A Bayesian Extension of the J-Test for Non-Nested Hypotheses

Moheb Ghali, John M. Krieg and Surekha Rao ()

Journal of Quantitative Economics, 2011, vol. 9, issue 1, 53-72

Abstract: It is a common practice to use the Davidson and MacKinnon's J-test in empirical applications to test non-nested model specifications. However, when the alternate specifications fit the data well the J- test may fail to distinguish between the true and false models: the J-test will either reject, or fail to reject both specifications. We show that it is possible to use the information generated by the J-test and combine it with the Bayesian posterior odds approach that would yield an unequivocal and acceptable solution for non-nested hypotheses. We show that the approximations of Schwarz and Bayesian Information Criterion based on classical estimates for the J- test yield the Bayesian posterior odds without any need for the specification of the prior distributions and the onerous Bayesian computations.

Date: 2011
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