143 lines
4.3 KiB
Matlab
143 lines
4.3 KiB
Matlab
function pdraw = prior_draw(init,cc)
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% function pdraw = prior_draw(init,cc)
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% Builds one draw from the prior distribution.
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%
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% INPUTS
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% o init [integer] scalar equal to 1 (first call) or 0.
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% o cc [double] two columns matrix (same as in
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% metropolis.m), constraints over the
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% parameter space (upper and lower bounds).
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%
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% OUTPUTS
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% o pdraw [double] draw from the joint prior density.
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%
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%
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% SPECIAL REQUIREMENTS
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% none
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% Copyright (C) 2006-2008 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 <http://www.gnu.org/licenses/>.
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global estim_params_ bayestopt_
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persistent fname npar bounds pshape pmean pstd a b p1 p2 p3 p4 condition
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if init
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nvx = estim_params_.nvx;
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nvn = estim_params_.nvn;
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ncx = estim_params_.ncx;
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ncn = estim_params_.ncn;
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np = estim_params_.np ;
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npar = nvx+nvn+ncx+ncn+np;
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pshape = bayestopt_.pshape;
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pmean = bayestopt_.pmean;
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pstd = bayestopt_.pstdev;
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p1 = bayestopt_.p1;
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p2 = bayestopt_.p2;
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p3 = bayestopt_.p3;
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p4 = bayestopt_.p4;
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a = zeros(npar,1);
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b = zeros(npar,1);
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if nargin == 2
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bounds = cc;
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else
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bounds = kron(ones(npar,1),[-Inf Inf]);
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end
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for i = 1:npar
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switch pshape(i)
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case 3% Gaussian prior
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b(i) = pstd(i)^2/(pmean(i)-p3(i));
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a(i) = (pmean(i)-p3(i))/b(i);
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case 1% Beta prior
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mu = (p1(i)-p3(i))/(p4(i)-p3(i));
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stdd = p2(i)/(p4(i)-p3(i));
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a(i) = (1-mu)*mu^2/stdd^2 - mu;
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b(i) = a(i)*(1/mu - 1);
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case 2;%Gamma prior
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mu = p1(i)-p3(i);
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b(i) = p2(i)^2/mu;
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a(i) = mu/b(i);
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case {5,4,6}
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% Nothing to do here
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%
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% 4: Inverse gamma, type 1, prior
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% p2(i) = nu
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% p1(i) = s
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% 6: Inverse gamma, type 2, prior
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% p2(i) = nu
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% p1(i) = s
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% 5: Uniform prior
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% p3(i) and p4(i) are used.
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otherwise
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error(sprintf('prior_draw: unknown distribution shape (index %d, type %d)', i, pshape(i)));
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end
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pdraw = zeros(npar,1);
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end
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condition = 1;
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pdraw = zeros(npar,1);
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return
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end
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for i = 1:npar
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switch pshape(i)
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case 5% Uniform prior.
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pdraw(i) = rand*(p4(i)-p3(i)) + p3(i);
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case 3% Gaussian prior.
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while condition
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tmp = randn*pstd(i) + pmean(i);
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if tmp >= bounds(i,1) && tmp <= bounds(i,2)
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pdraw(i) = tmp;
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break
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end
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end
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case 2% Gamma prior.
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while condition
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g = gamrnd(a(i),b(i)) + p3(i);
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if g >= bounds(i,1) && g <= bounds(i,2)
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pdraw(i) = g;
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break
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end
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end
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case 1% Beta distribution
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while condition
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tmp = betarnd(a(i), b(i));
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if tmp >= bounds(i,1) && tmp <= bounds(i,2)
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pdraw(i) = p3(i)+tmp*(p4(i)-p3(i));
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break
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end
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end
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case 4% INV-GAMMA1 distribution
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while condition
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tmp = sqrt(1/gamrnd(p2(i)/2,2/p1(i)));
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if tmp >= bounds(i,1) && tmp <= bounds(i,2)
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pdraw(i) = tmp;
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break
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end
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end
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case 6% INV-GAMMA2 distribution
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while condition
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tmp = 1/gamrnd(p2(i)/2,2/p1(i));
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if tmp >= bounds(i,1) && tmp <= bounds(i,2)
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pdraw(i) = tmp;
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break
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end
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end
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otherwise
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error(sprintf('prior_draw: unknown distribution shape (index %d, type %d)', i, pshape(i)));
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end
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end
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