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Bayesian Likelihoods for Moment Condition Models

  • Giuseppe Ragusa

    ()

    (Department of Economics, University of California-Irvine)

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.

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File URL: http://www.economics.uci.edu/files/docs/workingpapers/2006-07/Ragusa-14.pdf
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Paper provided by University of California-Irvine, Department of Economics in its series Working Papers with number 060714.

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Length: 37 pages
Date of creation: Jan 2007
Date of revision:
Handle: RePEc:irv:wpaper:060714
Contact details of provider: Postal: Irvine, CA 92697-3125
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Web page: http://www.economics.uci.edu/
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  1. Ghysels, Eric & Hall, Alastair, 2002. "Interview with Christopher A. Sims," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 448-49, October.
  2. Imbens, Guido W, 1997. "One-Step Estimators for Over-Identified Generalized Method of Moments Models," Review of Economic Studies, Wiley Blackwell, vol. 64(3), pages 359-83, July.
  3. Tony Lancaster & Sung Jae Jun, 2006. "Baysian Quantile Regression," Working Papers 2006-05, Brown University, Department of Economics.
  4. Whitney Newey & Richard Smith, 2003. "Higher order properties of GMM and generalised empirical likelihood estimators," CeMMAP working papers CWP04/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  5. Gary Chamberlain & Guido W. Imbens, 1996. "Nonparametric Applications of Bayesian Inference," Harvard Institute of Economic Research Working Papers 1772, Harvard - Institute of Economic Research.
  6. Hahn, Jinyong, 1997. "Bayesian Bootstrap of the Quantile Regression Estimator: A Large Sample Study," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 38(4), pages 795-808, November.
  7. Brown, Bryan W & Newey, Whitney K, 2002. "Generalized Method of Moments, Efficient Bootstrapping, and Improved Inference," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 507-17, October.
  8. Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August.
  9. Susanne M. Schennach, 2005. "Bayesian exponentially tilted empirical likelihood," Biometrika, Biometrika Trust, vol. 92(1), pages 31-46, March.
  10. Kim, Jae-Young, 2002. "Limited information likelihood and Bayesian analysis," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 175-193, March.
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