Single-index model selections

We derive a new model selection criterion for single-index models, AICC, by minimizing the expected Kullback-Leibler distance between the true and candidate models. The proposed criterion selects not only relevant variables but also the smoothing parameter for an unknown link function. Thus, it is a general selection criterion that provides a uniÞed approach to model selection across both parametric and nonparametric functions. Monte Carlo studies demonstrate that AICC performs satisfactorily in most situations. We illustrate the practical use of AICC with an empirical example for modeling the hedonic price function for automobiles. In addition, we extend the applicability of AICC to partially linear and additive single-index models.