126 lines
3.9 KiB
Matlab
126 lines
3.9 KiB
Matlab
function pdraw = prior_draw_gsa(init,rdraw,cc)
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% Draws from the prior distributions
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% Adapted by M. Ratto from prior_draw (of DYNARE, copyright M. Juillard),
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% for use with Sensitivity Toolbox for DYNARE
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%
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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 rdraw
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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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% ALGORITHM
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% ...
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%
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% SPECIAL REQUIREMENTS
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% MATLAB Statistics Toolbox
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%
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%
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% Part of the Sensitivity Analysis Toolbox for DYNARE
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%
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% Written by Marco Ratto, 2006
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% Joint Research Centre, The European Commission,
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% (http://eemc.jrc.ec.europa.eu/),
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% marco.ratto@jrc.it
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%
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% Disclaimer: This software is not subject to copyright protection and is in the public domain.
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% It is an experimental system. The Joint Research Centre of European Commission
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% assumes no responsibility whatsoever for its use by other parties
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% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
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% characteristic. We would appreciate acknowledgement if the software is used.
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% Reference:
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% M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
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%
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global M_ options_ 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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MhDirectoryName = CheckPath('metropolis');
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fname = [ MhDirectoryName '/' M_.fname];
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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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disp('prior_draw :: Error!')
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disp('Unknown prior shape.')
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return
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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) = rdraw(:,i)*(p4(i)-p3(i)) + p3(i);
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case 3% Gaussian prior.
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pdraw(:,i) = norminv(rdraw(:,i),pmean(i),pstd(i));
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case 2% Gamma prior.
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pdraw(:,i) = gaminv(rdraw(:,i),a(i),b(i))+p3(i);
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case 1% Beta distribution (TODO: generalized beta distribution)
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pdraw(:,i) = betainv(rdraw(:,i),a(i),b(i))*(p4(i)-p3(i))+p3(i);
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case 4% INV-GAMMA1 distribution
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% TO BE CHECKED
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pdraw(:,i) = sqrt(1./gaminv(rdraw(:,i),p2(i)/2,1/p1(i)));
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case 6% INV-GAMMA2 distribution
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% TO BE CHECKED
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pdraw(:,i) = 1./gaminv(rdraw(:,i),p2(i)/2,1/p1(i));
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otherwise
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% Nothing to do here.
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end
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end
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