A Bayesian Extension of the J-Test for Non-Nested Hypotheses
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.
Volume (Year): 9 (2011)
Issue (Month): 1 ()
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