  We find that, in a linear model, the James-Stein estimator, which dominates
the maximum-likelihood estimator in terms of its in-sample prediction error,
can perform poorly compared to the maximum-likelihood estimator in
out-of-sample prediction. We give a detailed analysis of this phenomenon and
discuss its implications. When evaluating the predictive performance of
estimators, we treat the regressor matrix in the training data as fixed, i.e.,
we condition on the design variables. Our findings contrast those obtained by
Baranchik (1973, Ann. Stat. 1:312-321) and, more recently, by Dicker (2012,
arXiv:1102.2952) in an unconditional performance evaluation.
