The model selection criterion AICu

For regression and time series model selection, Hurvich and Tsai (1989) obtained a bias correction Akaike information criterion, AICc, which provides better model order choices than the Akaike information criterion, AIC (Akaike, 1973). In this paper, we propose an alternative improved regression model selection criterion, AICu, which is an approximate unbiased estimator of Kullback-Leibler information. We show that AICu is neither a consistent (Shibata, 1986) nor an efficient (Shibata, 1980, 1981) criterion. Our simulation studies indicate that the behavior of AICu is a compromise between that of efficient (AICc) and consistent (BIG, Akaike,1978) criteria. Specifically, AICu performs better than AICc for moderate to large sample sizes except when the true model is of infinite order. In addition, it outperforms BIC except when a true model exists and the sample size is large.