diff --git a/matlab/utilities/dataset/makedataset.m b/matlab/utilities/dataset/makedataset.m
index c5c244b54..a26a074c5 100644
--- a/matlab/utilities/dataset/makedataset.m
+++ b/matlab/utilities/dataset/makedataset.m
@@ -267,7 +267,7 @@ else
 end
 
 % Compute the empirical mean of the observed variables.
-DatasetInfo.descriptive.mean = nanmean(DynareDataset.data);
+DatasetInfo.descriptive.mean = nanmean(DynareDataset.data,1);
 
 % Compute the empirical covariance matrix of the observed variables.
 DatasetInfo.descriptive.covariance = nancovariance(DynareDataset.data);
diff --git a/matlab/utilities/dataset/nancovariance.m b/matlab/utilities/dataset/nancovariance.m
index 8e2e2b227..09f2a38af 100644
--- a/matlab/utilities/dataset/nancovariance.m
+++ b/matlab/utilities/dataset/nancovariance.m
@@ -51,7 +51,7 @@ function CovarianceMatrix = nancovariance(data)
 CovarianceMatrix = zeros(size(data,2));
 
 if isanynan(data)
-    data = bsxfun(@minus,data,nanmean(data));
+    data = bsxfun(@minus,data,nanmean(data,1));
     for i=1:size(data,2)
         for j=i:size(data,2)
             CovarianceMatrix(i,j) = nanmean(data(:,i).*data(:,j));
@@ -61,7 +61,7 @@ if isanynan(data)
         end
     end
 else
-    data = bsxfun(@minus,data,mean(data));
+    data = bsxfun(@minus,data,mean(data,1));
     CovarianceMatrix = (transpose(data)*data)/size(data,1);
 end