104 lines
3.4 KiB
Matlab
104 lines
3.4 KiB
Matlab
function bounds = prior_bounds(bayestopt_, priortrunc)
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% computes bounds for prior density.
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%
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% INPUTS
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% - bayestopt [struct] characterizing priors (shape, mean, p1..p4)
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% - priortrunc [double] scalar, probability mass in the tails to be removed
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%
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% OUTPUTS
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% - bounds [struct] prior bounds (lb, lower bounds, and ub, upper bounds, fields are n×1 vectors)
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% Copyright © 2003-2023 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
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if nargin<2, priortrunc = 0.0; end
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assert(priortrunc>=0 && priortrunc<=1, 'Second input argument must be non negative and not larger than one.')
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pshape = bayestopt_.pshape;
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p3 = bayestopt_.p3;
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p4 = bayestopt_.p4;
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p6 = bayestopt_.p6;
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p7 = bayestopt_.p7;
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bounds.lb = zeros(size(p6));
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bounds.ub = zeros(size(p6));
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for i=1:length(p6)
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switch pshape(i)
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case 1
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if priortrunc==0
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bounds.lb(i) = p3(i);
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bounds.ub(i) = p4(i);
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else
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bounds.lb(i) = betainv(priortrunc, p6(i), p7(i))*(p4(i)-p3(i))+p3(i);
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bounds.ub(i) = betainv(1.0-priortrunc, p6(i), p7(i))*(p4(i)-p3(i))+p3(i);
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end
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case 2
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if priortrunc==0
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bounds.lb(i) = p3(i);
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bounds.ub(i) = Inf;
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else
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bounds.lb(i) = gaminv(priortrunc, p6(i), p7(i))+p3(i);
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bounds.ub(i) = gaminv(1.0-priortrunc, p6(i), p7(i))+p3(i);
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end
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case 3
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if priortrunc == 0
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bounds.lb(i) = max(-Inf, p3(i));
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bounds.ub(i) = min(Inf, p4(i));
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else
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bounds.lb(i) = max(norminv(priortrunc, p6(i), p7(i)), p3(i));
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bounds.ub(i) = min(norminv(1-priortrunc, p6(i), p7(i)), p4(i));
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end
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case 4
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if priortrunc==0
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bounds.lb(i) = p3(i);
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bounds.ub(i) = Inf;
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else
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bounds.lb(i) = 1.0/sqrt(gaminv(1.0-priortrunc, p7(i)/2.0, 2.0/p6(i)))+p3(i);
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bounds.ub(i) = 1.0/sqrt(gaminv(priortrunc, p7(i)/2.0, 2.0/p6(i)))+p3(i);
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end
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case 5
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if priortrunc == 0
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bounds.lb(i) = p6(i);
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bounds.ub(i) = p7(i);
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else
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bounds.lb(i) = p6(i)+(p7(i)-p6(i))*priortrunc;
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bounds.ub(i) = p7(i)-(p7(i)-p6(i))*priortrunc;
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end
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case 6
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if priortrunc == 0
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bounds.lb(i) = p3(i);
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bounds.ub(i) = Inf;
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else
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bounds.lb(i) = 1.0/gaminv(1.0-priortrunc, p7(i)/2.0, 2.0/p6(i))+p3(i);
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bounds.ub(i) = 1.0/gaminv(priortrunc, p7(i)/2.0, 2.0/p6(i))+ p3(i);
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end
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case 8
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if priortrunc == 0
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bounds.lb(i) = p3(i);
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bounds.ub(i) = Inf;
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else
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bounds.lb(i) = p3(i)+wblinv(priortrunc, p6(i), p7(i));
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bounds.ub(i) = p3(i)+wblinv(1.0-priortrunc, p6(i), p7(i));
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end
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otherwise
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error('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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