Changed second input of prior_bounds routine.

Do not pass options_ structure but only the required field (prior_trunc).

matlab/PosteriorIRF_core1.m

@@ -77,7 +77,7 @@ if options_.dsge_var
     NumberOfLagsTimesNvobs = myinputs.NumberOfLagsTimesNvobs;
     Companion_matrix = myinputs.Companion_matrix;
     stock_irf_bvardsge = zeros(options_.irf,nvobs,M_.exo_nbr,MAX_nirfs_dsgevar);
-    bounds = prior_bounds(bayestopt_,options_);
+    bounds = prior_bounds(bayestopt_,options_.prior_trunc);
 end
 
 

matlab/dynare_estimation_1.m

@@ -427,7 +427,7 @@ end
 
 if (any(bayestopt_.pshape  >0 ) && options_.mh_replic) || ...
         (any(bayestopt_.pshape >0 ) && options_.load_mh_file)  %% not ML estimation
-    bounds = prior_bounds(bayestopt_,options_);
+    bounds = prior_bounds(bayestopt_, options_.prior_trunc);
     outside_bound_pars=find(xparam1 < bounds.lb | xparam1 > bounds.ub);
     if ~isempty(outside_bound_pars)
         for ii=1:length(outside_bound_pars)

matlab/dynare_estimation_init.m

@@ -315,7 +315,7 @@ if ~isempty(estim_params_) && ~all(strcmp(fieldnames(estim_params_),'full_calibr
             plot_priors(bayestopt_,M_,estim_params_,options_)
         end
         % Set prior bounds
-        bounds = prior_bounds(bayestopt_,options_);
+        bounds = prior_bounds(bayestopt_, options_.prior_trunc);
         bounds.lb = max(bounds.lb,lb);
         bounds.ub = min(bounds.ub,ub);
     else  % estimated parameters but no declared priors

matlab/evaluate_likelihood.m

@@ -70,6 +70,6 @@ if isempty(dataset)
     [dataset, dataset_info] = makedataset(options_);
 end
 
-llik = -dsge_likelihood(parameters,dataset,dataset_info,options_,M_,estim_params_,bayestopt_,prior_bounds(bayestopt_,options_),oo_);
+llik = -dsge_likelihood(parameters,dataset,dataset_info,options_,M_,estim_params_,bayestopt_,prior_bounds(bayestopt_,options_.prior_trunc),oo_);
 ldens = evaluate_prior(parameters);
 llik = llik - ldens;
\ No newline at end of file

matlab/gsa/prior_draw_gsa.m

@@ -58,7 +58,7 @@ if init
     [junk1,junk2,junk3,lb,ub,junk4] = set_prior(estim_params_,M_,options_); %Prepare bounds
     if ~isempty(bayestopt_) && any(bayestopt_.pshape > 0)
         % Set prior bounds
-        bounds = prior_bounds(bayestopt_,options_);
+        bounds = prior_bounds(bayestopt_, options_.prior_trunc);
         bounds.lb = max(bounds.lb,lb);
         bounds.ub = min(bounds.ub,ub);
     else  % estimated parameters but no declared priors

matlab/gsa/redform_map.m

@@ -56,7 +56,7 @@ pvalue_corr = options_gsa_.alpha2_redform;
 pnames = M_.param_names(estim_params_.param_vals(:,1),:);
 fname_ = M_.fname;
 
-bounds = prior_bounds(bayestopt_,options_);
+bounds = prior_bounds(bayestopt_, options_.prior_trunc);
 
 if nargin==0,
     dirname='';

matlab/gsa/stab_map_.m

@@ -99,7 +99,7 @@ p4 = bayestopt_.p4(nshock+1:end);
 [junk1,junk2,junk3,lb,ub,junk4] = set_prior(estim_params_,M_,options_); %Prepare bounds
 if ~isempty(bayestopt_) && any(bayestopt_.pshape > 0)
     % Set prior bounds
-    bounds = prior_bounds(bayestopt_,options_);
+    bounds = prior_bounds(bayestopt_, options_.trunc);
     bounds.lb = max(bounds.lb,lb);
     bounds.ub = min(bounds.ub,ub);
 else  % estimated parameters but no declared priors

matlab/identification_analysis.m

@@ -147,7 +147,7 @@ if info(1)==0,
             info = stoch_simul(char(options_.varobs));
             dataset_ = dseries(oo_.endo_simul(options_.varobs_id,100+1:end)',dates('1Q1'), options_.varobs);            
             derivatives_info.no_DLIK=1;
-            bounds = prior_bounds(bayestopt_,options_);
+            bounds = prior_bounds(bayestopt_, options_.prior_trunc);
             [fval,DLIK,AHess,cost_flag,ys,trend_coeff,info,M_,options_,bayestopt_,oo_] = dsge_likelihood(params',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_,derivatives_info);
 %                 fval = DsgeLikelihood(xparam1,data_info,options_,M_,estim_params_,bayestopt_,oo_);
             options_.analytic_derivation = analytic_derivation;

matlab/print_table_prior.m

@@ -38,7 +38,7 @@ T2 = strvcat(T2, l2);
 
 prior_trunc_backup = DynareOptions.prior_trunc ;
 DynareOptions.prior_trunc = (1-DynareOptions.prior_interval)/2 ;
-PriorIntervals = prior_bounds(BayesInfo,DynareOptions) ;
+PriorIntervals = prior_bounds(BayesInfo, DynareOptions.prior_trunc) ;
 DynareOptions.prior_trunc = prior_trunc_backup ;
 
 RESIZE = false;

matlab/prior_bounds.m

-function bounds = prior_bounds(bayestopt,options)
+function bounds = prior_bounds(bayestopt, prior_trunc)
 
 %@info:
 %! @deftypefn {Function File} {@var{bounds} =} prior_bounds (@var{bayesopt},@var{option})
@@ -69,7 +69,6 @@ p3 = bayestopt.p3;
 p4 = bayestopt.p4;
 p6 = bayestopt.p6;
 p7 = bayestopt.p7;
-prior_trunc = options.prior_trunc;
 
 bounds.lb = zeros(length(p6),1);
 bounds.ub = zeros(length(p6),1);

matlab/prior_draw.m

@@ -51,7 +51,7 @@ if nargin>0 && init
     p7 = evalin('base', 'bayestopt_.p7');
     p3 = evalin('base', 'bayestopt_.p3');
     p4 = evalin('base', 'bayestopt_.p4');
-    bounds = evalin('base', 'prior_bounds(bayestopt_,options_)');
+    bounds = evalin('base', 'prior_bounds(bayestopt_, options_.prior_trunc)');
     lb = bounds.lb;
     ub = bounds.ub;
     number_of_estimated_parameters = length(p6);
@@ -173,6 +173,9 @@ if weibull_draws
     end
 end
 
+
+
+
 %@test:1
 %$ %% Initialize required structures
 %$ options_.prior_trunc=0;
@@ -190,17 +193,17 @@ end
 %$ 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(1,3)= -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);
+%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_)
+%$  
+%$ pdraw = prior_draw(1,0); pdraw
 %$ pdraw_vec=NaN(ndraws,1);
 %$ for ii=1:ndraws
 %$     pdraw_vec(ii)=prior_draw(0,0);
@@ -437,7 +440,7 @@ end
 %$ 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_)';
+%$ bounds = prior_bounds(bayestopt_,options_.prior_trunc)';
 %$ 
 %$ pdraw = prior_draw(1,0);
 %$ pdraw_vec=NaN(ndraws,1);
@@ -459,7 +462,7 @@ end
 %$ 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_)';
+%$ bounds = prior_bounds(bayestopt_,options_.prior_trunc)';
 %$ 
 %$ pdraw = prior_draw(1,0);
 %$ pdraw_vec=NaN(ndraws,1);
@@ -481,7 +484,7 @@ end
 %$ 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_)';
+%$ bounds = prior_bounds(bayestopt_,options_.prior_trunc)';
 %$ 
 %$ pdraw = prior_draw(1,0);
 %$ pdraw_vec=NaN(ndraws,1);
@@ -503,7 +506,7 @@ end
 %$ 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_)';
+%$ bounds = prior_bounds(bayestopt_,options_.prior_trunc)';
 %$ 
 %$ pdraw = prior_draw(1,0);
 %$ pdraw_vec=NaN(ndraws,1);
@@ -526,7 +529,7 @@ end
 %$ 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_)';
+%$ bounds = prior_bounds(bayestopt_,options_.prior_trunc)';
 %$ 
 %$ pdraw = prior_draw(1,0);
 %$ pdraw_vec=NaN(ndraws,1);