Nonnested Model Selection Criteria
نویسنده
چکیده
This paper studies model selection for a general class of models based on minimizing random distance functions. The proposed model selection criteria are consistent, regardless of whether models are nested or nonnested and regardless of whether models are correctly specified or not, in the sense that they select the best model with the least number of parameters with probability converging to 1. As byproduct, in the case of non-nonested models, it is shown that while traditional Bayesian methods for model selection based on Bayes factors choose models with the best fit objective functions, they do not consistently select the most parsimonious model among the best fitting models. In addition, we study the relation between Bayesian and frequentist prediction. JEL Classification: C14; C52
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