diff --git a/matlab/ols/sur.m b/matlab/ols/sur.m
index 7b34b0b95..cbec96ceb 100644
--- a/matlab/ols/sur.m
+++ b/matlab/ols/sur.m
@@ -121,13 +121,16 @@ if ~isempty(st) && strcmp(st(1).name, 'surgibbs')
 end
 
 % constrained_param_idxs: indexes in X.name of parameters that were constrained
-constrained_param_idxs = [];
+constrained_param_idxs = NaN(length(constrained), 1);
+j = 0;
 for i = 1:length(constrained)
     idx = find(strcmp(X.name, constrained{i}));
     if ~isempty(idx)
-        constrained_param_idxs(end+1, 1) = idx;
+        j = j+1;
+        constrained_param_idxs(j, 1) = idx;
     end
 end
+constrained_param_idxs = constrained_param_idxs(1:j);
 
 %% Estimation
 oo_.sur.(model_name).dof = nobs;
diff --git a/matlab/surgibbs.m b/matlab/surgibbs.m
index df853905d..0dee0f61d 100644
--- a/matlab/surgibbs.m
+++ b/matlab/surgibbs.m
@@ -88,9 +88,9 @@ end
 %  Using a Combination of Direct Monte Carlo and Importance Sampling
 %  Techniques. Bayesian Analysis. 2010. pp 67-70.
 if nargin == 8
-    [nobs, X, Y, m, lhssub, fp, ~] = sur(ds, param_names, eqtags);
+    [nobs, X, Y, m, lhssub, fp] = sur(ds, param_names, eqtags);
 else
-    [nobs, X, Y, m, lhssub, fp, ~] = sur(ds, param_names);
+    [nobs, X, Y, m, lhssub, fp] = sur(ds, param_names);
 end
 
 oo_.surgibbs.(model_name).dof = nobs;
@@ -176,7 +176,7 @@ write_param_init_inc_file('surgibbs', model_name, oo_.surgibbs.(model_name).para
 
 %% Print Output
 if ~options_.noprint
-    ttitle = ['Gibbs Sampling on SUR'];
+    ttitle = 'Gibbs Sampling on SUR';
     preamble = {['Model name: ' model_name], ...
         sprintf('No. Equations: %d', oo_.surgibbs.(model_name).neqs), ...
         sprintf('No. Independent Variables: %d', size(X, 2)), ...