Bayesian Likelihoods for Moment Condition Models

Ragusa, Giuseppe (2007) Bayesian Likelihoods for Moment Condition Models. [Working Paper]. p. 37. Working Papers (No. 060714). (Submitted)

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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.

Item Type: Report / Paper (Working Paper)
Research documents and activity classification: Working Papers > Non-Refereed Working Papers / of national relevance only
Divisions: Department of Business and Management
Uncontrolled Keywords: Moment condition; GMM; GEL; Likelihood functions; Bayesian inference.
MIUR Scientific Area: Area 13 - Economics and Statistics > SECS-P/05 Econometrics
Deposited by: Maria Teresa Nisticò
Date Deposited: 16 Dec 2010 18:09
Last Modified: 22 Apr 2015 00:13


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