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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Official URL: http://www.economics.uci.edu/docs/2006-07/Ragusa-1...
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 Nistico|
|Deposited On:||16 Dec 2010 19:09|
|Last Modified:||16 Dec 2010 19:09|
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