Moment Condition Models Research Articles

Bayesian exponentially tilted empirical likelihood

SUMMARY While empirical likelihood has been shown to exhibit many of the properties of conven tional parametric likelihoods, a formal probabilistic interpretation has so far been lacking. We show that a likelihood function very closely related to empirical likelihood naturally arises from a nonparametric Bayesian procedure which places a type of noninformative prior on the space of distributions. This prior gives preference to distributions having a small support and, among those sharing the same support, it favours entropy-maximising distributions. The resulting nonparametric Bayesian procedure admits a computationally convenient representation as an empirical-likelihood-type likelihood where the probability weights are obtained via exponential tilting. The proposed methodology provides an attractive alternative to the Bayesian bootstrap as a nonparametric limit of a Bayesian procedure for moment condition models.

Generalized empirical likelihood non-nested tests

This paper examines non-nested tests for competing moment condition models using a semi-parametric generalized empirical likelihood (GEL) framework. The resultant GEL estimators are first order asymptotically equivalent to those based on generalized method of moments (GMM). Cox-type, moment encompassing and parametric encompassing non-nested tests for competing moment condition models are proposed. Simulation experiments are conducted to examine the efficacy of the proposed GEL statistics in terms of their size and power properties and to compare their properties with those of corresponding non-nested test statistics based on GMM estimation.

Information Theoretic Approaches to Inference in Moment Condition Models

Information Theoretic Approaches to Inference in Moment Condition Models