Empiricial Comparison between Some Model Selection Criteria

Abstract

Model selection aims to find the best model. Most of the usual criteria are based on goodness of fit and parsimony and aim to maximize a transformed version of likelihood. The situation is less clear when two models are equivalent: are they close to the unknown true model or are they far from it? Based on simulations, we study the results of Vuong's test, Cox's test, AIC and BIC and the ability of these four tests to discriminate between models.

Keywords:

• Akaike information criterion
• Bayesian information criterion
• Cox's test
• Hypothesis testing
• Kullback–Leibler
• Model selection
• Mis-specified models
• Non nested models
• Vuong's test

Mathematics Subject Classification:

• 62Gxx
• 62E20