From 3d9f12d769b4ad81b46b1d6d17c75ed08914db48 Mon Sep 17 00:00:00 2001
From: Johannes Pfeifer <jpfeifer@gmx.de>
Date: Fri, 24 Jul 2015 15:08:52 +0200
Subject: [PATCH 1/5] Add check whether prior mean violates Generalized
 (Inverse) Gamma

Performs check as for generalized beta distribution
---
 matlab/set_prior.m | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/matlab/set_prior.m b/matlab/set_prior.m
index a72a6204d..16e27f3f3 100644
--- a/matlab/set_prior.m
+++ b/matlab/set_prior.m
@@ -196,6 +196,9 @@ k2 = find(isnan(bayestopt_.p4(k)));
 bayestopt_.p3(k(k1)) = zeros(length(k1),1);
 bayestopt_.p4(k(k2)) = Inf(length(k2),1);
 for i=1:length(k)
+    if (bayestopt_.p1(k(i))<bayestopt_.p3(k(i))) || (bayestopt_.p1(k(i))>bayestopt_.p4(k(i)))
+        error(['The prior mean of ' bayestopt_.name{k(i)} ' has to be above the lower (' num2str(bayestopt_.p3(k(i))) ') bound of the Gamma prior density!']);
+    end
     if isinf(bayestopt_.p2(k(i)))
         error(['Infinite prior standard deviation for parameter ' bayestopt_.name{k(i)}  ' is not allowed (Gamma prior)!'])
     end
@@ -224,6 +227,9 @@ k2 = find(isnan(bayestopt_.p4(k)));
 bayestopt_.p3(k(k1)) = zeros(length(k1),1);
 bayestopt_.p4(k(k2)) = Inf(length(k2),1);
 for i=1:length(k)
+    if (bayestopt_.p1(k(i))<bayestopt_.p3(k(i))) || (bayestopt_.p1(k(i))>bayestopt_.p4(k(i)))
+        error(['The prior mean of ' bayestopt_.name{k(i)} ' has to be above the lower (' num2str(bayestopt_.p3(k(i))) ') bound of the Inverse Gamma prior density!']);
+    end
     [bayestopt_.p6(k(i)),bayestopt_.p7(k(i))] = ...
         inverse_gamma_specification(bayestopt_.p1(k(i))-bayestopt_.p3(k(i)),bayestopt_.p2(k(i)),1,0) ;
     bayestopt_.p5(k(i)) = compute_prior_mode([ bayestopt_.p6(k(i)) , bayestopt_.p7(k(i)) , bayestopt_.p3(k(i)) ], 4) ;
@@ -246,6 +252,9 @@ k2 = find(isnan(bayestopt_.p4(k)));
 bayestopt_.p3(k(k1)) = zeros(length(k1),1);
 bayestopt_.p4(k(k2)) = Inf(length(k2),1);
 for i=1:length(k)
+    if (bayestopt_.p1(k(i))<bayestopt_.p3(k(i))) || (bayestopt_.p1(k(i))>bayestopt_.p4(k(i)))
+        error(['The prior mean of ' bayestopt_.name{k(i)} ' has to be above the lower (' num2str(bayestopt_.p3(k(i))) ') bound of the Inverse Gamma II prior density!']);
+    end
     [bayestopt_.p6(k(i)),bayestopt_.p7(k(i))] = ...
         inverse_gamma_specification(bayestopt_.p1(k(i))-bayestopt_.p3(k(i)),bayestopt_.p2(k(i)),2,0);
     bayestopt_.p5(k(i)) = compute_prior_mode([ bayestopt_.p6(k(i)) , bayestopt_.p7(k(i)) , bayestopt_.p3(k(i)) ], 6) ;

From 560d9719db4114e34044a2e38a3525737faa6943 Mon Sep 17 00:00:00 2001
From: Johannes Pfeifer <jpfeifer@gmx.de>
Date: Fri, 24 Jul 2015 15:59:40 +0200
Subject: [PATCH 2/5] Correct copy and paste mistake in prior_draw.m for
 inverse gamma II

In case of bound violations, it was instead sampled from inverse gamma I
---
 matlab/prior_draw.m | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/matlab/prior_draw.m b/matlab/prior_draw.m
index 8923be87c..9a22dc39c 100644
--- a/matlab/prior_draw.m
+++ b/matlab/prior_draw.m
@@ -139,7 +139,7 @@ if inverse_gamma_2_draws
     out_of_bound = find( (pdraw(inverse_gamma_2_index)'>ub(inverse_gamma_2_index)) | (pdraw(inverse_gamma_2_index)'<lb(inverse_gamma_2_index)));
     while ~isempty(out_of_bound),
         pdraw(inverse_gamma_2_index(out_of_bound)) = ...
-            sqrt(1./gamrnd(p7(inverse_gamma_2_index(out_of_bound))/2,2./p6(inverse_gamma_2_index(out_of_bound))))+p3(inverse_gamma_2_index(out_of_bound));
+            1./gamrnd(p7(inverse_gamma_2_index(out_of_bound))/2,2./p6(inverse_gamma_2_index(out_of_bound)))+p3(inverse_gamma_2_index(out_of_bound));
         out_of_bound = find( (pdraw(inverse_gamma_2_index)'>ub(inverse_gamma_2_index)) | (pdraw(inverse_gamma_2_index)'<lb(inverse_gamma_2_index)));
     end
 end

From b5aeefe526af0b024b2a52d09cb819d831822497 Mon Sep 17 00:00:00 2001
From: Johannes Pfeifer <jpfeifer@gmx.de>
Date: Fri, 24 Jul 2015 16:23:40 +0200
Subject: [PATCH 3/5] Make prior_draw.m check for uniform distribution
 violation of bounds

Follows the logic for all other distributions
---
 matlab/prior_draw.m | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/matlab/prior_draw.m b/matlab/prior_draw.m
index 9a22dc39c..937730748 100644
--- a/matlab/prior_draw.m
+++ b/matlab/prior_draw.m
@@ -1,4 +1,4 @@
-function pdraw = prior_draw(init,uniform)
+function pdraw = prior_draw(init,uniform) % --*-- Unitary tests --*--
 % This function generate one draw from the joint prior distribution.
 % 
 % INPUTS 
@@ -93,6 +93,11 @@ end
 
 if uniform_draws
     pdraw(uniform_index) = rand(length(uniform_index),1).*(p4(uniform_index)-p3(uniform_index)) + p3(uniform_index);  
+    out_of_bound = find( (pdraw(uniform_index)'>ub(uniform_index)) | (pdraw(uniform_index)'<lb(uniform_index)));
+    while ~isempty(out_of_bound),
+        pdraw(uniform_index) = rand(length(uniform_index),1).*(p4(uniform_index)-p3(uniform_index)) + p3(uniform_index);
+        out_of_bound = find( (pdraw(uniform_index)'>ub(uniform_index)) | (pdraw(uniform_index)'<lb(uniform_index)));
+    end
 end
 
 if gaussian_draws
@@ -143,3 +148,4 @@ if inverse_gamma_2_draws
         out_of_bound = find( (pdraw(inverse_gamma_2_index)'>ub(inverse_gamma_2_index)) | (pdraw(inverse_gamma_2_index)'<lb(inverse_gamma_2_index)));
     end
 end
+

From 8b49b30f917700ea353a8aa8e573afe5f25277b9 Mon Sep 17 00:00:00 2001
From: Johannes Pfeifer <jpfeifer@gmx.de>
Date: Fri, 24 Jul 2015 16:32:05 +0200
Subject: [PATCH 4/5] Add unit tests for prior sampling

---
 matlab/prior_draw.m | 368 +++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 367 insertions(+), 1 deletion(-)

diff --git a/matlab/prior_draw.m b/matlab/prior_draw.m
index 937730748..cd0c32a50 100644
--- a/matlab/prior_draw.m
+++ b/matlab/prior_draw.m
@@ -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) 2006-2010 Dynare Team
+% Copyright (C) 2006-2015 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

From 19fdb309ac18befd9932599047f823a126349fe3 Mon Sep 17 00:00:00 2001
From: Johannes Pfeifer <jpfeifer@gmx.de>
Date: Mon, 27 Jul 2015 10:19:02 +0200
Subject: [PATCH 5/5] Fix computation of prior mode for uniform distribution

While this code is never actually used, we return the prior mean, which is directly given by the first hyperparameter.
---
 matlab/distributions/compute_prior_mode.m | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/matlab/distributions/compute_prior_mode.m b/matlab/distributions/compute_prior_mode.m
index 50df6a168..e6d35920c 100644
--- a/matlab/distributions/compute_prior_mode.m
+++ b/matlab/distributions/compute_prior_mode.m
@@ -22,7 +22,7 @@ function m = compute_prior_mode(hyperparameters,shape)
 % [3] The uniform distribution has an infinity of modes. In this case the function returns the prior mean.
 % [4] For the beta distribution we can have 1, 2 or an infinity of modes.
 
-% Copyright (C) 2009 Dynare Team
+% Copyright (C) 2009-2015 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -74,7 +74,7 @@ switch shape
         m = m + hyperparameters(3);
     end
   case 5
-    m = .5*(hyperparameters(2)-hyperparameters(1)) ;
+    m = hyperparameters(1);
   case 6
     % s  = hyperparameters(1)
     % nu = hyperparameters(2)