Bayesian Likelihoods for Moment Condition Models

Giuseppe Ragusa ()

No 60714, Working Papers from University of California-Irvine, Department of Economics

Abstract: Bayesian inference in moment condition models is difficult to implement. For these models, a posterior distribution cannot be calculated because the likelihood function has not been fully specified. In this paper, we obtain a class of likelihoods by formal Bayesian calculations that take into account the semiparametric nature of the problem. The likelihoods are derived by integrating out the nuisance parameters with respect to a maximum entropy tilted prior on the space of distribution. The result is a unification that uncovers a mapping between priors and likelihood functions. We show that there exist priors such that the likelihoods are closely connected to Generalized Empirical Likelihood (GEL) methods.

Keywords: Moment condition; GMM; GEL; Likelihood functions; Bayesian inference (search for similar items in EconPapers)
JEL-codes: C1 C11 C14 C21 (search for similar items in EconPapers)
Pages: 37 pages
Date: 2007-01
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:irv:wpaper:060714

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