diff --git a/matlab/optimization/dynare_minimize_objective.m b/matlab/optimization/dynare_minimize_objective.m
index d8ffbb4b1..a7e6a83f5 100644
--- a/matlab/optimization/dynare_minimize_objective.m
+++ b/matlab/optimization/dynare_minimize_objective.m
@@ -51,7 +51,7 @@ if isempty(bounds)
 end
 
 if isempty(parameter_names)
-    parameter_names=[repmat('parameter ',n_params,1),num2str((1:n_params)')];
+    parameter_names=cellstr([repmat('parameter ',n_params,1),num2str((1:n_params)')]);
 end
 
 %% initialize function outputs
@@ -268,7 +268,7 @@ switch minimizer_algorithm
     hess_info.gstep=options_.gstep;
     hess_info.htol = 1.e-4;
     hess_info.h1=options_.gradient_epsilon*ones(n_params,1);
-    [opt_par_values,hessian_mat,gg,fval,invhess,new_rat_hess_info] = newrat(objective_function,start_par_value,bounds,analytic_grad,crit,nit,0,Verbose, Save_files,hess_info,prior_information.p2,varargin{:});    %hessian_mat is the plain outer product gradient Hessian
+    [opt_par_values,hessian_mat,gg,fval,invhess,new_rat_hess_info] = newrat(objective_function,start_par_value,bounds,analytic_grad,crit,nit,0,Verbose,Save_files,hess_info,prior_information.p2,options_.gradient_epsilon,parameter_names,varargin{:});    %hessian_mat is the plain outer product gradient Hessian
   case 6
     if isempty(prior_information) %Inf will be reset
         prior_information.p2=Inf(n_params,1);
diff --git a/matlab/optimization/mr_gstep.m b/matlab/optimization/mr_gstep.m
index 0836a7a87..048601484 100644
--- a/matlab/optimization/mr_gstep.m
+++ b/matlab/optimization/mr_gstep.m
@@ -1,5 +1,5 @@
-function [f0, x, ig] = mr_gstep(h1,x,bounds,func0,penalty,htol0,Verbose,Save_files,varargin)
-% [f0, x, ig] = mr_gstep(h1,x,bounds,func0,penalty,htol0,Verbose,Save_files,varargin)
+function [f0, x, ig] = mr_gstep(h1,x,bounds,func0,penalty,htol0,Verbose,Save_files,gradient_epsilon,parameter_names,varargin)
+% [f0, x, ig] = mr_gstep(h1,x,bounds,func0,penalty,htol0,Verbose,Save_files,gradient_epsilon,parameter_names,varargin)
 %
 % Gibbs type step in optimisation
 %
@@ -11,7 +11,7 @@ function [f0, x, ig] = mr_gstep(h1,x,bounds,func0,penalty,htol0,Verbose,Save_fil
 % varargin{6} --> BayesInfo
 % varargin{1} --> DynareResults
 
-% Copyright (C) 2006-2017 Dynare Team
+% Copyright (C) 2006-2020 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -30,7 +30,7 @@ function [f0, x, ig] = mr_gstep(h1,x,bounds,func0,penalty,htol0,Verbose,Save_fil
 
 n=size(x,1);
 if isempty(h1)
-    h1=varargin{3}.gradient_epsilon*ones(n,1);
+    h1=gradient_epsilon*ones(n,1);
 end
 
 
@@ -72,10 +72,10 @@ while i<n
         gg(i)=(f1(i)'-f_1(i)')./(2.*h1(i));
         hh(i) = 1/max(1.e-9,abs( (f1(i)+f_1(i)-2*f0)./(h1(i)*h1(i)) ));
         if gg(i)*(hh(i)*gg(i))/2 > htol(i)
-            [f0, x, fc, retcode] = csminit1(func0,x,penalty,f0,gg,0,diag(hh),Verbose,varargin{:});
+            [f0, x, ~, ~] = csminit1(func0,x,penalty,f0,gg,0,diag(hh),Verbose,varargin{:});
             ig(i)=1;
             if Verbose
-                fprintf(['Done for param %s = %8.4f\n'],varargin{6}.name{i},x(i))
+                fprintf(['Done for param %s = %8.4f\n'],parameter_names{i},x(i))
             end
         end
         xh1=x;
diff --git a/matlab/optimization/newrat.m b/matlab/optimization/newrat.m
index e1ec4c1ed..e94d56554 100644
--- a/matlab/optimization/newrat.m
+++ b/matlab/optimization/newrat.m
@@ -1,5 +1,5 @@
-function [xparam1, hh, gg, fval, igg, hess_info] = newrat(func0, x, bounds, analytic_derivation, ftol0, nit, flagg, Verbose, Save_files, hess_info, prior_std, varargin)
-%  [xparam1, hh, gg, fval, igg, hess_info] = newrat(func0, x, bounds, analytic_derivation, ftol0, nit, flagg, Verbose, Save_files, hess_info, varargin)
+function [xparam1, hh, gg, fval, igg, hess_info] = newrat(func0, x, bounds, analytic_derivation, ftol0, nit, flagg, Verbose, Save_files, hess_info, prior_std, gradient_epsilon, parameter_names, varargin)
+%  [xparam1, hh, gg, fval, igg, hess_info] = newrat(func0, x, bounds, analytic_derivation, ftol0, nit, flagg, Verbose, Save_files, hess_info, gradient_epsilon, parameter_names, varargin)
 %
 %  Optimiser with outer product gradient and with sequences of univariate steps
 %  uses Chris Sims subroutine for line search
@@ -24,6 +24,8 @@ function [xparam1, hh, gg, fval, igg, hess_info] = newrat(func0, x, bounds, anal
 %                           computation of Hessian
 %  - prior_std              prior standard devation of parameters (can be NaN); 
 %                           passed to mr_hessian
+%  - gradient_epsilon       [double] step size in gradient
+%  - parameter_names        [cell] names of parameters for error messages
 %  - varargin               other inputs
 %                           e.g. in dsge_likelihood and others:
 %                           varargin{1} --> DynareDataset
@@ -167,7 +169,7 @@ while norm(gg)>gtol && check==0 && jit<nit
         [fvala,x0,fc,retcode] = csminit1(func0,x0,penalty,fval,ggx,0,iggx,Verbose,varargin{:});
     end
     x0 = check_bounds(x0,bounds);
-    [fvala, x0, ig] = mr_gstep(h1,x0,bounds,func0,penalty,htol0,Verbose,Save_files,varargin{:});
+    [fvala, x0, ig] = mr_gstep(h1,x0,bounds,func0,penalty,htol0,Verbose,Save_files,gradient_epsilon, parameter_names,varargin{:});
     x0 = check_bounds(x0,bounds);
     nig=[nig ig];
     disp_verbose('Sequence of univariate steps!!',Verbose)