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Published September 15, 2017 | Submitted
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Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses

Abstract

In this paper, we propose a classical approach to model selection. Using the Kullback-Leibler Information measure, we propose simple and directional likelihood-ratio tests for discriminating and choosing between two competing models whether the models are nonnested, overlapping or nested and whether both, one, or neither is misspecified. As a prerequisite, we fully characterize the asymptotic distribution of the likelihood ratio statistic under the most general conditions.

Additional Information

This research was supported by National Science Foundation Grant SES-8410593. I am indebted to P. Bjorn, D. Lien, and D. Rivers for helpful discussions, and to J. M. Dufour for some references on weighted sums of chi-square distributions. I would like to thank especially H. White whose comments much improved this paper. I am also grateful to C. R. Jackson without whom this paper would not have been written and to L. Donnelly for stimulating thoughts. Remaining errors are mine. Published as Vuong, Quang H. "Likelihood ratio tests for model selection and non-nested hypotheses." Econometrica: Journal of the Econometric Society (1989): 307-333.

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