English, Article, Journal or magazine article edition: Bayesian Likelihoods for Moment Condition Models Giuseppe Ragusa

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Bookmark: http://trove.nla.gov.au/version/33974
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Bayesian Likelihoods for Moment Condition Models
Author
  • Giuseppe Ragusa
Physical Description
  • preprint
Notes
  • 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.
  • Moment condition; GMM; GEL; Likelihood functions; Bayesian inference
  • RePEc:irv:wpaper:060714
Language
  • English
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