  Iterative imputation is a popular tool to accommodate missing data. While it
is widely accepted that valid inferences can be obtained with this technique,
these inferences all rely on algorithmic convergence. There is no consensus on
how to evaluate the convergence properties of the method. Our study provides
insight into identifying non-convergence in iterative imputation algorithms. We
found that--in the cases considered--inferential validity was achieved after
five to ten iterations, much earlier than indicated by diagnostic methods. We
conclude that it never hurts to iterate longer, but such calculations hardly
bring added value.
