2019-11-20 16:53:53 +01:00
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function ds = surgibbs(ds, param_names, beta0, A, ndraws, discarddraws, thin, eqtags, model_name)
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%function ds = surgibbs(ds, param_names, beta0, A, ndraws, discarddraws, thin, eqtags, model_name)
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2017-07-12 16:43:12 +02:00
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% Implements Gibbs Samipling for SUR
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%
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% INPUTS
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2018-01-05 17:13:46 +01:00
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% ds [dseries] data
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2018-01-11 11:18:47 +01:00
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% param_names [cellstr] list of parameters to estimate
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% beta0 [vector] prior values (in order of param_names)
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% A [matrix] prior distribution variance (in order of
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% param_names)
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2018-01-05 17:13:46 +01:00
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% ndraws [int] number of draws
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% discarddraws [int] number of draws to discard
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2018-01-12 14:43:18 +01:00
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% thin [int] if thin == N, save every Nth draw
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2018-01-16 18:42:15 +01:00
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% eqtags [cellstr] names of equation tags to estimate. If empty,
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% estimate all equations
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2019-02-28 11:49:50 +01:00
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% model_name [string] name to use in oo_ and inc file
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2017-07-12 16:43:12 +02:00
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%
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% OUTPUTS
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% none
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%
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% SPECIAL REQUIREMENTS
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2019-01-24 12:42:08 +01:00
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% dynare must have been run with the option: json=compute
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2017-07-12 16:43:12 +02:00
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2019-01-11 16:00:20 +01:00
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% Copyright (C) 2017-2019 Dynare Team
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2017-07-12 16:43:12 +02:00
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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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2018-01-05 17:13:46 +01:00
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%% The notation that follows comes from Section 2.2 of
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% Ando, Tomohiro and Zellner, Arnold. 2010. Hierarchical Bayesian Analysis of the
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% Seemingly Unrelated Regression and Simultaneous Equations Models Using a
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% Combination of Direct Monte Carlo and Importance Sampling Techniques.
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2018-01-05 17:19:33 +01:00
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% Bayesian Analysis Volume 5, Number 1, pp. 65-96.
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2018-01-05 17:13:46 +01:00
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2019-01-14 17:57:36 +01:00
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global M_ oo_ options_
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%% Check input
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2019-02-28 11:49:50 +01:00
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assert(nargin >= 5 && nargin <= 9, 'Incorrect number of arguments passed to surgibbs');
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2018-01-11 11:18:47 +01:00
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assert(isdseries(ds), 'The 1st argument must be a dseries');
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assert(iscellstr(param_names), 'The 2nd argument must be a cellstr');
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assert(isvector(beta0) && length(beta0) == length(param_names), ...
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'The 3rd argument must be a vector with the same length as param_names and the same ');
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if isrow(beta0)
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beta0 = beta0';
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end
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assert(ismatrix(A) && all(all((A == A'))) && length(beta0) == size(A, 2), ...
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'The 4th argument must be a symmetric matrix with the same dimension as beta0');
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assert(isint(ndraws), 'The 5th argument must be an integer');
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if nargin == 5
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2018-01-05 17:13:46 +01:00
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discarddraws = 0;
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2018-01-11 11:18:47 +01:00
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else
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assert(isint(discarddraws), 'The 6th argument, if provided, must be an integer');
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2018-01-05 17:13:46 +01:00
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end
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2018-01-12 14:43:18 +01:00
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if nargin == 6
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thin = 1;
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else
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assert(isint(thin), 'The 7th argument, if provided, must be an integer');
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end
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2017-07-12 16:43:12 +02:00
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2019-02-28 11:49:50 +01:00
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if nargin <= 8
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if ~isfield(oo_, 'surgibbs')
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model_name = 'surgibbs_model_number_1';
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else
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model_name = ['surgibbs_model_number_' num2str(length(fieldnames(oo_.surgibbs)) + 1)];
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end
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else
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if ~isvarname(model_name)
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error('The 9th argument must be a valid string');
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end
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end
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2017-07-12 16:43:12 +02:00
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%% Estimation
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2018-09-28 16:09:11 +02:00
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% Notation from:
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% Ando, Tomohiro and Zellner, Arnold. Hierarchical Bayesian Analysis of
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% the Seemingly Unrelated Regression and Simultaneous Equations Models
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% Using a Combination of Direct Monte Carlo and Importance Sampling
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% Techniques. Bayesian Analysis. 2010. pp 67-70.
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2018-01-16 18:42:15 +01:00
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if nargin == 8
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2019-11-25 14:09:55 +01:00
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[nobs, X, Y, m, lhssub, fp] = sur(ds, param_names, eqtags);
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2018-01-16 18:42:15 +01:00
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else
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2019-11-25 14:09:55 +01:00
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[nobs, X, Y, m, lhssub, fp] = sur(ds, param_names);
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end
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2019-01-11 16:00:20 +01:00
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2019-11-20 16:53:53 +01:00
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oo_.surgibbs.(model_name).dof = nobs;
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2018-01-11 11:18:47 +01:00
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beta = beta0;
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2018-01-05 17:13:46 +01:00
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A = inv(A);
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2018-01-12 14:43:18 +01:00
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thinidx = 1;
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drawidx = 1;
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2018-01-24 15:22:30 +01:00
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nparams = length(param_names);
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2019-02-28 11:49:50 +01:00
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oo_.surgibbs.(model_name).betadraws = zeros(floor((ndraws-discarddraws)/thin), nparams);
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2019-01-11 16:00:20 +01:00
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if ~options_.noprint
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2019-01-14 17:57:36 +01:00
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disp('surgibbs: estimating, please wait...')
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2019-01-11 16:00:20 +01:00
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end
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2019-12-10 17:03:22 +01:00
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hh = dyn_waitbar(0,'Please wait. Gibbs sampler...');
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set(hh,'Name','Surgibbs estimation.');
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2017-07-12 16:43:12 +02:00
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for i = 1:ndraws
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2019-12-10 17:03:22 +01:00
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if ~mod(i,10)
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dyn_waitbar(i/ndraws,hh,'Please wait. Gibbs sampler...');
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end
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2018-01-05 17:13:46 +01:00
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% Draw Omega, given X, Y, Beta
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2017-07-12 16:43:12 +02:00
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resid = reshape(Y - X*beta, nobs, m);
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2018-01-05 17:13:46 +01:00
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Omega = rand_inverse_wishart(m, nobs, chol(inv(resid'*resid/nobs)));
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2017-07-12 16:43:12 +02:00
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2018-01-05 17:13:46 +01:00
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% Draw beta, given X, Y, Omega
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2017-07-12 16:43:12 +02:00
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tmp = kron(inv(Omega), eye(nobs));
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tmp1 = X'*tmp*X;
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Omegabar = inv(tmp1 + A);
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2018-01-05 17:13:46 +01:00
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betahat = tmp1\X'*tmp*Y;
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betabar = Omegabar*(tmp1*betahat+A*beta0);
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2018-01-11 11:18:47 +01:00
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beta = rand_multivariate_normal(betabar', chol(Omegabar), nparams)';
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2018-01-05 17:13:46 +01:00
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if i > discarddraws
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2018-01-12 14:43:18 +01:00
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if thinidx == thin
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2019-11-20 16:53:53 +01:00
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oo_.surgibbs.(model_name).betadraws(drawidx, 1:nparams) = beta';
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2018-01-12 14:43:18 +01:00
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thinidx = 1;
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drawidx = drawidx + 1;
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else
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thinidx = thinidx + 1;
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end
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2018-01-05 17:13:46 +01:00
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end
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2017-07-12 18:12:26 +02:00
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end
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2019-12-10 17:03:22 +01:00
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dyn_waitbar_close(hh);
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2017-07-12 18:12:26 +02:00
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2019-11-20 16:53:53 +01:00
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%% Save posterior moments.
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oo_.surgibbs.(model_name).posterior.mean.beta = (sum(oo_.surgibbs.(model_name).betadraws)/rows(oo_.surgibbs.(model_name).betadraws))';
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oo_.surgibbs.(model_name).posterior.variance.beta = cov(oo_.surgibbs.(model_name).betadraws);
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% Yhat
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2019-11-25 11:48:09 +01:00
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oo_.surgibbs.(model_name).Yhat = X*oo_.surgibbs.(model_name).posterior.mean.beta;
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2019-12-10 14:08:50 +01:00
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oo_.surgibbs.(model_name).YhatOrig = oo_.surgibbs.(model_name).Yhat;
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oo_.surgibbs.(model_name).Yobs = Y;
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2019-11-20 16:53:53 +01:00
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% Residuals
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oo_.surgibbs.(model_name).resid = Y - oo_.surgibbs.(model_name).Yhat;
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% Correct Yhat reported back to user
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oo_.surgibbs.(model_name).Yhat = oo_.surgibbs.(model_name).Yhat + lhssub;
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yhatname = [model_name '_FIT'];
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ds.(yhatname) = dseries(oo_.surgibbs.(model_name).Yhat, fp, yhatname);
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2019-02-26 16:56:20 +01:00
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2019-11-20 16:53:53 +01:00
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% Compute and save posterior densities.
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for i=1:nparams
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xx = oo_.surgibbs.(model_name).betadraws(:,i);
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nn = length(xx);
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bandwidth = mh_optimal_bandwidth(xx, nn, 0, 'gaussian');
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[x, f] = kernel_density_estimate(xx, 512, nn, bandwidth, 'gaussian');
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oo_.surgibbs.(model_name).posterior.density.(param_names{i}) = [x, f];
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end
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% Update model1s parameters with posterior mean.
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oo_.surgibbs.(model_name).param_idxs = zeros(length(param_names), 1);
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2019-02-26 16:56:20 +01:00
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for i = 1:length(param_names)
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2019-11-20 16:53:53 +01:00
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if ~strcmp(param_names{i}, 'intercept')
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oo_.surgibbs.(model_name).param_idxs(i) = find(strcmp(M_.param_names, param_names{i}));
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M_.params(oo_.surgibbs.(model_name).param_idxs(i)) = oo_.surgibbs.(model_name).posterior.mean.beta(i);
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end
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2019-02-26 16:56:20 +01:00
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end
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2019-11-20 16:53:53 +01:00
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oo_.surgibbs.(model_name).pnames = param_names;
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oo_.surgibbs.(model_name).neqs = m;
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% Estimate for sigma^2
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SS_res = oo_.surgibbs.(model_name).resid'*oo_.surgibbs.(model_name).resid;
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oo_.surgibbs.(model_name).s2 = SS_res/oo_.surgibbs.(model_name).dof;
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% R^2
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ym = Y - mean(Y);
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SS_tot = ym'*ym;
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oo_.surgibbs.(model_name).R2 = 1 - SS_res/SS_tot;
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2017-07-13 13:41:40 +02:00
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2019-02-26 14:32:19 +01:00
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% Write .inc file
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2019-11-20 16:53:53 +01:00
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write_param_init_inc_file('surgibbs', model_name, oo_.surgibbs.(model_name).param_idxs, oo_.surgibbs.(model_name).posterior.mean.beta);
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2019-02-26 14:32:19 +01:00
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2017-07-17 17:47:29 +02:00
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%% Print Output
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2019-01-11 16:00:20 +01:00
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if ~options_.noprint
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2019-11-25 14:09:55 +01:00
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ttitle = 'Gibbs Sampling on SUR';
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2019-11-20 16:53:53 +01:00
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preamble = {['Model name: ' model_name], ...
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sprintf('No. Equations: %d', oo_.surgibbs.(model_name).neqs), ...
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sprintf('No. Independent Variables: %d', size(X, 2)), ...
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sprintf('Observations: %d', oo_.surgibbs.(model_name).dof)};
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2019-11-25 11:48:09 +01:00
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2019-11-20 16:53:53 +01:00
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afterward = {sprintf('s^2: %f', oo_.surgibbs.(model_name).s2), sprintf('R^2: %f', oo_.surgibbs.(model_name).R2)};
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dyn_table(ttitle, preamble, afterward, param_names,...
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{'Posterior mean', 'Posterior std.'}, 4,...
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[oo_.surgibbs.(model_name).posterior.mean.beta, sqrt(diag(oo_.surgibbs.(model_name).posterior.variance.beta))]);
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2019-01-11 16:00:20 +01:00
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end
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2017-07-17 17:47:29 +02:00
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%% Plot
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2019-01-11 16:00:20 +01:00
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if ~options_.nograph
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figure
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nrows = 5;
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ncols = floor(nparams/nrows);
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if mod(nparams, nrows) ~= 0
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ncols = ncols + 1;
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end
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for j = 1:length(param_names)
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subplot(nrows, ncols, j)
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2019-02-28 11:49:50 +01:00
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histogram(oo_.surgibbs.(model_name).betadraws(:, j))
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hc = histcounts(oo_.surgibbs.(model_name).betadraws(:, j));
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2019-11-20 16:53:53 +01:00
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line([oo_.surgibbs.(model_name).posterior.mean.beta(j) oo_.surgibbs.(model_name).posterior.mean.beta(j)], [min(hc) max(hc)], 'Color', 'red');
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2019-01-11 16:00:20 +01:00
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title(param_names{j}, 'Interpreter', 'none')
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end
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2017-07-12 18:12:26 +02:00
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end
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end
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