The Akaike Likelihood Ratio Index

This work argues that the Akaike measure is the appropriate criterion for model selection and defines an alternative index based on the A Kaike Information Criterion, which favors more parsimonious models.

Abstract

In 1983, Horowitz proposed the use of an adjusted likelihood ratio index to select between alternative models. We define an alternative index based on the Akaike Information Criterion. The two indices incorporate different degrees-of-freedom corrections to the same measure of goodness-of-fit. The Akaike index favors more parsimonious models. We argue that the Akaike measure is the appropriate criterion for model selection. We utilize Horowitz's results on non-nested hypothesis testing for the new index.