73 lines
2.4 KiB
Matlab
73 lines
2.4 KiB
Matlab
function bounds = prior_bounds(bayestopt)
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% function bounds = prior_bounds(bayestopt)
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% computes practical bounds for prior density
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%
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% INPUTS
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% bayestopt: structure characterizing priors (shape, mean, p1..p4)
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%
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% OUTPUTS
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% bounds: matrix specifying bounds (row= parameter, column=upper&lower bound)
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%
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% SPECIAL REQUIREMENTS
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% none
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% Copyright (C) 2003-2008 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 options_
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pshape = bayestopt.pshape;
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pmean = bayestopt.pmean;
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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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n = length(pmean);
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bounds = zeros(n,2);
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for i=1:n
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switch pshape(i)
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case 1
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mu = (pmean(i)-p3(i))/(p4(i)-p3(i));
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stdd = p2(i)/(p4(i)-p3(i));
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A = (1-mu)*mu^2/stdd^2 - mu;
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B = A*(1/mu - 1);
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bounds(i,1) = betainv(options_.prior_trunc,A,B)*(p4(i)-p3(i))+p3(i);
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bounds(i,2) = betainv(1-options_.prior_trunc,A,B)*(p4(i)-p3(i))+p3(i);
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case 2
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b = p2(i)^2/(pmean(i)-p3(i));
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a = (pmean(i)-p3(i))/b;
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bounds(i,1) = gaminv(options_.prior_trunc,a,b)+p3(i);
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bounds(i,2) = gaminv(1-options_.prior_trunc,a,b)+p3(i);
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case 3
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bounds(i,1) = norminv(options_.prior_trunc,pmean(i),p2(i));
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bounds(i,2) = norminv(1-options_.prior_trunc,pmean(i),p2(i));
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case 4
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bounds(i,1) = 1/sqrt(gaminv(1-options_.prior_trunc, p2(i)/2, 2/p1(i)));
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bounds(i,2) = 1/sqrt(gaminv(options_.prior_trunc, p2(i)/2, 2/p1(i)));
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case 5
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bounds(i,1) = p1(i);
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bounds(i,2) = p2(i);
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case 6
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bounds(i,1) = 1/gaminv(1-options_.prior_trunc, p2(i)/2, 2/p1(i));
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bounds(i,2) = 1/gaminv(options_.prior_trunc, p2(i)/2, 2/p1(i));
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otherwise
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error(sprintf('prior_bounds: unknown distribution shape (index %d, type %d)', i, pshape(i)));
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end
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end
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