
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BayesInfo.p7 = p7;
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% Call the tested routine
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try
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Prior = dprior(BayesInfo, prior_trunc, false);
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% Compute density at the prior mode
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lpdstar = Prior.density(p5);
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% Draw random deviates in a loop and evaluate the density.
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LPD = NaN(10000,1);
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parfor i = 1:10000
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x = Prior.draw();
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LPD(i) = Prior.density(x);
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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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%@test:2
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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);
|
||||
p5(i) = compute_prior_mode([p6(i) p7(i)], 6);
|
||||
case 8
|
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% Weibull distribution
|
||||
p3(i) = 0;
|
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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
|
||||
|
||||
BayesInfo.pshape = p0;
|
||||
BayesInfo.p1 = p1;
|
||||
BayesInfo.p2 = p2;
|
||||
BayesInfo.p3 = p3;
|
||||
BayesInfo.p4 = p4;
|
||||
BayesInfo.p5 = p5;
|
||||
BayesInfo.p6 = p6;
|
||||
BayesInfo.p7 = p7;
|
||||
|
||||
% Call the tested routine
|
||||
try
|
||||
Prior = dprior(BayesInfo, prior_trunc, false);
|
||||
mu = NaN(14,1);
|
||||
std = NaN(14,1);
|
||||
|
||||
for i=1:14
|
||||
% Define conditional density (it's also a marginal since priors are orthogonal)
|
||||
f = @(x) exp(Prior.densities(substitute(p5, i, x)));
|
||||
% TODO: Check the version of Octave we use (integral is available as a compatibility wrapper in latest Octave version)
|
||||
m = integral(f, p3(i), p4(i));
|
||||
density = @(x) f(x)/m; % rescaling is required since the probability mass depends on the conditioning.
|
||||
% Compute the conditional expectation
|
||||
mu(i) = integral(@(x) x.*density(x), p3(i), p4(i));
|
||||
std(i) = sqrt(integral(@(x) ((x-mu(i)).^2).*density(x), p3(i), p4(i)));
|
||||
end
|
||||
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
if t(1)
|
||||
t(2) = all(abs(mu-.4)<1e-6);
|
||||
t(3) = all(abs(std-.2)<1e-6);
|
||||
end
|
||||
T = all(t);
|
||||
%@eof:2
|
||||
|
|
@ -1,26 +1,48 @@
|
|||
classdef dprior
|
||||
classdef dprior < handle
|
||||
|
||||
% 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/>.
|
||||
|
||||
properties
|
||||
p6 = []; % Prior first hyperparameter.
|
||||
p7 = []; % Prior second hyperparameter.
|
||||
p1 = []; % Prior mean.
|
||||
p2 = []; % Prior stddev.
|
||||
p3 = []; % Lower bound of the prior support.
|
||||
p4 = []; % Upper bound of the prior support.
|
||||
p5 = []; % Prior mode.
|
||||
p6 = []; % Prior first hyperparameter.
|
||||
p7 = []; % Prior second hyperparameter.
|
||||
p11 = []; % Prior median
|
||||
lb = []; % Truncated prior lower bound.
|
||||
ub = []; % Truncated prior upper bound.
|
||||
uniform_index = []; % Index for the uniform priors.
|
||||
gaussian_index = []; % Index for the gaussian priors.
|
||||
gamma_index = []; % Index for the gamma priors.
|
||||
beta_index = []; % Index for the beta priors.
|
||||
inverse_gamma_1_index = []; % Index for the inverse gamma type 1 priors.
|
||||
inverse_gamma_2_index = []; % Index for the inverse gamma type 2 priors.
|
||||
weibull_index = []; % Index for the weibull priors.
|
||||
uniform_draws = false;
|
||||
gaussian_draws = false;
|
||||
gamma_draws = false;
|
||||
beta_draws = false;
|
||||
inverse_gamma_1_draws = false;
|
||||
inverse_gamma_2_draws = false;
|
||||
weibull_draws = false;
|
||||
name = {}; % Name of the parameter
|
||||
iduniform = []; % Index for the uniform priors.
|
||||
idgaussian = []; % Index for the gaussian priors.
|
||||
idgamma = []; % Index for the gamma priors.
|
||||
idbeta = []; % Index for the beta priors.
|
||||
idinvgamma1 = []; % Index for the inverse gamma type 1 priors.
|
||||
idinvgamma2 = []; % Index for the inverse gamma type 2 priors.
|
||||
idweibull = []; % Index for the weibull priors.
|
||||
isuniform = false;
|
||||
isgaussian = false;
|
||||
isgamma = false;
|
||||
isbeta = false;
|
||||
isinvgamma1 = false;
|
||||
isinvgamma2 = false;
|
||||
isweibull = false;
|
||||
end
|
||||
|
||||
methods
|
||||
|
|
@ -38,10 +60,37 @@ classdef dprior
|
|||
%
|
||||
% REQUIREMENTS
|
||||
% None.
|
||||
o.p6 = BayesInfo.p6;
|
||||
o.p7 = BayesInfo.p7;
|
||||
o.p3 = BayesInfo.p3;
|
||||
o.p4 = BayesInfo.p4;
|
||||
if ~nargin
|
||||
% Empty object
|
||||
return
|
||||
end
|
||||
if isfield(BayesInfo, 'p1')
|
||||
o.p1 = BayesInfo.p1;
|
||||
end
|
||||
if isfield(BayesInfo, 'p2')
|
||||
o.p2 = BayesInfo.p2;
|
||||
end
|
||||
if isfield(BayesInfo, 'p3')
|
||||
o.p3 = BayesInfo.p3;
|
||||
end
|
||||
if isfield(BayesInfo, 'p4')
|
||||
o.p4 = BayesInfo.p4;
|
||||
end
|
||||
if isfield(BayesInfo, 'p5')
|
||||
o.p5 = BayesInfo.p5;
|
||||
end
|
||||
if isfield(BayesInfo, 'p6')
|
||||
o.p6 = BayesInfo.p6;
|
||||
end
|
||||
if isfield(BayesInfo, 'p7')
|
||||
o.p7 = BayesInfo.p7;
|
||||
end
|
||||
if isfield(BayesInfo, 'p11')
|
||||
o.p11 = BayesInfo.p11;
|
||||
end
|
||||
if isfield(BayesInfo, 'name')
|
||||
o.name = BayesInfo.name;
|
||||
end
|
||||
bounds = prior_bounds(BayesInfo, PriorTrunc);
|
||||
o.lb = bounds.lb;
|
||||
o.ub = bounds.ub;
|
||||
|
|
@ -50,138 +99,38 @@ classdef dprior
|
|||
else
|
||||
prior_shape = BayesInfo.pshape;
|
||||
end
|
||||
o.beta_index = find(prior_shape==1);
|
||||
if ~isempty(o.beta_index)
|
||||
o.beta_draws = true;
|
||||
o.idbeta = find(prior_shape==1);
|
||||
if ~isempty(o.idbeta)
|
||||
o.isbeta = true;
|
||||
end
|
||||
o.gamma_index = find(prior_shape==2);
|
||||
if ~isempty(o.gamma_index)
|
||||
o.gamma_draws = true;
|
||||
o.idgamma = find(prior_shape==2);
|
||||
if ~isempty(o.idgamma)
|
||||
o.isgamma = true;
|
||||
end
|
||||
o.gaussian_index = find(prior_shape==3);
|
||||
if ~isempty(o.gaussian_index)
|
||||
o.gaussian_draws = true;
|
||||
o.idgaussian = find(prior_shape==3);
|
||||
if ~isempty(o.idgaussian)
|
||||
o.isgaussian = true;
|
||||
end
|
||||
o.inverse_gamma_1_index = find(prior_shape==4);
|
||||
if ~isempty(o.inverse_gamma_1_index)
|
||||
o.inverse_gamma_1_draws = true;
|
||||
o.idinvgamma1 = find(prior_shape==4);
|
||||
if ~isempty(o.idinvgamma1)
|
||||
o.isinvgamma1 = true;
|
||||
end
|
||||
o.uniform_index = find(prior_shape==5);
|
||||
if ~isempty(o.uniform_index)
|
||||
o.uniform_draws = true;
|
||||
o.iduniform = find(prior_shape==5);
|
||||
if ~isempty(o.iduniform)
|
||||
o.isuniform = true;
|
||||
end
|
||||
o.inverse_gamma_2_index = find(prior_shape==6);
|
||||
if ~isempty(o.inverse_gamma_2_index)
|
||||
o.inverse_gamma_2_draws = true;
|
||||
o.idinvgamma2 = find(prior_shape==6);
|
||||
if ~isempty(o.idinvgamma2)
|
||||
o.isinvgamma2 = true;
|
||||
end
|
||||
o.weibull_index = find(prior_shape==8);
|
||||
if ~isempty(o.weibull_index)
|
||||
o.weibull_draws = true;
|
||||
o.idweibull = find(prior_shape==8);
|
||||
if ~isempty(o.idweibull)
|
||||
o.isweibull = true;
|
||||
end
|
||||
end
|
||||
|
||||
function p = draw(o)
|
||||
% Return a random draw from the prior distribution.
|
||||
%
|
||||
% INPUTS
|
||||
% - o [dprior]
|
||||
%
|
||||
% OUTPUTS
|
||||
% - p [double] m×1 vector, random draw from the prior distribution (m is the number of estimated parameters).
|
||||
%
|
||||
% REMARKS
|
||||
% None.
|
||||
%
|
||||
% EXAMPLE
|
||||
%
|
||||
% >> Prior = dprior(bayestopt_, options_.prior_trunc);
|
||||
% >> d = Prior.draw()
|
||||
p = NaN(rows(o.lb), 1);
|
||||
if o.uniform_draws
|
||||
p(o.uniform_index) = rand(length(o.uniform_index),1).*(o.p4(o.uniform_index)-o.p3(o.uniform_index)) + o.p3(o.uniform_index);
|
||||
out_of_bound = find( (p(o.uniform_index)>o.ub(o.uniform_index)) | (p(o.uniform_index)<o.lb(o.uniform_index)));
|
||||
while ~isempty(out_of_bound)
|
||||
p(o.uniform_index) = rand(length(o.uniform_index), 1).*(o.p4(o.uniform_index)-o.p3(o.uniform_index)) + o.p3(o.uniform_index);
|
||||
out_of_bound = find( (p(o.uniform_index)>o.ub(o.uniform_index)) | (p(o.uniform_index)<o.lb(o.uniform_index)));
|
||||
end
|
||||
end
|
||||
if o.gaussian_draws
|
||||
p(o.gaussian_index) = randn(length(o.gaussian_index), 1).*o.p7(o.gaussian_index) + o.p6(o.gaussian_index);
|
||||
out_of_bound = find( (p(o.gaussian_index)>o.ub(o.gaussian_index)) | (p(o.gaussian_index)<o.lb(o.gaussian_index)));
|
||||
while ~isempty(out_of_bound)
|
||||
p(o.gaussian_index(out_of_bound)) = randn(length(o.gaussian_index(out_of_bound)), 1).*o.p7(o.gaussian_index(out_of_bound)) + o.p6(o.gaussian_index(out_of_bound));
|
||||
out_of_bound = find( (p(o.gaussian_index)>o.ub(o.gaussian_index)) | (p(o.gaussian_index)<o.lb(o.gaussian_index)));
|
||||
end
|
||||
end
|
||||
if o.gamma_draws
|
||||
p(o.gamma_index) = gamrnd(o.p6(o.gamma_index), o.p7(o.gamma_index))+o.p3(o.gamma_index);
|
||||
out_of_bound = find( (p(o.gamma_index)>o.ub(o.gamma_index)) | (p(o.gamma_index)<o.lb(o.gamma_index)));
|
||||
while ~isempty(out_of_bound)
|
||||
p(o.gamma_index(out_of_bound)) = gamrnd(o.p6(o.gamma_index(out_of_bound)), o.p7(o.gamma_index(out_of_bound)))+o.p3(o.gamma_index(out_of_bound));
|
||||
out_of_bound = find( (p(o.gamma_index)>o.ub(o.gamma_index)) | (p(o.gamma_index)<o.lb(o.gamma_index)));
|
||||
end
|
||||
end
|
||||
if o.beta_draws
|
||||
p(o.beta_index) = (o.p4(o.beta_index)-o.p3(o.beta_index)).*betarnd(o.p6(o.beta_index), o.p7(o.beta_index))+o.p3(o.beta_index);
|
||||
out_of_bound = find( (p(o.beta_index)>o.ub(o.beta_index)) | (p(o.beta_index)<o.lb(o.beta_index)));
|
||||
while ~isempty(out_of_bound)
|
||||
p(o.beta_index(out_of_bound)) = (o.p4(o.beta_index(out_of_bound))-o.p3(o.beta_index(out_of_bound))).*betarnd(o.p6(o.beta_index(out_of_bound)), o.p7(o.beta_index(out_of_bound)))+o.p3(o.beta_index(out_of_bound));
|
||||
out_of_bound = find( (p(o.beta_index)>o.ub(o.beta_index)) | (p(o.beta_index)<o.lb(o.beta_index)));
|
||||
end
|
||||
end
|
||||
if o.inverse_gamma_1_draws
|
||||
p(o.inverse_gamma_1_index) = ...
|
||||
sqrt(1./gamrnd(o.p7(o.inverse_gamma_1_index)/2, 2./o.p6(o.inverse_gamma_1_index)))+o.p3(o.inverse_gamma_1_index);
|
||||
out_of_bound = find( (p(o.inverse_gamma_1_index)>o.ub(o.inverse_gamma_1_index)) | (p(o.inverse_gamma_1_index)<o.lb(o.inverse_gamma_1_index)));
|
||||
while ~isempty(out_of_bound)
|
||||
p(o.inverse_gamma_1_index(out_of_bound)) = ...
|
||||
sqrt(1./gamrnd(o.p7(o.inverse_gamma_1_index(out_of_bound))/2, 2./o.p6(o.inverse_gamma_1_index(out_of_bound))))+o.p3(o.inverse_gamma_1_index(out_of_bound));
|
||||
out_of_bound = find( (p(o.inverse_gamma_1_index)>o.ub(o.inverse_gamma_1_index)) | (p(o.inverse_gamma_1_index)<o.lb(o.inverse_gamma_1_index)));
|
||||
end
|
||||
end
|
||||
if o.inverse_gamma_2_draws
|
||||
p(o.inverse_gamma_2_index) = ...
|
||||
1./gamrnd(o.p7(o.inverse_gamma_2_index)/2, 2./o.p6(o.inverse_gamma_2_index))+o.p3(o.inverse_gamma_2_index);
|
||||
out_of_bound = find( (p(o.inverse_gamma_2_index)>o.ub(o.inverse_gamma_2_index)) | (p(o.inverse_gamma_2_index)<o.lb(o.inverse_gamma_2_index)));
|
||||
while ~isempty(out_of_bound)
|
||||
p(o.inverse_gamma_2_index(out_of_bound)) = ...
|
||||
1./gamrnd(o.p7(o.inverse_gamma_2_index(out_of_bound))/2, 2./o.p6(o.inverse_gamma_2_index(out_of_bound)))+o.p3(o.inverse_gamma_2_index(out_of_bound));
|
||||
out_of_bound = find( (p(o.inverse_gamma_2_index)>o.ub(o.inverse_gamma_2_index)) | (p(o.inverse_gamma_2_index)<o.lb(o.inverse_gamma_2_index)));
|
||||
end
|
||||
end
|
||||
if o.weibull_draws
|
||||
p(o.weibull_index) = wblrnd(o.p7(o.weibull_index), o.p6(o.weibull_index)) + o.p3(o.weibull_index);
|
||||
out_of_bound = find( (p(o.weibull_index)>o.ub(o.weibull_index)) | (p(o.weibull_index)<o.lb(o.weibull_index)));
|
||||
while ~isempty(out_of_bound)
|
||||
p(o.weibull_index(out_of_bound)) = wblrnd(o.p7(o.weibull_index(out_of_bound)), o.p6(o.weibull_index(out_of_bound)))+o.p3(o.weibull_index(out_of_bound));
|
||||
out_of_bound = find( (p(o.weibull_index)>o.ub(o.weibull_index)) | (p(o.weibull_index)<o.lb(o.weibull_index)));
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function P = draws(o, n)
|
||||
% Return n independent random draws from the prior distribution.
|
||||
%
|
||||
% INPUTS
|
||||
% - o [dprior]
|
||||
%
|
||||
% OUTPUTS
|
||||
% - P [double] m×n matrix, random draw from the prior distribution.
|
||||
%
|
||||
% REMARKS
|
||||
% If the Parallel Computing Toolbox is available, the main loop is run in parallel.
|
||||
%
|
||||
% EXAMPLE
|
||||
%
|
||||
% >> Prior = dprior(bayestopt_, options_.prior_trunc);
|
||||
% >> Prior.draws(1e6)
|
||||
P = NaN(rows(o.lb), 1);
|
||||
parfor i=1:n
|
||||
P(:,i) = draw(o);
|
||||
end
|
||||
end
|
||||
end % dprior (constructor)
|
||||
|
||||
end % methods
|
||||
|
||||
end % classdef --*-- Unit tests --*--
|
||||
|
||||
%@test:1
|
||||
|
|
@ -263,114 +212,22 @@ end % classdef --*-- Unit tests --*--
|
|||
%$ try
|
||||
%$ % Instantiate dprior object
|
||||
%$ o = dprior(BayesInfo, prior_trunc, false);
|
||||
%$ % Do simulations in a loop and estimate recursively the mean and the variance.
|
||||
%$ for i = 1:ndraws
|
||||
%$ draw = o.draw();
|
||||
%$ m1 = m0 + (draw-m0)/i;
|
||||
%$ m2 = m1*m1';
|
||||
%$ v0 = v0 + ((draw*draw'-m2-v0) + (i-1)*(m0*m0'-m2'))/i;
|
||||
%$ m0 = m1;
|
||||
%$ end
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$ t(2) = all(abs(m0-BayesInfo.p1)<3e-3);
|
||||
%$ t(3) = all(all(abs(v0-diag(BayesInfo.p2.^2))<5e-3));
|
||||
%$ end
|
||||
%$ T = all(t);
|
||||
%@eof:1
|
||||
|
||||
%@test:2
|
||||
%$ % 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
|
||||
%$
|
||||
%$ BayesInfo.pshape = p0;
|
||||
%$ BayesInfo.p1 = p1;
|
||||
%$ BayesInfo.p2 = p2;
|
||||
%$ BayesInfo.p3 = p3;
|
||||
%$ BayesInfo.p4 = p4;
|
||||
%$ BayesInfo.p5 = p5;
|
||||
%$ BayesInfo.p6 = p6;
|
||||
%$ BayesInfo.p7 = p7;
|
||||
%$
|
||||
%$ ndraws = 1e5;
|
||||
%$
|
||||
%$ % Call the tested routine
|
||||
%$ try
|
||||
%$ % Instantiate dprior object.
|
||||
%$ o = dprior(BayesInfo, prior_trunc, false);
|
||||
%$ X = o.draws(ndraws);
|
||||
%$ m = mean(X, 2);
|
||||
%$ v = var(X, 0, 2);
|
||||
%$ % Instantiate dprior object
|
||||
%$ o = dprior();
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$ t(2) = all(abs(m-BayesInfo.p1)<3e-3);
|
||||
%$ t(3) = all(all(abs(v-BayesInfo.p2.^2)<5e-3));
|
||||
%$ end
|
||||
%$ T = all(t);
|
||||
%@eof:2
|
||||
|
|
|
|||
|
|
@ -0,0 +1,197 @@
|
|||
function p = draw(o)
|
||||
|
||||
% Return a random draw from the prior distribution.
|
||||
%
|
||||
% INPUTS
|
||||
% - o [dprior]
|
||||
%
|
||||
% OUTPUTS
|
||||
% - p [double] m×1 vector, random draw from the prior distribution (m is the number of estimated parameters).
|
||||
%
|
||||
% REMARKS
|
||||
% None.
|
||||
%
|
||||
% EXAMPLE
|
||||
%
|
||||
% >> Prior = dprior(bayestopt_, options_.prior_trunc);
|
||||
% >> d = Prior.draw()
|
||||
|
||||
% 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/>.
|
||||
|
||||
p = NaN(rows(o.lb), 1);
|
||||
if o.isuniform
|
||||
p(o.iduniform) = rand(length(o.iduniform),1).*(o.p4(o.iduniform)-o.p3(o.iduniform)) + o.p3(o.iduniform);
|
||||
oob = find( (p(o.iduniform)>o.ub(o.iduniform)) | (p(o.iduniform)<o.lb(o.iduniform)));
|
||||
while ~isempty(oob)
|
||||
p(o.iduniform) = rand(length(o.iduniform), 1).*(o.p4(o.iduniform)-o.p3(o.iduniform)) + o.p3(o.iduniform);
|
||||
oob = find( (p(o.iduniform)>o.ub(o.iduniform)) | (p(o.iduniform)<o.lb(o.iduniform)));
|
||||
end
|
||||
end
|
||||
if o.isgaussian
|
||||
p(o.idgaussian) = randn(length(o.idgaussian), 1).*o.p7(o.idgaussian) + o.p6(o.idgaussian);
|
||||
oob = find( (p(o.idgaussian)>o.ub(o.idgaussian)) | (p(o.idgaussian)<o.lb(o.idgaussian)));
|
||||
while ~isempty(oob)
|
||||
p(o.idgaussian(oob)) = randn(length(o.idgaussian(oob)), 1).*o.p7(o.idgaussian(oob)) + o.p6(o.idgaussian(oob));
|
||||
oob = find( (p(o.idgaussian)>o.ub(o.idgaussian)) | (p(o.idgaussian)<o.lb(o.idgaussian)));
|
||||
end
|
||||
end
|
||||
if o.isgamma
|
||||
p(o.idgamma) = gamrnd(o.p6(o.idgamma), o.p7(o.idgamma))+o.p3(o.idgamma);
|
||||
oob = find( (p(o.idgamma)>o.ub(o.idgamma)) | (p(o.idgamma)<o.lb(o.idgamma)));
|
||||
while ~isempty(oob)
|
||||
p(o.idgamma(oob)) = gamrnd(o.p6(o.idgamma(oob)), o.p7(o.idgamma(oob)))+o.p3(o.idgamma(oob));
|
||||
oob = find( (p(o.idgamma)>o.ub(o.idgamma)) | (p(o.idgamma)<o.lb(o.idgamma)));
|
||||
end
|
||||
end
|
||||
if o.isbeta
|
||||
p(o.idbeta) = (o.p4(o.idbeta)-o.p3(o.idbeta)).*betarnd(o.p6(o.idbeta), o.p7(o.idbeta))+o.p3(o.idbeta);
|
||||
oob = find( (p(o.idbeta)>o.ub(o.idbeta)) | (p(o.idbeta)<o.lb(o.idbeta)));
|
||||
while ~isempty(oob)
|
||||
p(o.idbeta(oob)) = (o.p4(o.idbeta(oob))-o.p3(o.idbeta(oob))).*betarnd(o.p6(o.idbeta(oob)), o.p7(o.idbeta(oob)))+o.p3(o.idbeta(oob));
|
||||
oob = find( (p(o.idbeta)>o.ub(o.idbeta)) | (p(o.idbeta)<o.lb(o.idbeta)));
|
||||
end
|
||||
end
|
||||
if o.isinvgamma1
|
||||
p(o.idinvgamma1) = ...
|
||||
sqrt(1./gamrnd(o.p7(o.idinvgamma1)/2, 2./o.p6(o.idinvgamma1)))+o.p3(o.idinvgamma1);
|
||||
oob = find( (p(o.idinvgamma1)>o.ub(o.idinvgamma1)) | (p(o.idinvgamma1)<o.lb(o.idinvgamma1)));
|
||||
while ~isempty(oob)
|
||||
p(o.idinvgamma1(oob)) = ...
|
||||
sqrt(1./gamrnd(o.p7(o.idinvgamma1(oob))/2, 2./o.p6(o.idinvgamma1(oob))))+o.p3(o.idinvgamma1(oob));
|
||||
oob = find( (p(o.idinvgamma1)>o.ub(o.idinvgamma1)) | (p(o.idinvgamma1)<o.lb(o.idinvgamma1)));
|
||||
end
|
||||
end
|
||||
if o.isinvgamma2
|
||||
p(o.idinvgamma2) = ...
|
||||
1./gamrnd(o.p7(o.idinvgamma2)/2, 2./o.p6(o.idinvgamma2))+o.p3(o.idinvgamma2);
|
||||
oob = find( (p(o.idinvgamma2)>o.ub(o.idinvgamma2)) | (p(o.idinvgamma2)<o.lb(o.idinvgamma2)));
|
||||
while ~isempty(oob)
|
||||
p(o.idinvgamma2(oob)) = ...
|
||||
1./gamrnd(o.p7(o.idinvgamma2(oob))/2, 2./o.p6(o.idinvgamma2(oob)))+o.p3(o.idinvgamma2(oob));
|
||||
oob = find( (p(o.idinvgamma2)>o.ub(o.idinvgamma2)) | (p(o.idinvgamma2)<o.lb(o.idinvgamma2)));
|
||||
end
|
||||
end
|
||||
if o.isweibull
|
||||
p(o.idweibull) = wblrnd(o.p7(o.idweibull), o.p6(o.idweibull)) + o.p3(o.idweibull);
|
||||
oob = find( (p(o.idweibull)>o.ub(o.idweibull)) | (p(o.idweibull)<o.lb(o.idweibull)));
|
||||
while ~isempty(oob)
|
||||
p(o.idweibull(oob)) = wblrnd(o.p7(o.idweibull(oob)), o.p6(o.idweibull(oob)))+o.p3(o.idweibull(oob));
|
||||
oob = find( (p(o.idweibull)>o.ub(o.idweibull)) | (p(o.idweibull)<o.lb(o.idweibull)));
|
||||
end
|
||||
end
|
||||
|
||||
return % --*-- Unit tests --*--
|
||||
|
||||
%@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
|
||||
|
||||
BayesInfo.pshape = p0;
|
||||
BayesInfo.p1 = p1;
|
||||
BayesInfo.p2 = p2;
|
||||
BayesInfo.p3 = p3;
|
||||
BayesInfo.p4 = p4;
|
||||
BayesInfo.p5 = p5;
|
||||
BayesInfo.p6 = p6;
|
||||
BayesInfo.p7 = p7;
|
||||
|
||||
ndraws = 1e5;
|
||||
m0 = BayesInfo.p1; %zeros(14,1);
|
||||
v0 = diag(BayesInfo.p2.^2); %zeros(14);
|
||||
|
||||
% Call the tested routine
|
||||
try
|
||||
% Instantiate dprior object
|
||||
o = dprior(BayesInfo, prior_trunc, false);
|
||||
% Do simulations in a loop and estimate recursively the mean and the variance.
|
||||
for i = 1:ndraws
|
||||
draw = o.draw();
|
||||
m1 = m0 + (draw-m0)/i;
|
||||
m2 = m1*m1';
|
||||
v0 = v0 + ((draw*draw'-m2-v0) + (i-1)*(m0*m0'-m2'))/i;
|
||||
m0 = m1;
|
||||
end
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
if t(1)
|
||||
t(2) = all(abs(m0-BayesInfo.p1)<3e-3);
|
||||
t(3) = all(all(abs(v0-diag(BayesInfo.p2.^2))<5e-3));
|
||||
end
|
||||
T = all(t);
|
||||
%@eof:1
|
||||
|
|
@ -0,0 +1,133 @@
|
|||
function P = draws(o, n)
|
||||
|
||||
% Return n independent random draws from the prior distribution.
|
||||
%
|
||||
% INPUTS
|
||||
% - o [dprior]
|
||||
%
|
||||
% OUTPUTS
|
||||
% - P [double] m×n matrix, random draw from the prior distribution.
|
||||
%
|
||||
% REMARKS
|
||||
% If the Parallel Computing Toolbox is available, the main loop is run in parallel.
|
||||
%
|
||||
% EXAMPLE
|
||||
%
|
||||
% >> Prior = dprior(bayestopt_, options_.prior_trunc);
|
||||
% >> Prior.draws(1e6)
|
||||
|
||||
% 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/>.
|
||||
|
||||
P = NaN(rows(o.lb), 1);
|
||||
parfor i=1:n
|
||||
P(:,i) = draw(o);
|
||||
end
|
||||
|
||||
return % --*-- Unit tests --*--
|
||||
|
||||
%@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
|
||||
|
||||
BayesInfo.pshape = p0;
|
||||
BayesInfo.p1 = p1;
|
||||
BayesInfo.p2 = p2;
|
||||
BayesInfo.p3 = p3;
|
||||
BayesInfo.p4 = p4;
|
||||
BayesInfo.p5 = p5;
|
||||
BayesInfo.p6 = p6;
|
||||
BayesInfo.p7 = p7;
|
||||
|
||||
ndraws = 1e5;
|
||||
|
||||
% Call the tested routine
|
||||
try
|
||||
% Instantiate dprior object.
|
||||
o = dprior(BayesInfo, prior_trunc, false);
|
||||
X = o.draws(ndraws);
|
||||
m = mean(X, 2);
|
||||
v = var(X, 0, 2);
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
if t(1)
|
||||
t(2) = all(abs(m-BayesInfo.p1)<3e-3);
|
||||
t(3) = all(all(abs(v-BayesInfo.p2.^2)<5e-3));
|
||||
end
|
||||
T = all(t);
|
||||
%@eof:1
|
||||
|
|
@ -0,0 +1,42 @@
|
|||
function m = mean(o, resetmoments)
|
||||
|
||||
% Return the prior mean.
|
||||
%
|
||||
% INPUTS
|
||||
% - o [dprior]
|
||||
% - resetmoments [logical] Force the computation of the prior mean
|
||||
%
|
||||
% OUTPUTS
|
||||
% - m [double] n×1 vector, prior mean
|
||||
|
||||
% 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/>.
|
||||
|
||||
if nargin<2
|
||||
resetmoments = false;
|
||||
end
|
||||
|
||||
if any(isnan(o.p1))
|
||||
resetmoments = true;
|
||||
end
|
||||
|
||||
if resetmoments
|
||||
o.p1 = NaN(size(o.p1));
|
||||
o.moments('mean');
|
||||
end
|
||||
|
||||
m = o.p1;
|
||||
|
|
@ -0,0 +1,42 @@
|
|||
function m = median(o, resetmoments)
|
||||
|
||||
% Return the prior median.
|
||||
%
|
||||
% INPUTS
|
||||
% - o [dprior]
|
||||
% - resetmoments [logical] Force the computation of the prior median
|
||||
%
|
||||
% OUTPUTS
|
||||
% - m [double] n×1 vector, prior median
|
||||
|
||||
% 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/>.
|
||||
|
||||
if nargin<2
|
||||
resetmoments = false;
|
||||
end
|
||||
|
||||
if any(isnan(o.p11))
|
||||
resetmoments = true;
|
||||
end
|
||||
|
||||
if resetmoments
|
||||
o.p11 = NaN(size(o.p11));
|
||||
o.moments('median');
|
||||
end
|
||||
|
||||
m = o.p11;
|
||||
|
|
@ -0,0 +1,42 @@
|
|||
function m = mode(o, resetmoments)
|
||||
|
||||
% Return the prior mode.
|
||||
%
|
||||
% INPUTS
|
||||
% - o [dprior]
|
||||
% - resetmoments [logical] Force the computation of the prior mode
|
||||
%
|
||||
% OUTPUTS
|
||||
% - m [double] n×1 vector, prior mode
|
||||
|
||||
% 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/>.
|
||||
|
||||
if nargin<2
|
||||
resetmoments = false;
|
||||
end
|
||||
|
||||
if any(isnan(o.p5))
|
||||
resetmoments = true;
|
||||
end
|
||||
|
||||
if resetmoments
|
||||
o.p5 = NaN(size(o.p5));
|
||||
o.moments('mode');
|
||||
end
|
||||
|
||||
m = o.p5;
|
||||
|
|
@ -0,0 +1,291 @@
|
|||
function o = moments(o, name)
|
||||
|
||||
% Compute the prior moments.
|
||||
%
|
||||
% INPUTS
|
||||
% - o [dprior]
|
||||
%
|
||||
% OUTPUTS
|
||||
% - o [dprior]
|
||||
|
||||
% 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 name
|
||||
case 'mean'
|
||||
m = o.p1;
|
||||
case 'median'
|
||||
m = o.p11;
|
||||
case 'std'
|
||||
m = o.p2;
|
||||
case 'mode'
|
||||
m = o.p5;
|
||||
otherwise
|
||||
error('%s is not an implemented moemnt.', name)
|
||||
end
|
||||
id = isnan(m);
|
||||
if any(id)
|
||||
% For some parameters the prior mean is not defined.
|
||||
% We compute the first order moment from the
|
||||
% hyperparameters, if the hyperparameters are not
|
||||
% available an error is thrown.
|
||||
if o.isuniform
|
||||
jd = intersect(o.iduniform, find(id));
|
||||
if ~isempty(jd)
|
||||
if any(isnan(o.p3(jd))) || any(isnan(o.p4(jd)))
|
||||
error('dprior::mean: Some hyperparameters are missing (uniform distribution).')
|
||||
end
|
||||
switch name
|
||||
case 'mean'
|
||||
m(jd) = o.p3(jd) + .5*(o.p4(jd)-o.p3(jd));
|
||||
case 'median'
|
||||
m(jd) = o.p3(jd) + .5*(o.p4(jd)-o.p3(jd));
|
||||
case 'std'
|
||||
m(jd) = (o.p4(jd)-o.p3(jd))/sqrt(12);
|
||||
case 'mode' % Actually we have a continuum of modes with the uniform distribution.
|
||||
m(jd) = o.p3(jd) + .5*(o.p4(jd)-o.p3(jd));
|
||||
end
|
||||
end
|
||||
end
|
||||
if o.isgaussian
|
||||
jd = intersect(o.idgaussian, find(id));
|
||||
if ~isempty(jd)
|
||||
if any(isnan(o.p6(jd))) || any(isnan(o.p7(jd)))
|
||||
error('dprior::mean: Some hyperparameters are missing (gaussian distribution).')
|
||||
end
|
||||
switch name
|
||||
case 'mean'
|
||||
m(jd) = o.p6(jd);
|
||||
case 'median'
|
||||
m(jd) = o.p6(jd);
|
||||
case 'std'
|
||||
m(jd) = o.p7(jd);
|
||||
case 'mode' % Actually we have a continuum of modes with the uniform distribution.
|
||||
m(jd) = o.p6(jd);
|
||||
end
|
||||
end
|
||||
end
|
||||
if o.isgamma
|
||||
jd = intersect(o.idgamma, find(id));
|
||||
if ~isempty(jd)
|
||||
if any(isnan(o.p6(jd))) || any(isnan(o.p7(jd))) || any(isnan(o.p3(jd)))
|
||||
error('dprior::mean: Some hyperparameters are missing (gamma distribution).')
|
||||
end
|
||||
% α → o.p6, β → o.p7
|
||||
switch name
|
||||
case 'mean'
|
||||
m(jd) = o.p3(jd) + o.p6(jd).*o.p7(jd);
|
||||
case 'median'
|
||||
m(jd) = o.p3(jd) + gaminv(.5, o.p6(jd), o.p7(jda));
|
||||
case 'std'
|
||||
m(jd) = sqrt(o.p6(jd)).*o.p7(jd);
|
||||
case 'mode'
|
||||
m(jd) = 0;
|
||||
hd = o.p6(jd)>1;
|
||||
m(jd(hd)) = (o.p6(jd(hd))-1).*o.p7(jd(hd));
|
||||
end
|
||||
end
|
||||
end
|
||||
if o.isbeta
|
||||
jd = intersect(o.idbeta, find(id));
|
||||
if ~isempty(jd)
|
||||
if any(isnan(o.p6(jd))) || any(isnan(o.p7(jd))) || any(isnan(o.p3(jd))) || any(isnan(o.p4(jd)))
|
||||
error('dprior::mean: Some hyperparameters are missing (beta distribution).')
|
||||
end
|
||||
% α → o.p6, β → o.p7
|
||||
switch name
|
||||
case 'mean'
|
||||
m(jd) = o.p3(jd) + (o.p6(jd)./(o.p6(jd)+o.p7(jd))).*(o.p4(jd)-o.p3(jd));
|
||||
case 'median'
|
||||
m(jd) = o.p3(jd) + betainv(.5, o.p6(jd), o.p7(jd)).*(o.p4(jd)-o.p3(jd));
|
||||
case 'std'
|
||||
m(jd) = (o.p4(jd)-o.p3(jd)).*sqrt(o.p6(jd).*o.p7(jd)./((o.p6(jd)+o.p7(jd)).^2.*(o.p6(jd)+o.p7(jd)+1)));
|
||||
case 'mode'
|
||||
h0 = true(jd, 1);
|
||||
h1 = o.p6(jd)<=1 & o.p7(jd)>1; h0 = h0 & ~h1;
|
||||
h2 = o.p7(jd)<=1 & o.p6(jd)>1; h0 = h0 & ~h2;
|
||||
h3 = o.p6(jd)<1 & o.p7(jd)<1; h0 = h0 & ~h3;
|
||||
h4 = ismembertol(o.p6(jd), 1) & ismembertol(o.p7(jd),1); h0 = h0 & ~h4;
|
||||
m(jd(h1)) = o.p3(jd(h1)); % Standard β has a mode at 0
|
||||
m(jd(h2)) = o.p4(jd(h2)); % Standard β has a mode at 1
|
||||
m(jd(h3)) = o.p3(jd(h3)); % Standard β is bimodal, we pick the lowest mode (0)
|
||||
m(jd(h4)) = o.p3(jd(h4)) + .5*(o.p4(jd(h4))-o.p3(jd(h4))); % Standard β is the uniform distribution (continuum of modes), we pick the mean as the mode
|
||||
m(jd(h0)) = o.p3(jd(h0))+(o.p4(jd(h0))-o.p3(jd(h0))).*((o.p6(jd(h0))-1)./(o.p6(jd(h0))+o.p7(jd(h0))-2)); % β distribution is concave and has a unique interior mode.
|
||||
end
|
||||
end
|
||||
end
|
||||
if o.isinvgamma1
|
||||
jd = intersect(o.idinvgamma1, find(id));
|
||||
if ~isempty(jd)
|
||||
if any(isnan(o.p6(jd))) || any(isnan(o.p7(jd))) || any(isnan(o.p3(jd)))
|
||||
error('dprior::mean: Some hyperparameters are missing (inverse gamma type 1 distribution).')
|
||||
end
|
||||
% s → o.p6, ν → o.p7
|
||||
switch name
|
||||
case 'mean'
|
||||
m(jd) = o.p3(jd) + sqrt(.5*o.p6(jd)) .*(gamma(.5*(o.p7(jd)-1))./gamma(.5*o.p7(jd)));
|
||||
case 'median'
|
||||
m(jd) = o.p3(jd) + 1.0/sqrt(gaminv(.5, o.p7(jd)/2.0, 2.0/o.p6(jd)));
|
||||
case 'std'
|
||||
m(jd) = sqrt( o.p6(jd)./(o.p7(jd)-2)-(.5*o.p6(jd)).*(gamma(.5*(o.p7(jd)-1))./gamma(.5*o.p7(jd))).^2);
|
||||
case 'mode'
|
||||
m(jd) = sqrt((o.p7(jd)-1)./o.p6(jd));
|
||||
end
|
||||
end
|
||||
end
|
||||
if o.isinvgamma2
|
||||
jd = intersect(o.idinvgamma2, find(id));
|
||||
if ~isempty(jd)
|
||||
if any(isnan(o.p6(jd))) || any(isnan(o.p7(jd))) || any(isnan(o.p3(jd)))
|
||||
error('dprior::mean: Some hyperparameters are missing (inverse gamma type 2 distribution).')
|
||||
end
|
||||
% s → o.p6, ν → o.p7
|
||||
switch name
|
||||
case 'mean'
|
||||
m(jd) = o.p3(jd) + o.p6(jd)./(o.p7(jd)-2);
|
||||
case 'median'
|
||||
m(jd) = o.p3(jd) + 1.0/gaminv(.5, o.p7(jd)/2.0, 2.0/o.p6(jd));
|
||||
case 'std'
|
||||
m(jd) = sqrt(2./(o.p7(jd)-4)).*o.p6(jd)./(o.p7(jd)-2);
|
||||
case 'mode'
|
||||
m(jd) = o.p6(jd)./(o.p7(jd)+2);
|
||||
end
|
||||
end
|
||||
end
|
||||
if o.isweibull
|
||||
jd = intersect(o.idweibull, find(id));
|
||||
if ~isempty(jd)
|
||||
if any(isnan(o.p6(jd))) || any(isnan(o.p7(jd))) || any(isnan(o.p3(jd)))
|
||||
error('dprior::mean: Some hyperparameters are missing (weibull distribution).')
|
||||
end
|
||||
% k → o.p6 (shape parameter), λ → o.p7 (scale parameter)
|
||||
% See https://en.wikipedia.org/wiki/Weibull_distribution
|
||||
switch name
|
||||
case 'mean'
|
||||
m(jd) = o.p3(jd) + o.p7(jd).*gamma(1+1./o.p6(jd));
|
||||
case 'median'
|
||||
m(jd) = o.p3(jd) + o.p7(jd).*log(2).^(1./o.p6(jd));
|
||||
case 'std'
|
||||
m(jd) = o.p7(jd).*sqrt(gamma(1+2./o.p6(jd))-gamma(1+1./o.p6(jd)).^2);
|
||||
case 'mode'
|
||||
m(jd) = 0;
|
||||
hd = o.p6(jd)>1;
|
||||
m(jd(hd)) = o.p3(jd(hd)) + o.p7(jd(hd)).*((o.p6(jd(hd))-1)./o.p6(jd(hd))).^(1./o.p6(jd(hd)));
|
||||
end
|
||||
end
|
||||
end
|
||||
switch name
|
||||
case 'mean'
|
||||
o.p1 = m;
|
||||
case 'median'
|
||||
o.p11 = m;
|
||||
case 'std'
|
||||
o.p2 = m;
|
||||
case 'mode'
|
||||
o.p5 = m;
|
||||
end
|
||||
end
|
||||
|
||||
return % --*-- Unit tests --*--
|
||||
|
||||
%@test:5
|
||||
% 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
|
||||
|
||||
BayesInfo.pshape = p0;
|
||||
BayesInfo.p1 = p1;
|
||||
BayesInfo.p2 = p2;
|
||||
BayesInfo.p3 = p3;
|
||||
BayesInfo.p4 = p4;
|
||||
BayesInfo.p5 = p5;
|
||||
BayesInfo.p6 = p6;
|
||||
BayesInfo.p7 = p7;
|
||||
|
||||
% Call the tested routine
|
||||
try
|
||||
Prior = dprior(BayesInfo, prior_trunc, false);
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
if t(1)
|
||||
t(2) = all(Prior.mean()==.4);
|
||||
t(3) = all(ismembertol(Prior.mean(true),.4));
|
||||
t(4) = all(ismembertol(Prior.variance(),.04));
|
||||
t(5) = all(ismembertol(Prior.variance(true),.04));
|
||||
end
|
||||
T = all(t);
|
||||
%@eof:5
|
||||
|
|
@ -0,0 +1,41 @@
|
|||
function p = subsref(o, S)
|
||||
|
||||
% Overload subsref method.
|
||||
|
||||
% 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 S(1).type
|
||||
case '.'
|
||||
if ismember(S(1).subs, {'p1','p2','p3','p4','p5','p6','p7','lb','ub'})
|
||||
p = builtin('subsref', o, S(1));
|
||||
elseif ismember(S(1).subs, {'draw'})
|
||||
p = feval(S(1).subs, o);
|
||||
elseif ismember(S(1).subs, {'draws', 'density', 'densities', 'moments'})
|
||||
p = feval(S(1).subs, o , S(2).subs{:});
|
||||
elseif ismember(S(1).subs, {'mean', 'median', 'variance', 'mode'})
|
||||
if (length(S)==2 && isempty(S(2).subs)) || length(S)==1
|
||||
p = feval(S(1).subs, o);
|
||||
else
|
||||
p = feval(S(1).subs, o , S(2).subs{:});
|
||||
end
|
||||
else
|
||||
error('dprior::subsref: unknown method (%s).', S(1).subs)
|
||||
end
|
||||
otherwise
|
||||
error('dprior::subsref: %s indexing not implemented.', S(1).type)
|
||||
end
|
||||
|
|
@ -0,0 +1,42 @@
|
|||
function m = variance(o, resetmoments)
|
||||
|
||||
% Return the prior variance.
|
||||
%
|
||||
% INPUTS
|
||||
% - o [dprior]
|
||||
% - resetmoments [logical] Force the computation of the prior variance
|
||||
%
|
||||
% OUTPUTS
|
||||
% - m [double] n×1 vector, prior variance
|
||||
|
||||
% 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/>.
|
||||
|
||||
if nargin<2
|
||||
resetmoments = false;
|
||||
end
|
||||
|
||||
if any(isnan(o.p2))
|
||||
resetmoments = true;
|
||||
end
|
||||
|
||||
if resetmoments
|
||||
o.p2 = NaN(size(o.p2));
|
||||
o.moments('std');
|
||||
end
|
||||
|
||||
m = o.p2.^2;
|
||||
|
|
@ -1,9 +1,18 @@
|
|||
function [xparams,lpd,hessian_mat] = ...
|
||||
maximize_prior_density(iparams, prior_shape, prior_hyperparameter_1, prior_hyperparameter_2, prior_inf_bound, prior_sup_bound,DynareOptions,DynareModel,BayesInfo,EstimatedParams,DynareResults)
|
||||
function [xparams, lpd, hessian_mat] = ...
|
||||
maximize_prior_density(iparams, names, DynareOptions, DynareModel, Prior, EstimatedParams, DynareResults)
|
||||
|
||||
% Maximizes the logged prior density using Chris Sims' optimization routine.
|
||||
%
|
||||
% INPUTS
|
||||
% iparams [double] vector of initial parameters.
|
||||
% - iparams [double] vector of initial parameters.
|
||||
% - Prior [dprior] vector specifying prior densities shapes.
|
||||
% - DynareOptions [struct] Options, AKA options_
|
||||
% - DynareModel [struct] Model description, AKA M_
|
||||
% - EstimatedParams [struct] Info about estimated parameters, AKA estimated_params_
|
||||
% - DynareResults [struct] Results, AKA oo_
|
||||
|
||||
%
|
||||
%
|
||||
% prior_shape [integer] vector specifying prior densities shapes.
|
||||
% prior_hyperparameter_1 [double] vector, first hyperparameter.
|
||||
% prior_hyperparameter_2 [double] vector, second hyperparameter.
|
||||
|
|
@ -32,10 +41,18 @@ function [xparams,lpd,hessian_mat] = ...
|
|||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
[xparams, lpd, exitflag, hessian_mat]=dynare_minimize_objective('minus_logged_prior_density', ...
|
||||
iparams, DynareOptions.mode_compute, DynareOptions, [prior_inf_bound, prior_sup_bound], ...
|
||||
BayesInfo.name, BayesInfo, [], ...
|
||||
prior_shape, prior_hyperparameter_1, prior_hyperparameter_2, prior_inf_bound, prior_sup_bound, ...
|
||||
DynareOptions,DynareModel,EstimatedParams,DynareResults);
|
||||
[xparams, lpd, exitflag, hessian_mat] = dynare_minimize_objective('minus_logged_prior_density', ...
|
||||
iparams, ...
|
||||
DynareOptions.mode_compute, ...
|
||||
DynareOptions, ...
|
||||
[Prior.p3, Prior.p4], ...
|
||||
BayesInfo.name, ...
|
||||
[], ...
|
||||
[], ...
|
||||
Prior,
|
||||
DynareOptions, ...
|
||||
DynareModel, ...
|
||||
EstimatedParams, ...
|
||||
DynareResults);
|
||||
|
||||
lpd = -lpd;
|
||||
|
|
|
|||
|
|
@ -1,19 +1,20 @@
|
|||
function [fval,info,exit_flag,fake_1,fake_2] = minus_logged_prior_density(xparams,pshape,p6,p7,p3,p4,DynareOptions,DynareModel,EstimatedParams,DynareResults)
|
||||
function [fval, info, exitflag, ~, ~] = minus_logged_prior_density(xparams, Prior, DynareOptions, DynareModel, EstimatedParams, DynareResults)
|
||||
|
||||
% Evaluates minus the logged prior density.
|
||||
%
|
||||
% INPUTS
|
||||
% xparams [double] vector of parameters.
|
||||
% pshape [integer] vector specifying prior densities shapes.
|
||||
% p6 [double] vector, first hyperparameter.
|
||||
% p7 [double] vector, second hyperparameter.
|
||||
% p3 [double] vector, prior's lower bound.
|
||||
% p4 [double] vector, prior's upper bound.
|
||||
% - xparams [double] vector of parameters.
|
||||
% - Prior [dprior] vector specifying prior densities shapes.
|
||||
% - DynareOptions [struct] Options, AKA options_
|
||||
% - DynareModel [struct] Model description, AKA M_
|
||||
% - EstimatedParams [struct] Info about estimated parameters, AKA estimated_params_
|
||||
% - DynareResults [struct] Results, AKA oo_
|
||||
%
|
||||
% OUTPUTS
|
||||
% f [double] value of minus the logged prior density.
|
||||
% info [double] vector: second entry stores penalty, first entry the error code.
|
||||
%
|
||||
% Copyright © 2009-2017 Dynare Team
|
||||
% - fval [double] value of minus the logged prior density.
|
||||
% - info [double] 4×1 vector, second entry stores penalty, first entry the error code, last entry a penalty (used for optimization).
|
||||
|
||||
% Copyright © 2009-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
|
@ -30,10 +31,7 @@ function [fval,info,exit_flag,fake_1,fake_2] = minus_logged_prior_density(xparam
|
|||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
fake_1 = 1;
|
||||
fake_2 = 1;
|
||||
|
||||
exit_flag = 1;
|
||||
exitflag = true;
|
||||
info = zeros(4,1);
|
||||
|
||||
%------------------------------------------------------------------------------
|
||||
|
|
@ -41,74 +39,75 @@ info = zeros(4,1);
|
|||
%------------------------------------------------------------------------------
|
||||
|
||||
% Return, with endogenous penalty, if some parameters are smaller than the lower bound of the prior domain.
|
||||
if ~isequal(DynareOptions.mode_compute,1) && any(xparams<p3)
|
||||
k = find(xparams<p3);
|
||||
if ~isequal(DynareOptions.mode_compute,1) && any(xparams<Prior.p3)
|
||||
k = find(xparams<Prior.p3);
|
||||
fval = Inf;
|
||||
exit_flag = 0;
|
||||
exitflag = false;
|
||||
info(1) = 41;
|
||||
info(4) = sum((p3(k)-xparams(k)).^2);
|
||||
info(4) = sum((Prior.p3(k)-xparams(k)).^2);
|
||||
return
|
||||
end
|
||||
|
||||
% Return, with endogenous penalty, if some parameters are greater than the upper bound of the prior domain.
|
||||
if ~isequal(DynareOptions.mode_compute,1) && any(xparams>p4)
|
||||
k = find(xparams>p4);
|
||||
if ~isequal(DynareOptions.mode_compute,1) && any(xparams>Prior.p4)
|
||||
k = find(xparams>Prior.p4);
|
||||
fval = Inf;
|
||||
exit_flag = 0;
|
||||
exitflag = false;
|
||||
info(1) = 42;
|
||||
info(4) = sum((xparams(k)-p4(k)).^2);
|
||||
info(4) = sum((xparams(k)-Prior.p4(k)).^2);
|
||||
return
|
||||
end
|
||||
|
||||
% Get the diagonal elements of the covariance matrices for the structural innovations (Q) and the measurement error (H).
|
||||
DynareModel = set_all_parameters(xparams,EstimatedParams,DynareModel);
|
||||
DynareModel = set_all_parameters(xparams, EstimatedParams, DynareModel);
|
||||
|
||||
Q = DynareModel.Sigma_e;
|
||||
H = DynareModel.H;
|
||||
|
||||
% Test if Q is positive definite.
|
||||
if ~issquare(Q) || EstimatedParams.ncx || isfield(EstimatedParams,'calibrated_covariances')
|
||||
if ~issquare(Q) || EstimatedParams.ncx || isfield(EstimatedParams, 'calibrated_covariances')
|
||||
% Try to compute the cholesky decomposition of Q (possible iff Q is positive definite)
|
||||
[Q_is_positive_definite, penalty] = ispd(Q);
|
||||
if ~Q_is_positive_definite
|
||||
% The variance-covariance matrix of the structural innovations is not definite positive. We have to compute the eigenvalues of this matrix in order to build the endogenous penalty.
|
||||
% The variance-covariance matrix of the structural innovations is not definite positive. We have to compute the
|
||||
% eigenvalues of this matrix in order to build the endogenous penalty.
|
||||
fval = Inf;
|
||||
exit_flag = 0;
|
||||
exitflag = false;
|
||||
info(1) = 43;
|
||||
info(4) = penalty;
|
||||
return
|
||||
end
|
||||
if isfield(EstimatedParams,'calibrated_covariances')
|
||||
correct_flag=check_consistency_covariances(Q);
|
||||
if isfield(EstimatedParams, 'calibrated_covariances')
|
||||
correct_flag = check_consistency_covariances(Q);
|
||||
if ~correct_flag
|
||||
penalty = sum(Q(EstimatedParams.calibrated_covariances.position).^2);
|
||||
fval = Inf;
|
||||
exit_flag = 0;
|
||||
exitflag = false;
|
||||
info(1) = 71;
|
||||
info(4) = penalty;
|
||||
return
|
||||
end
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
% Test if H is positive definite.
|
||||
if ~issquare(H) || EstimatedParams.ncn || isfield(EstimatedParams,'calibrated_covariances_ME')
|
||||
if ~issquare(H) || EstimatedParams.ncn || isfield(EstimatedParams, 'calibrated_covariances_ME')
|
||||
[H_is_positive_definite, penalty] = ispd(H);
|
||||
if ~H_is_positive_definite
|
||||
% The variance-covariance matrix of the measurement errors is not definite positive. We have to compute the eigenvalues of this matrix in order to build the endogenous penalty.
|
||||
% The variance-covariance matrix of the measurement errors is not definite positive. We have to compute the eigenvalues
|
||||
% of this matrix in order to build the endogenous penalty.
|
||||
fval = Inf;
|
||||
exit_flag = 0;
|
||||
exitflag = false;
|
||||
info(1) = 44;
|
||||
info(4) = penalty;
|
||||
return
|
||||
end
|
||||
if isfield(EstimatedParams,'calibrated_covariances_ME')
|
||||
correct_flag=check_consistency_covariances(H);
|
||||
if isfield(EstimatedParams, 'calibrated_covariances_ME')
|
||||
correct_flag = check_consistency_covariances(H);
|
||||
if ~correct_flag
|
||||
penalty = sum(H(EstimatedParams.calibrated_covariances_ME.position).^2);
|
||||
fval = Inf;
|
||||
exit_flag = 0;
|
||||
exitflag = false;
|
||||
info(1) = 72;
|
||||
info(4) = penalty;
|
||||
return
|
||||
|
|
@ -121,8 +120,7 @@ end
|
|||
% 2. Check BK and steady state
|
||||
%-----------------------------
|
||||
|
||||
M_ = set_all_parameters(xparams,EstimatedParams,DynareModel);
|
||||
[dr,info,DynareModel,DynareResults] = resol(0,DynareModel,DynareOptions,DynareResults);
|
||||
[dr, info, DynareModel, DynareResults] = resol(0, DynareModel, DynareOptions, DynareResults);
|
||||
|
||||
% Return, with endogenous penalty when possible, if dynare_resolve issues an error code (defined in resol).
|
||||
if info(1)
|
||||
|
|
@ -132,16 +130,14 @@ if info(1)
|
|||
%meaningful second entry of output that can be used
|
||||
fval = Inf;
|
||||
info(4) = info(2);
|
||||
exit_flag = 0;
|
||||
exitflag = false;
|
||||
return
|
||||
else
|
||||
fval = Inf;
|
||||
info(4) = 0.1;
|
||||
exit_flag = 0;
|
||||
exitflag = false;
|
||||
return
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
|
||||
fval = - priordens(xparams,pshape,p6,p7,p3,p4);
|
||||
fval = - Prior.density(xparams);
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
function optimize_prior(DynareOptions, ModelInfo, DynareResults, BayesInfo, EstimationInfo)
|
||||
function optimize_prior(DynareOptions, ModelInfo, DynareResults, BayesInfo, EstimationInfo, pnames)
|
||||
|
||||
% This routine computes the mode of the prior density using an optimization algorithm.
|
||||
|
||||
|
|
@ -19,24 +19,25 @@ function optimize_prior(DynareOptions, ModelInfo, DynareResults, BayesInfo, Esti
|
|||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
DynareResults.dr = set_state_space(DynareResults.dr, ModelInfo, DynareOptions);
|
||||
|
||||
% Initialize to the prior mean
|
||||
DynareResults.dr = set_state_space(DynareResults.dr,ModelInfo,DynareOptions);
|
||||
xparam1 = BayesInfo.p1;
|
||||
xparam1 = Prior.p1;
|
||||
|
||||
% Pertubation of the initial condition.
|
||||
look_for_admissible_initial_condition = 1; scale = 1.0; iter = 0;
|
||||
look_for_admissible_initial_condition = true; scale = 1.0; iter = 0;
|
||||
while look_for_admissible_initial_condition
|
||||
xinit = xparam1+scale*randn(size(xparam1));
|
||||
if all(xinit(:)>BayesInfo.p3) && all(xinit(:)<BayesInfo.p4)
|
||||
ModelInfo = set_all_parameters(xinit,EstimationInfo,ModelInfo);
|
||||
[dr,INFO,ModelInfo,DynareResults] = resol(0,ModelInfo,DynareOptions,DynareResults);
|
||||
if all(xinit>Prior.p3) && all(xinit<Prior.p4)
|
||||
ModelInfo = set_all_parameters(xinit, EstimationInfo, ModelInfo);
|
||||
[dr, INFO, ModelInfo, DynareResults] = resol(0, ModelInfo, DynareOptions, DynareResults);
|
||||
if ~INFO(1)
|
||||
look_for_admissible_initial_condition = 0;
|
||||
look_for_admissible_initial_condition = false;
|
||||
end
|
||||
else
|
||||
if iter == 2000
|
||||
if iter==2000
|
||||
scale = scale/1.1;
|
||||
iter = 0;
|
||||
iter = 0;
|
||||
else
|
||||
iter = iter+1;
|
||||
end
|
||||
|
|
@ -45,23 +46,19 @@ end
|
|||
|
||||
% Maximization of the prior density
|
||||
[xparams, lpd, hessian_mat] = ...
|
||||
maximize_prior_density(xinit, BayesInfo.pshape, ...
|
||||
BayesInfo.p6, ...
|
||||
BayesInfo.p7, ...
|
||||
BayesInfo.p3, ...
|
||||
BayesInfo.p4,DynareOptions,ModelInfo,BayesInfo,EstimationInfo,DynareResults);
|
||||
maximize_prior_density(xinit, pnames, DynareOptions, ModelInfo, Prior, EstimationInfo, DynareResults);
|
||||
|
||||
% Display the results.
|
||||
% Display results.
|
||||
skipline(2)
|
||||
disp('------------------')
|
||||
disp('PRIOR OPTIMIZATION')
|
||||
disp('------------------')
|
||||
skipline()
|
||||
for i = 1:length(xparams)
|
||||
disp(['deep parameter ' int2str(i) ': ' get_the_name(i,0,ModelInfo,EstimationInfo,DynareOptions) '.'])
|
||||
disp([' Initial condition ....... ' num2str(xinit(i)) '.'])
|
||||
disp([' Prior mode .............. ' num2str(BayesInfo.p5(i)) '.'])
|
||||
disp([' Optimized prior mode .... ' num2str(xparams(i)) '.'])
|
||||
dprintf('deep parameter %u: %s.', i, get_the_name(i, 0, ModelInfo, EstimationInfo, DynareOptions))
|
||||
dprintf(' Initial condition ........ %s.', num2str(xinit(i)))
|
||||
dprintf(' Prior mode ............... %s.', num2str(Prior.p5(i)))
|
||||
dprintf(' Optimized prior mode ..... %s.', num2str(xparams(i)))
|
||||
skipline()
|
||||
end
|
||||
skipline()
|
||||
skipline()
|
||||
|
|
|
|||
|
|
@ -1,51 +1,14 @@
|
|||
function bounds = prior_bounds(bayestopt, prior_trunc)
|
||||
|
||||
%@info:
|
||||
%! @deftypefn {Function File} {@var{bounds} =} prior_bounds (@var{bayesopt},@var{option})
|
||||
%! @anchor{prior_bounds}
|
||||
%! @sp 1
|
||||
%! Returns bounds for the prior densities. For each estimated parameter the lower and upper bounds
|
||||
%! are such that the defined intervals contains a probability mass equal to 1-2*@var{option}.prior_trunc. The
|
||||
%! default value for @var{option}.prior_trunc is 1e-10 (set in @ref{global_initialization}).
|
||||
%! @sp 2
|
||||
%! @strong{Inputs}
|
||||
%! @sp 1
|
||||
%! @table @ @var
|
||||
%! @item bayestopt
|
||||
%! Matlab's structure describing the prior distribution (initialized by @code{dynare}).
|
||||
%! @item option
|
||||
%! Matlab's structure describing the options (initialized by @code{dynare}).
|
||||
%! @end table
|
||||
%! @sp 2
|
||||
%! @strong{Outputs}
|
||||
%! @sp 1
|
||||
%! @table @ @var
|
||||
%! @item bounds
|
||||
%! A structure with two fields lb and up (p*1 vectors of doubles, where p is the number of estimated parameters) for the lower and upper bounds.
|
||||
%! @end table
|
||||
%! @sp 2
|
||||
%! @strong{This function is called by:}
|
||||
%! @sp 1
|
||||
%! @ref{get_prior_info}, @ref{dynare_estimation_1}, @ref{dynare_estimation_init}
|
||||
%! @sp 2
|
||||
%! @strong{This function calls:}
|
||||
%! @sp 1
|
||||
%! None.
|
||||
%! @end deftypefn
|
||||
%@eod:
|
||||
|
||||
function bounds = prior_bounds(bayestopt, priortrunc)
|
||||
|
||||
% function bounds = prior_bounds(bayestopt)
|
||||
% computes bounds for prior density.
|
||||
%
|
||||
% INPUTS
|
||||
% bayestopt [structure] characterizing priors (shape, mean, p1..p4)
|
||||
% - bayestopt [struct] characterizing priors (shape, mean, p1..p4)
|
||||
% - priortrunc [double] scalar, probability mass in the tails to be removed
|
||||
%
|
||||
% OUTPUTS
|
||||
% bounds [double] structure specifying prior bounds (lb and ub fields)
|
||||
%
|
||||
% SPECIAL REQUIREMENTS
|
||||
% none
|
||||
% - bounds [struct] prior bounds (lb, lower bounds, and ub, upper bounds, fields are n×1 vectors)
|
||||
|
||||
% Copyright © 2003-2023 Dynare Team
|
||||
%
|
||||
|
|
@ -64,74 +27,78 @@ function bounds = prior_bounds(bayestopt, prior_trunc)
|
|||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
if nargin<2, priortrunc = 0.0; end
|
||||
|
||||
assert(priortrunc>=0 && priortrunc<=1, 'Second input argument must be non negative and not larger than one.')
|
||||
|
||||
pshape = bayestopt.pshape;
|
||||
p3 = bayestopt.p3;
|
||||
p4 = bayestopt.p4;
|
||||
p6 = bayestopt.p6;
|
||||
p7 = bayestopt.p7;
|
||||
|
||||
bounds.lb = zeros(length(p6),1);
|
||||
bounds.ub = zeros(length(p6),1);
|
||||
bounds.lb = zeros(size(p6));
|
||||
bounds.ub = zeros(size(p6));
|
||||
|
||||
for i=1:length(p6)
|
||||
switch pshape(i)
|
||||
case 1
|
||||
if prior_trunc == 0
|
||||
if priortrunc==0
|
||||
bounds.lb(i) = p3(i);
|
||||
bounds.ub(i) = p4(i);
|
||||
else
|
||||
bounds.lb(i) = betainv(prior_trunc,p6(i),p7(i))*(p4(i)-p3(i))+p3(i);
|
||||
bounds.ub(i) = betainv(1-prior_trunc,p6(i),p7(i))*(p4(i)-p3(i))+p3(i);
|
||||
bounds.lb(i) = betainv(priortrunc, p6(i), p7(i))*(p4(i)-p3(i))+p3(i);
|
||||
bounds.ub(i) = betainv(1.0-priortrunc, p6(i), p7(i))*(p4(i)-p3(i))+p3(i);
|
||||
end
|
||||
case 2
|
||||
if prior_trunc == 0
|
||||
if priortrunc==0
|
||||
bounds.lb(i) = p3(i);
|
||||
bounds.ub(i) = Inf;
|
||||
else
|
||||
bounds.lb(i) = gaminv(prior_trunc,p6(i),p7(i))+p3(i);
|
||||
bounds.ub(i) = gaminv(1-prior_trunc,p6(i),p7(i))+p3(i);
|
||||
bounds.lb(i) = gaminv(priortrunc, p6(i), p7(i))+p3(i);
|
||||
bounds.ub(i) = gaminv(1.0-priortrunc, p6(i), p7(i))+p3(i);
|
||||
end
|
||||
case 3
|
||||
if prior_trunc == 0
|
||||
if priortrunc==0
|
||||
bounds.lb(i) = -Inf;
|
||||
bounds.ub(i) = Inf;
|
||||
else
|
||||
bounds.lb(i) = norminv(prior_trunc,p6(i),p7(i));
|
||||
bounds.ub(i) = norminv(1-prior_trunc,p6(i),p7(i));
|
||||
bounds.lb(i) = norminv(priortrunc, p6(i), p7(i));
|
||||
bounds.ub(i) = norminv(1.0-priortrunc, p6(i), p7(i));
|
||||
end
|
||||
case 4
|
||||
if prior_trunc == 0
|
||||
if priortrunc==0
|
||||
bounds.lb(i) = p3(i);
|
||||
bounds.ub(i) = Inf;
|
||||
else
|
||||
bounds.lb(i) = 1/sqrt(gaminv(1-prior_trunc, p7(i)/2, 2/p6(i)))+p3(i);
|
||||
bounds.ub(i) = 1/sqrt(gaminv(prior_trunc, p7(i)/2, 2/p6(i)))+p3(i);
|
||||
bounds.lb(i) = 1.0/sqrt(gaminv(1.0-priortrunc, p7(i)/2.0, 2.0/p6(i)))+p3(i);
|
||||
bounds.ub(i) = 1.0/sqrt(gaminv(priortrunc, p7(i)/2.0, 2.0/p6(i)))+p3(i);
|
||||
end
|
||||
case 5
|
||||
if prior_trunc == 0
|
||||
if priortrunc == 0
|
||||
bounds.lb(i) = p6(i);
|
||||
bounds.ub(i) = p7(i);
|
||||
else
|
||||
bounds.lb(i) = p6(i)+(p7(i)-p6(i))*prior_trunc;
|
||||
bounds.ub(i) = p7(i)-(p7(i)-p6(i))*prior_trunc;
|
||||
bounds.lb(i) = p6(i)+(p7(i)-p6(i))*priortrunc;
|
||||
bounds.ub(i) = p7(i)-(p7(i)-p6(i))*priortrunc;
|
||||
end
|
||||
case 6
|
||||
if prior_trunc == 0
|
||||
if priortrunc == 0
|
||||
bounds.lb(i) = p3(i);
|
||||
bounds.ub(i) = Inf;
|
||||
else
|
||||
bounds.lb(i) = 1/gaminv(1-prior_trunc, p7(i)/2, 2/p6(i))+p3(i);
|
||||
bounds.ub(i) = 1/gaminv(prior_trunc, p7(i)/2, 2/p6(i))+ p3(i);
|
||||
bounds.lb(i) = 1.0/gaminv(1.0-priortrunc, p7(i)/2.0, 2.0/p6(i))+p3(i);
|
||||
bounds.ub(i) = 1.0/gaminv(priortrunc, p7(i)/2.0, 2.0/p6(i))+ p3(i);
|
||||
end
|
||||
case 8
|
||||
if prior_trunc == 0
|
||||
if priortrunc == 0
|
||||
bounds.lb(i) = p3(i);
|
||||
bounds.ub(i) = Inf;
|
||||
else
|
||||
bounds.lb(i) = p3(i)+wblinv(prior_trunc,p6(i),p7(i));
|
||||
bounds.ub(i) = p3(i)+wblinv(1-prior_trunc,p6(i),p7(i));
|
||||
bounds.lb(i) = p3(i)+wblinv(priortrunc, p6(i), p7(i));
|
||||
bounds.ub(i) = p3(i)+wblinv(1.0-priortrunc, p6(i), p7(i));
|
||||
end
|
||||
otherwise
|
||||
error(sprintf('prior_bounds: unknown distribution shape (index %d, type %d)', i, pshape(i)));
|
||||
end
|
||||
end
|
||||
end
|
||||
|
|
|
|||
|
|
@ -0,0 +1,59 @@
|
|||
function v = substitute(v, i, x)
|
||||
|
||||
% Substitute a scalar in a vector.
|
||||
%
|
||||
% INPUTS
|
||||
% - v [double] m×1 vector
|
||||
% - i [integer] scalar, index for the scalar to be replaced
|
||||
% - x [double] scalar or 1×n vector.
|
||||
%
|
||||
% OUTPUTS
|
||||
% - v [double] m×1 vector or m×n matrix (with substituted value(s))
|
||||
%
|
||||
% REMARKS
|
||||
% If x is a vector with n elements, then n substitutions are performed (returning n updated vectors in a matrix with n columns)
|
||||
%
|
||||
% EXAMPLES
|
||||
% >> v = ones(2,1);
|
||||
% >> substitude(v, 1, 0)
|
||||
%
|
||||
% ans = %
|
||||
%
|
||||
% 0
|
||||
%
|
||||
% 1
|
||||
%
|
||||
% >> substitute(v, 1, [3 4])
|
||||
%
|
||||
% ans =
|
||||
%
|
||||
% 3 4
|
||||
% 1 1
|
||||
|
||||
% 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/>.
|
||||
|
||||
assert(isvector(v), 'First input argument must be a vector.')
|
||||
assert(isvector(x), 'Last input argument must be a scalar or a vector.')
|
||||
assert(isscalar(i) && isint(i) && i>0 && i<=length(v), 'Second input argument must be a scalar integer')
|
||||
|
||||
if length(x)==1
|
||||
v(i) = x;
|
||||
else
|
||||
v = repmat(v, 1, length(x));
|
||||
v(i,:) = x;
|
||||
end
|
||||
Loading…
Reference in New Issue