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Johannes Pfeifer
dynare
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8b49b30f
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8b49b30f
authored
9 years ago
by
Johannes Pfeifer
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Add unit tests for prior sampling
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b5aeefe5
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matlab/prior_draw.m
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@@ -15,7 +15,7 @@ function pdraw = prior_draw(init,uniform) % --*-- Unitary tests --*--
% NOTE 1. Input arguments 1 an 2 are only needed for initialization.
% NOTE 2. A given draw from the joint prior distribution does not satisfy BK conditions a priori.
% Copyright (C) 2006-201
0
Dynare Team
% Copyright (C) 2006-201
5
Dynare Team
%
% This file is part of Dynare.
%
...
...
@@ -149,3 +149,369 @@ if inverse_gamma_2_draws
end
end
%@test:1
%$ %% Initialize required structures
%$ options_.prior_trunc=0;
%$ options_.initialize_estimated_parameters_with_the_prior_mode=0;
%$
%$ M_.dname='test';
%$ M_.param_names = 'alp';
%$ ndraws=100000;
%$ global estim_params_
%$ estim_params_.var_exo = [];
%$ estim_params_.var_endo = [];
%$ estim_params_.corrx = [];
%$ estim_params_.corrn = [];
%$ estim_params_.param_vals = [];
%$ estim_params_.param_vals = [1, NaN, (-Inf), Inf, 1, 0.356, 0.02, NaN, NaN, NaN ];
%$
%$ %% beta
%$ estim_params_.param_vals(1,3)= -Inf;%LB
%$ estim_params_.param_vals(1,4)= +Inf;%UB
%$ estim_params_.param_vals(1,5)= 1;%Shape
%$ estim_params_.param_vals(1,6)=0.5;
%$ estim_params_.param_vals(1,7)=0.01;
%$ estim_params_.param_vals(1,8)=NaN;
%$ estim_params_.param_vals(1,9)=NaN;
%$
%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
%$
%$ pdraw = prior_draw(1,0);
%$ pdraw_vec=NaN(ndraws,1);
%$ for ii=1:ndraws
%$ pdraw_vec(ii)=prior_draw(0,0);
%$ end
%$
%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4 || any(pdraw_vec<0) || any(pdraw_vec>1)
%$ error('Beta prior wrong')
%$ end
%$
%$
%$ %% Gamma
%$ estim_params_.param_vals(1,3)= -Inf;%LB
%$ estim_params_.param_vals(1,4)= +Inf;%UB
%$ estim_params_.param_vals(1,5)= 2;%Shape
%$ estim_params_.param_vals(1,6)=0.5;
%$ estim_params_.param_vals(1,7)=0.01;
%$ estim_params_.param_vals(1,8)=NaN;
%$ estim_params_.param_vals(1,9)=NaN;
%$
%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
%$
%$ pdraw = prior_draw(1,0);
%$ pdraw_vec=NaN(ndraws,1);
%$ for ii=1:ndraws
%$ pdraw_vec(ii)=prior_draw(0,0);
%$ end
%$
%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4 || any(pdraw_vec<0)
%$ error('Gamma prior wrong')
%$ end
%$
%$ %% Normal
%$ estim_params_.param_vals(1,3)= -Inf;%LB
%$ estim_params_.param_vals(1,4)= +Inf;%UB
%$ estim_params_.param_vals(1,5)= 3;%Shape
%$ estim_params_.param_vals(1,6)=0.5;
%$ estim_params_.param_vals(1,7)=0.01;
%$ estim_params_.param_vals(1,8)=NaN;
%$ estim_params_.param_vals(1,9)=NaN;
%$
%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
%$
%$ pdraw = prior_draw(1,0);
%$ pdraw_vec=NaN(ndraws,1);
%$ for ii=1:ndraws
%$ pdraw_vec(ii)=prior_draw(0,0);
%$ end
%$
%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4
%$ error('Normal prior wrong')
%$ end
%$
%$ %% inverse gamma distribution (type 1)
%$ estim_params_.param_vals(1,3)= -Inf;%LB
%$ estim_params_.param_vals(1,4)= +Inf;%UB
%$ estim_params_.param_vals(1,5)= 4;%Shape
%$ estim_params_.param_vals(1,6)=0.5;
%$ estim_params_.param_vals(1,7)=0.01;
%$ estim_params_.param_vals(1,8)=NaN;
%$ estim_params_.param_vals(1,9)=NaN;
%$
%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
%$
%$ pdraw = prior_draw(1,0);
%$ pdraw_vec=NaN(ndraws,1);
%$ for ii=1:ndraws
%$ pdraw_vec(ii)=prior_draw(0,0);
%$ end
%$
%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4 || any(pdraw_vec<0)
%$ error('inverse gamma distribution (type 1) prior wrong')
%$ end
%$
%$ %% Uniform
%$ estim_params_.param_vals(1,3)= -Inf;%LB
%$ estim_params_.param_vals(1,4)= +Inf;%UB
%$ estim_params_.param_vals(1,5)= 5;%Shape
%$ estim_params_.param_vals(1,6)=0.5;
%$ estim_params_.param_vals(1,7)=sqrt(12)^(-1)*(1-0);
%$ estim_params_.param_vals(1,8)=NaN;
%$ estim_params_.param_vals(1,9)=NaN;
%$
%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
%$
%$ pdraw = prior_draw(1,0);
%$ pdraw_vec=NaN(ndraws,1);
%$ for ii=1:ndraws
%$ pdraw_vec(ii)=prior_draw(0,0);
%$ end
%$
%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-2 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-3 || any(pdraw_vec<0) || any(pdraw_vec>1)
%$ error('Uniform prior wrong')
%$ end
%$
%$ %% inverse gamma distribution (type 2)
%$ estim_params_.param_vals(1,3)= -Inf;%LB
%$ estim_params_.param_vals(1,4)= +Inf;%UB
%$ estim_params_.param_vals(1,5)= 6;%Shape
%$ estim_params_.param_vals(1,6)=0.5;
%$ estim_params_.param_vals(1,7)=0.01;
%$ estim_params_.param_vals(1,8)=NaN;
%$ estim_params_.param_vals(1,9)=NaN;
%$
%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
%$
%$ pdraw = prior_draw(1,0);
%$ pdraw_vec=NaN(ndraws,1);
%$ for ii=1:ndraws
%$ pdraw_vec(ii)=prior_draw(0,0);
%$ end
%$
%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4 || any(pdraw_vec<0)
%$ error('inverse gamma distribution (type 2) prior wrong')
%$ end
%$
%$
%$ %%%%%%%%%%%%%%%%%%%%%% Generalized distributions %%%%%%%%%%%%%%%%%%%%%
%$
%$ %% beta
%$ estim_params_.param_vals(1,3)= -Inf;%LB
%$ estim_params_.param_vals(1,4)= +Inf;%UB
%$ estim_params_.param_vals(1,5)= 1;%Shape
%$ estim_params_.param_vals(1,6)=1.5;
%$ estim_params_.param_vals(1,7)=0.01;
%$ estim_params_.param_vals(1,8)=1;
%$ estim_params_.param_vals(1,9)=3;
%$
%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
%$
%$ pdraw = prior_draw(1,0);
%$ pdraw_vec=NaN(ndraws,1);
%$ for ii=1:ndraws
%$ pdraw_vec(ii)=prior_draw(0,0);
%$ end
%$
%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4 || any(pdraw_vec<estim_params_.param_vals(1,3)) || any(pdraw_vec>estim_params_.param_vals(1,4))
%$ error('Beta prior wrong')
%$ end
%$
%$ %% Gamma
%$ estim_params_.param_vals(1,3)= -Inf;%LB
%$ estim_params_.param_vals(1,4)= +Inf;%UB
%$ estim_params_.param_vals(1,5)= 2;%Shape
%$ estim_params_.param_vals(1,6)=1.5;
%$ estim_params_.param_vals(1,7)=0.01;
%$ estim_params_.param_vals(1,8)=1;
%$ estim_params_.param_vals(1,9)=NaN;
%$
%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
%$
%$ pdraw = prior_draw(1,0);
%$ pdraw_vec=NaN(ndraws,1);
%$ for ii=1:ndraws
%$ pdraw_vec(ii)=prior_draw(0,0);
%$ end
%$
%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4 || any(pdraw_vec<estim_params_.param_vals(1,8))
%$ error('Gamma prior wrong')
%$ end
%$
%$ %% inverse gamma distribution (type 1)
%$ estim_params_.param_vals(1,3)= -Inf;%LB
%$ estim_params_.param_vals(1,4)= +Inf;%UB
%$ estim_params_.param_vals(1,5)= 4;%Shape
%$ estim_params_.param_vals(1,6)=1.5;
%$ estim_params_.param_vals(1,7)=0.01;
%$ estim_params_.param_vals(1,8)=1;
%$ estim_params_.param_vals(1,9)=NaN;
%$
%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
%$
%$ pdraw = prior_draw(1,0);
%$ pdraw_vec=NaN(ndraws,1);
%$ for ii=1:ndraws
%$ pdraw_vec(ii)=prior_draw(0,0);
%$ end
%$
%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4 || any(pdraw_vec<estim_params_.param_vals(1,8))
%$ error('inverse gamma distribution (type 1) prior wrong')
%$ end
%$
%$ %% Uniform
%$ estim_params_.param_vals(1,3)= -Inf;%LB
%$ estim_params_.param_vals(1,4)= +Inf;%UB
%$ estim_params_.param_vals(1,5)= 5;%Shape
%$ estim_params_.param_vals(1,6)=1.5;
%$ estim_params_.param_vals(1,7)=0.01;
%$ estim_params_.param_vals(1,8)=NaN;
%$ estim_params_.param_vals(1,9)=NaN;
%$
%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
%$
%$ pdraw = prior_draw(1,0);
%$ pdraw_vec=NaN(ndraws,1);
%$ for ii=1:ndraws
%$ pdraw_vec(ii)=prior_draw(0,0);
%$ end
%$
%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4 || any(pdraw_vec<estim_params_.param_vals(1,3)) || any(pdraw_vec>estim_params_.param_vals(1,4))
%$ error('Uniform prior wrong')
%$ end
%$
%$ %% inverse gamma distribution (type 2)
%$ estim_params_.param_vals(1,3)= -Inf;%LB
%$ estim_params_.param_vals(1,4)= +Inf;%UB
%$ estim_params_.param_vals(1,5)= 6;%Shape
%$ estim_params_.param_vals(1,6)=1.5;
%$ estim_params_.param_vals(1,7)=0.01;
%$ estim_params_.param_vals(1,8)=1;
%$ estim_params_.param_vals(1,9)=NaN;
%$
%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
%$
%$ pdraw = prior_draw(1,0);
%$ pdraw_vec=NaN(ndraws,1);
%$ for ii=1:ndraws
%$ pdraw_vec(ii)=prior_draw(0,0);
%$ end
%$
%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4 || any(pdraw_vec<estim_params_.param_vals(1,8))
%$ error('inverse gamma distribution (type 2) prior wrong')
%$ end
%$
%$ %%%%%%%%%%%% With prior truncation
%$ options_.prior_trunc=.4;
%$
%$ %% beta
%$ estim_params_.param_vals(1,3)= -Inf;%LB
%$ estim_params_.param_vals(1,4)= +Inf;%UB
%$ estim_params_.param_vals(1,5)= 1;%Shape
%$ estim_params_.param_vals(1,6)=1.5;
%$ estim_params_.param_vals(1,7)=0.01;
%$ estim_params_.param_vals(1,8)=1;
%$ estim_params_.param_vals(1,9)=3;
%$
%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
%$ bounds = prior_bounds(bayestopt_,options_)';
%$
%$ pdraw = prior_draw(1,0);
%$ pdraw_vec=NaN(ndraws,1);
%$ for ii=1:ndraws
%$ pdraw_vec(ii)=prior_draw(0,0);
%$ end
%$
%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>5e-3 || any(pdraw_vec<bounds.lb) || any(pdraw_vec>bounds.ub)
%$ error('Beta prior wrong')
%$ end
%$
%$ %% Gamma
%$ estim_params_.param_vals(1,3)= -Inf;%LB
%$ estim_params_.param_vals(1,4)= +Inf;%UB
%$ estim_params_.param_vals(1,5)= 2;%Shape
%$ estim_params_.param_vals(1,6)=1.5;
%$ estim_params_.param_vals(1,7)=0.01;
%$ estim_params_.param_vals(1,8)=1;
%$ estim_params_.param_vals(1,9)=NaN;
%$
%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
%$ bounds = prior_bounds(bayestopt_,options_)';
%$
%$ pdraw = prior_draw(1,0);
%$ pdraw_vec=NaN(ndraws,1);
%$ for ii=1:ndraws
%$ pdraw_vec(ii)=prior_draw(0,0);
%$ end
%$
%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>5e-3 || any(pdraw_vec<bounds.lb) || any(pdraw_vec>bounds.ub)
%$ error('Gamma prior wrong')
%$ end
%$
%$ %% inverse gamma distribution (type 1)
%$ estim_params_.param_vals(1,3)= -Inf;%LB
%$ estim_params_.param_vals(1,4)= +Inf;%UB
%$ estim_params_.param_vals(1,5)= 4;%Shape
%$ estim_params_.param_vals(1,6)=1.5;
%$ estim_params_.param_vals(1,7)=0.01;
%$ estim_params_.param_vals(1,8)=1;
%$ estim_params_.param_vals(1,9)=NaN;
%$
%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
%$ bounds = prior_bounds(bayestopt_,options_)';
%$
%$ pdraw = prior_draw(1,0);
%$ pdraw_vec=NaN(ndraws,1);
%$ for ii=1:ndraws
%$ pdraw_vec(ii)=prior_draw(0,0);
%$ end
%$
%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>5e-3 || any(pdraw_vec<bounds.lb) || any(pdraw_vec>bounds.ub)
%$ error('inverse gamma distribution (type 1) prior wrong')
%$ end
%$
%$ %% Uniform
%$ estim_params_.param_vals(1,3)= -Inf;%LB
%$ estim_params_.param_vals(1,4)= +Inf;%UB
%$ estim_params_.param_vals(1,5)= 5;%Shape
%$ estim_params_.param_vals(1,6)=1.5;
%$ estim_params_.param_vals(1,7)=0.01;
%$ estim_params_.param_vals(1,8)=NaN;
%$ estim_params_.param_vals(1,9)=NaN;
%$
%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
%$ bounds = prior_bounds(bayestopt_,options_)';
%$
%$ pdraw = prior_draw(1,0);
%$ pdraw_vec=NaN(ndraws,1);
%$ for ii=1:ndraws
%$ pdraw_vec(ii)=prior_draw(0,0);
%$ end
%$
%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>5e-3 || any(pdraw_vec<bounds.lb) || any(pdraw_vec>bounds.ub)
%$ error('Uniform prior wrong')
%$ end
%$
%$
%$ %% inverse gamma distribution (type 2)
%$ estim_params_.param_vals(1,3)= -Inf;%LB
%$ estim_params_.param_vals(1,4)= +Inf;%UB
%$ estim_params_.param_vals(1,5)= 6;%Shape
%$ estim_params_.param_vals(1,6)=1.5;
%$ estim_params_.param_vals(1,7)=0.01;
%$ estim_params_.param_vals(1,8)=1;
%$ estim_params_.param_vals(1,9)=NaN;
%$
%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
%$ bounds = prior_bounds(bayestopt_,options_)';
%$
%$ pdraw = prior_draw(1,0);
%$ pdraw_vec=NaN(ndraws,1);
%$ for ii=1:ndraws
%$ pdraw_vec(ii)=prior_draw(0,0);
%$ end
%$
%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>5e-3 || any(pdraw_vec<bounds.lb) || any(pdraw_vec>bounds.ub)
%$ error('inverse gamma distribution (type 2) prior wrong')
%$ end
%$
%@eof:1
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