Selection versus averaging of logistic credit risk models

We evaluate the relative performance of logistic credit risk models that were selected by means of standard stepwise model selection methods and "average" models obtained by Bayesian model averaging (BMA). Our bootstrap analysis shows that BMA should be considered as an alternative to the stepwise model selection procedures that are currently often used in practice.