An iterative approach to variable selection based on the Kullback-Leibler information
Model selection criteria based on estimates of the Kullback-Leibler informal ion have become popular among statisticians and econometricians. This group of criteria includes Akaike's (1973) AIC and Sugiura's (1978) bias corrected AIC (AICo). It has been suggested that reduction of the bias in estimating the Kullback-Leibler information can lead to better model selection. In this paper we propose a method of reducing the bias of AICC by relaxing one of the key assumptions made in its derivation. Monte Carlo evidence presented suggests that this new procedure outperforms AICc under certain circumstances even in very small samples. Despite this, the results indicate that AICc's model selection performance is highly robust to the assumption that is made.