diff --git a/matlab/dynare_estimation.m b/matlab/dynare_estimation.m
index 348bdf8c4..e0f8907c1 100644
--- a/matlab/dynare_estimation.m
+++ b/matlab/dynare_estimation.m
@@ -91,7 +91,7 @@ else
     dynare_estimation_1(var_list,dname);
 end
 
-if options_.mode_compute && options_.analytic_derivation,
+if isnumeric(options_.mode_compute) && options_.mode_compute && options_.analytic_derivation,
     options_.analytic_derivation=analytic_derivation0;
 end
 
diff --git a/matlab/dynare_estimation_1.m b/matlab/dynare_estimation_1.m
index 5fdd91895..eba52ea28 100644
--- a/matlab/dynare_estimation_1.m
+++ b/matlab/dynare_estimation_1.m
@@ -261,14 +261,14 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
     [xparam1, fval, exitflag, hh, options_, Scale] = dynare_minimize_objective(objective_function,xparam1,options_.mode_compute,options_,[bounds.lb bounds.ub],bayestopt_.name,bayestopt_,hh,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
     fprintf('\nFinal value of minus the log posterior (or likelihood):%f \n', fval);
 
-    if options_.mode_compute==5 && options_.analytic_derivation==-1 %reset options changed by newrat
+    if isnumeric(options_.mode_compute) && options_.mode_compute==5 && options_.analytic_derivation==-1 %reset options changed by newrat
             options_.analytic_derivation = options_analytic_derivation_old; %reset      
-    elseif options_.mode_compute==6 %save scaling factor
+    elseif isnumeric(options_.mode_compute) && options_.mode_compute==6 %save scaling factor
         save([M_.fname '_optimal_mh_scale_parameter.mat'],'Scale');
         options_.mh_jscale = Scale;
         bayestopt_.jscale = ones(length(xparam1),1)*Scale;
     end       
-    if ~isequal(options_.mode_compute,6) %always already computes covariance matrix
+    if ~isnumeric(options_.mode_compute) || ~isequal(options_.mode_compute,6) %always already computes covariance matrix
         if options_.cova_compute == 1 %user did not request covariance not to be computed
             if options_.analytic_derivation && strcmp(func2str(objective_function),'dsge_likelihood'),
                 ana_deriv_old = options_.analytic_derivation;
@@ -276,12 +276,12 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
                 [junk1, junk2, hh] = feval(objective_function,xparam1, ...
                     dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
                 options_.analytic_derivation = ana_deriv_old;
-            elseif ~(isequal(options_.mode_compute,5) && newratflag~=1), 
+            elseif ~isnumeric(options_.mode_compute) || ~(isequal(options_.mode_compute,5) && newratflag~=1), 
                 % with flag==0, we force to use the hessian from outer
                 % product gradient of optimizer 5
                 hh = reshape(hessian(objective_function,xparam1, ...
                     options_.gstep,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_),nx,nx);
-            elseif isequal(options_.mode_compute,5)
+            elseif isnumeric(options_.mode_compute) && isequal(options_.mode_compute,5)
                 % other numerical hessian options available with optimizer 5
                 %
                 % if newratflag == 0
diff --git a/matlab/optimization/dynare_minimize_objective.m b/matlab/optimization/dynare_minimize_objective.m
index 77b1449dc..27456dbac 100644
--- a/matlab/optimization/dynare_minimize_objective.m
+++ b/matlab/optimization/dynare_minimize_objective.m
@@ -46,14 +46,14 @@ function [opt_par_values,fval,exitflag,hessian_mat,options_,Scale]=dynare_minimi
 %% set bounds and parameter names if not already set
 n_params=size(start_par_value,1);
 if isempty(bounds)
-    if minimizer_algorithm==10
+    if isnumeric(minimizer_algorithm) && minimizer_algorithm==10
         error('Algorithm 10 (simpsa) requires upper and lower bounds')
     else
         bounds=[-Inf(n_params,1) Inf(n_params,1)];
     end
 end
 
-if minimizer_algorithm==10 && any(any(isinf(bounds)))
+if isnumeric(minimizer_algorithm) && minimizer_algorithm==10 && any(any(isinf(bounds)))
     error('Algorithm 10 (simpsa) requires finite upper and lower bounds')
 end