Add unit tests for prior sampling
Johannes Pfeifer
parent
b5aeefe526
commit
8b49b30f91
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@ -15,7 +15,7 @@ function pdraw = prior_draw(init,uniform) % --*-- Unitary tests --*--
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% NOTE 1. Input arguments 1 an 2 are only needed for initialization.
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% NOTE 2. A given draw from the joint prior distribution does not satisfy BK conditions a priori.
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% Copyright (C) 2006-2010 Dynare Team
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% Copyright (C) 2006-2015 Dynare Team
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%
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% This file is part of Dynare.
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%
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@ -149,3 +149,369 @@ if inverse_gamma_2_draws
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end
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end
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%@test:1
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%$ %% Initialize required structures
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%$ options_.prior_trunc=0;
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%$ options_.initialize_estimated_parameters_with_the_prior_mode=0;
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%$
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%$ M_.dname='test';
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%$ M_.param_names = 'alp';
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%$ ndraws=100000;
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%$ global estim_params_
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%$ estim_params_.var_exo = [];
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%$ estim_params_.var_endo = [];
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%$ estim_params_.corrx = [];
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%$ estim_params_.corrn = [];
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%$ estim_params_.param_vals = [];
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%$ estim_params_.param_vals = [1, NaN, (-Inf), Inf, 1, 0.356, 0.02, NaN, NaN, NaN ];
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%$
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%$ %% beta
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%$ estim_params_.param_vals(1,3)= -Inf;%LB
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%$ estim_params_.param_vals(1,4)= +Inf;%UB
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%$ estim_params_.param_vals(1,5)= 1;%Shape
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%$ estim_params_.param_vals(1,6)=0.5;
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%$ estim_params_.param_vals(1,7)=0.01;
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%$ estim_params_.param_vals(1,8)=NaN;
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%$ estim_params_.param_vals(1,9)=NaN;
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%$
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%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
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%$
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%$ pdraw = prior_draw(1,0);
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%$ pdraw_vec=NaN(ndraws,1);
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%$ for ii=1:ndraws
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%$ pdraw_vec(ii)=prior_draw(0,0);
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%$ end
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%$
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%$ 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)
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%$ error('Beta prior wrong')
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%$ end
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%$
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%$
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%$ %% Gamma
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%$ estim_params_.param_vals(1,3)= -Inf;%LB
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%$ estim_params_.param_vals(1,4)= +Inf;%UB
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%$ estim_params_.param_vals(1,5)= 2;%Shape
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%$ estim_params_.param_vals(1,6)=0.5;
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%$ estim_params_.param_vals(1,7)=0.01;
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%$ estim_params_.param_vals(1,8)=NaN;
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%$ estim_params_.param_vals(1,9)=NaN;
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%$
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%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
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%$
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%$ pdraw = prior_draw(1,0);
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%$ pdraw_vec=NaN(ndraws,1);
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%$ for ii=1:ndraws
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%$ pdraw_vec(ii)=prior_draw(0,0);
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%$ end
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%$
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%$ 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)
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%$ error('Gamma prior wrong')
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%$ end
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%$
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%$ %% Normal
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%$ estim_params_.param_vals(1,3)= -Inf;%LB
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%$ estim_params_.param_vals(1,4)= +Inf;%UB
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%$ estim_params_.param_vals(1,5)= 3;%Shape
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%$ estim_params_.param_vals(1,6)=0.5;
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%$ estim_params_.param_vals(1,7)=0.01;
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%$ estim_params_.param_vals(1,8)=NaN;
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%$ estim_params_.param_vals(1,9)=NaN;
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%$
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%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
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%$
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%$ pdraw = prior_draw(1,0);
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%$ pdraw_vec=NaN(ndraws,1);
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%$ for ii=1:ndraws
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%$ pdraw_vec(ii)=prior_draw(0,0);
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%$ end
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%$
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%$ 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
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%$ error('Normal prior wrong')
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%$ end
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%$
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%$ %% inverse gamma distribution (type 1)
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%$ estim_params_.param_vals(1,3)= -Inf;%LB
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%$ estim_params_.param_vals(1,4)= +Inf;%UB
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%$ estim_params_.param_vals(1,5)= 4;%Shape
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%$ estim_params_.param_vals(1,6)=0.5;
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%$ estim_params_.param_vals(1,7)=0.01;
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%$ estim_params_.param_vals(1,8)=NaN;
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%$ estim_params_.param_vals(1,9)=NaN;
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%$
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%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
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%$
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%$ pdraw = prior_draw(1,0);
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%$ pdraw_vec=NaN(ndraws,1);
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%$ for ii=1:ndraws
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%$ pdraw_vec(ii)=prior_draw(0,0);
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%$ end
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%$
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%$ 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)
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%$ error('inverse gamma distribution (type 1) prior wrong')
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%$ end
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%$
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%$ %% Uniform
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%$ estim_params_.param_vals(1,3)= -Inf;%LB
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%$ estim_params_.param_vals(1,4)= +Inf;%UB
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%$ estim_params_.param_vals(1,5)= 5;%Shape
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%$ estim_params_.param_vals(1,6)=0.5;
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%$ estim_params_.param_vals(1,7)=sqrt(12)^(-1)*(1-0);
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%$ estim_params_.param_vals(1,8)=NaN;
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%$ estim_params_.param_vals(1,9)=NaN;
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%$
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%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
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%$
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%$ pdraw = prior_draw(1,0);
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%$ pdraw_vec=NaN(ndraws,1);
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%$ for ii=1:ndraws
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%$ pdraw_vec(ii)=prior_draw(0,0);
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%$ end
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%$
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%$ 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)
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%$ error('Uniform prior wrong')
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%$ end
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%$
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%$ %% inverse gamma distribution (type 2)
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%$ estim_params_.param_vals(1,3)= -Inf;%LB
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%$ estim_params_.param_vals(1,4)= +Inf;%UB
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%$ estim_params_.param_vals(1,5)= 6;%Shape
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%$ estim_params_.param_vals(1,6)=0.5;
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%$ estim_params_.param_vals(1,7)=0.01;
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%$ estim_params_.param_vals(1,8)=NaN;
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%$ estim_params_.param_vals(1,9)=NaN;
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%$
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%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
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%$
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%$ pdraw = prior_draw(1,0);
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%$ pdraw_vec=NaN(ndraws,1);
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%$ for ii=1:ndraws
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%$ pdraw_vec(ii)=prior_draw(0,0);
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%$ end
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%$
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%$ 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)
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%$ error('inverse gamma distribution (type 2) prior wrong')
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%$ end
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%$
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%$
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%$ %%%%%%%%%%%%%%%%%%%%%% Generalized distributions %%%%%%%%%%%%%%%%%%%%%
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%$
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%$ %% beta
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%$ estim_params_.param_vals(1,3)= -Inf;%LB
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%$ estim_params_.param_vals(1,4)= +Inf;%UB
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%$ estim_params_.param_vals(1,5)= 1;%Shape
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%$ estim_params_.param_vals(1,6)=1.5;
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%$ estim_params_.param_vals(1,7)=0.01;
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%$ estim_params_.param_vals(1,8)=1;
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%$ estim_params_.param_vals(1,9)=3;
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%$
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%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
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%$
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%$ pdraw = prior_draw(1,0);
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%$ pdraw_vec=NaN(ndraws,1);
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%$ for ii=1:ndraws
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%$ pdraw_vec(ii)=prior_draw(0,0);
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%$ end
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%$
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%$ 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))
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%$ error('Beta prior wrong')
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%$ end
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%$
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%$ %% Gamma
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%$ estim_params_.param_vals(1,3)= -Inf;%LB
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%$ estim_params_.param_vals(1,4)= +Inf;%UB
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%$ estim_params_.param_vals(1,5)= 2;%Shape
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%$ estim_params_.param_vals(1,6)=1.5;
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%$ estim_params_.param_vals(1,7)=0.01;
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%$ estim_params_.param_vals(1,8)=1;
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%$ estim_params_.param_vals(1,9)=NaN;
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%$
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%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
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%$
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%$ pdraw = prior_draw(1,0);
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%$ pdraw_vec=NaN(ndraws,1);
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%$ for ii=1:ndraws
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%$ pdraw_vec(ii)=prior_draw(0,0);
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%$ end
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%$
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%$ 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))
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%$ error('Gamma prior wrong')
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%$ end
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%$
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%$ %% inverse gamma distribution (type 1)
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%$ estim_params_.param_vals(1,3)= -Inf;%LB
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%$ estim_params_.param_vals(1,4)= +Inf;%UB
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%$ estim_params_.param_vals(1,5)= 4;%Shape
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%$ estim_params_.param_vals(1,6)=1.5;
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%$ estim_params_.param_vals(1,7)=0.01;
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%$ estim_params_.param_vals(1,8)=1;
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%$ estim_params_.param_vals(1,9)=NaN;
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%$
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%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
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%$
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%$ pdraw = prior_draw(1,0);
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%$ pdraw_vec=NaN(ndraws,1);
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%$ for ii=1:ndraws
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%$ pdraw_vec(ii)=prior_draw(0,0);
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%$ end
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%$
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%$ 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))
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%$ error('inverse gamma distribution (type 1) prior wrong')
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%$ end
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%$
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%$ %% Uniform
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%$ estim_params_.param_vals(1,3)= -Inf;%LB
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%$ estim_params_.param_vals(1,4)= +Inf;%UB
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%$ estim_params_.param_vals(1,5)= 5;%Shape
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%$ estim_params_.param_vals(1,6)=1.5;
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%$ estim_params_.param_vals(1,7)=0.01;
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%$ estim_params_.param_vals(1,8)=NaN;
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%$ estim_params_.param_vals(1,9)=NaN;
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%$
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%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
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%$
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%$ pdraw = prior_draw(1,0);
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%$ pdraw_vec=NaN(ndraws,1);
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%$ for ii=1:ndraws
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%$ pdraw_vec(ii)=prior_draw(0,0);
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%$ end
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%$
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%$ 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))
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%$ error('Uniform prior wrong')
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%$ end
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%$
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%$ %% inverse gamma distribution (type 2)
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%$ estim_params_.param_vals(1,3)= -Inf;%LB
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%$ estim_params_.param_vals(1,4)= +Inf;%UB
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%$ estim_params_.param_vals(1,5)= 6;%Shape
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%$ estim_params_.param_vals(1,6)=1.5;
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%$ estim_params_.param_vals(1,7)=0.01;
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%$ estim_params_.param_vals(1,8)=1;
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%$ estim_params_.param_vals(1,9)=NaN;
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%$
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%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
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%$
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%$ pdraw = prior_draw(1,0);
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%$ pdraw_vec=NaN(ndraws,1);
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%$ for ii=1:ndraws
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%$ pdraw_vec(ii)=prior_draw(0,0);
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%$ end
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%$
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%$ 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))
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%$ error('inverse gamma distribution (type 2) prior wrong')
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%$ end
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%$
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%$ %%%%%%%%%%%% With prior truncation
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%$ options_.prior_trunc=.4;
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%$
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%$ %% beta
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%$ estim_params_.param_vals(1,3)= -Inf;%LB
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%$ estim_params_.param_vals(1,4)= +Inf;%UB
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%$ estim_params_.param_vals(1,5)= 1;%Shape
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%$ estim_params_.param_vals(1,6)=1.5;
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%$ estim_params_.param_vals(1,7)=0.01;
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%$ estim_params_.param_vals(1,8)=1;
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%$ estim_params_.param_vals(1,9)=3;
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%$
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%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
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%$ bounds = prior_bounds(bayestopt_,options_)';
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%$
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%$ pdraw = prior_draw(1,0);
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%$ pdraw_vec=NaN(ndraws,1);
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%$ for ii=1:ndraws
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%$ pdraw_vec(ii)=prior_draw(0,0);
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%$ end
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%$
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%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>5e-3 || any(pdraw_vec<bounds.lb) || any(pdraw_vec>bounds.ub)
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%$ error('Beta prior wrong')
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%$ end
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%$
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%$ %% Gamma
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%$ estim_params_.param_vals(1,3)= -Inf;%LB
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%$ estim_params_.param_vals(1,4)= +Inf;%UB
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%$ estim_params_.param_vals(1,5)= 2;%Shape
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%$ estim_params_.param_vals(1,6)=1.5;
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%$ estim_params_.param_vals(1,7)=0.01;
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%$ estim_params_.param_vals(1,8)=1;
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%$ estim_params_.param_vals(1,9)=NaN;
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%$
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%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
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%$ bounds = prior_bounds(bayestopt_,options_)';
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%$
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%$ pdraw = prior_draw(1,0);
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%$ pdraw_vec=NaN(ndraws,1);
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%$ for ii=1:ndraws
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%$ pdraw_vec(ii)=prior_draw(0,0);
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%$ end
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%$
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%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>5e-3 || any(pdraw_vec<bounds.lb) || any(pdraw_vec>bounds.ub)
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%$ error('Gamma prior wrong')
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%$ end
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%$
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%$ %% inverse gamma distribution (type 1)
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%$ estim_params_.param_vals(1,3)= -Inf;%LB
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%$ estim_params_.param_vals(1,4)= +Inf;%UB
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%$ estim_params_.param_vals(1,5)= 4;%Shape
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%$ estim_params_.param_vals(1,6)=1.5;
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%$ estim_params_.param_vals(1,7)=0.01;
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%$ estim_params_.param_vals(1,8)=1;
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%$ estim_params_.param_vals(1,9)=NaN;
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%$
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%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
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%$ bounds = prior_bounds(bayestopt_,options_)';
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%$
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%$ pdraw = prior_draw(1,0);
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%$ pdraw_vec=NaN(ndraws,1);
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%$ for ii=1:ndraws
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%$ pdraw_vec(ii)=prior_draw(0,0);
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%$ end
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%$
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%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>5e-3 || any(pdraw_vec<bounds.lb) || any(pdraw_vec>bounds.ub)
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%$ error('inverse gamma distribution (type 1) prior wrong')
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%$ end
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%$
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%$ %% Uniform
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%$ estim_params_.param_vals(1,3)= -Inf;%LB
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%$ estim_params_.param_vals(1,4)= +Inf;%UB
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%$ estim_params_.param_vals(1,5)= 5;%Shape
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%$ estim_params_.param_vals(1,6)=1.5;
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%$ estim_params_.param_vals(1,7)=0.01;
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%$ estim_params_.param_vals(1,8)=NaN;
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%$ estim_params_.param_vals(1,9)=NaN;
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%$
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%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
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%$ bounds = prior_bounds(bayestopt_,options_)';
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%$
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%$ pdraw = prior_draw(1,0);
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%$ 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
|
||||
|
|
|
|||
Loading…
Reference in New Issue