Merge remote-tracking branch 'jpfeifer/master'

matlab/prior_bounds.m

@@ -4,7 +4,7 @@ function bounds = prior_bounds(bayestopt,options)
 %! @deftypefn {Function File} {@var{bounds} =} prior_bounds (@var{bayesopt},@var{option})
 %! @anchor{prior_bounds}
 %! @sp 1
-%! Returns bounds for the prior densities. For each estimated parameter the upper and lower bounds
+%! Returns bounds for the prior densities. For each estimated parameter the lower and upper bounds
 %! are such that the defined intervals contains a probability mass equal to 1-2*@var{option}.prior_trunc. The
 %! default value for @var{option}.prior_trunc is 1e-10 (set in @ref{global_initialization}).
 %! @sp 2
@@ -43,7 +43,7 @@ function bounds = prior_bounds(bayestopt,options)
 %    bayestopt  [structure]  characterizing priors (shape, mean, p1..p4)
 %    
 % OUTPUTS
-%    bounds     [double]      matrix specifying prior bounds (row= parameter, column=upper&lower bound)
+%    bounds     [double]      matrix specifying prior bounds (row= parameter, column=lower&upper bound)
 %    
 % SPECIAL REQUIREMENTS
 %    none

matlab/random_walk_metropolis_hastings_core.m

@@ -118,7 +118,7 @@ end
 %%%%
 
 
-if any(isnan(bayestopt_.jscale))
+if any(isnan(bayestopt_.jscale))  || (options_.mode_compute==0 && ~isempty(options_.mode_file))
     if exist([ModelName '_optimal_mh_scale_parameter.mat'])% This file is created by mode_compute=6.
         load([ModelName '_optimal_mh_scale_parameter'])
         proposal_covariance_Cholesky_decomposition = d*Scale;

matlab/set_prior.m

@@ -106,9 +106,9 @@ if ncx
     bayestopt_.p3 = [ bayestopt_.p3; estim_params_.corrx(:,9)];
     bayestopt_.p4 = [ bayestopt_.p4; estim_params_.corrx(:,10)];
     bayestopt_.jscale = [ bayestopt_.jscale; estim_params_.corrx(:,11)];
-    bayestopt_.name = [bayestopt_.name; cellstr(['corr ' ...
+    bayestopt_.name = [bayestopt_.name; cellstr([repmat('corr ',ncx,1)...
                         deblank(M_.exo_names(estim_params_.corrx(:,1),:)) ...
-                        ', ' , deblank(M_.exo_names(estim_params_.corrx(:,2),:))])];
+                        repmat(', ',ncx,1) , deblank(M_.exo_names(estim_params_.corrx(:,2),:))])];
 end
 if ncn
     if isequal(M_.H,0)
@@ -124,9 +124,9 @@ if ncn
     bayestopt_.p3 = [ bayestopt_.p3; estim_params_.corrn(:,9)];
     bayestopt_.p4 = [ bayestopt_.p4; estim_params_.corrn(:,10)];
     bayestopt_.jscale = [ bayestopt_.jscale; estim_params_.corrn(:,11)];
-    bayestopt_.name = [bayestiopt_.name; cellstr(['corr ' ...
+    bayestopt_.name = [bayestopt_.name; cellstr([repmat('corr ',ncn,1) ...
                         deblank(M_.exo_names(estim_params_.corrn(:,1),:)) ...
-                        ', ' , deblank(M_.exo_names(estim_params_.corrn(:,2),:))])];
+                        repmat(', ',ncn,1) , deblank(M_.exo_names(estim_params_.corrn(:,2),:))])];
 end
 if np
     xparam1 = [xparam1; estim_params_.param_vals(:,2)];