Classical Inference with ML and GMM Estimates with Various Rates of Convergence
نویسنده
چکیده
This paper considers classical hypothesis testing in the maximum likelihood (ML) and generalized method of moments (GMM) frameworks, where components of unconstrained (and constrained) estimates of a model may have various rates of convergence and their limiting distributions are asymptotically normally distributed. Sufficient conditions are established under which the likelihood ratio, efficient score, C(α), and Wald-type statistics for the testing of general equality constraints can be asymptotically χ and are asymptotically equivalent under both null and a sequence of local alternatives. Similarly, results for the analogous difference test, gradient test, C(α)-type gradient test, and Wald test in the GMM estimation framework are established.
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