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Bayesian Likelihoods for Moment Condition Models

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  • Giuseppe Ragusa

    ()
    (Department of Economics, University of California-Irvine)

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.

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File URL: http://www.economics.uci.edu/files/economics/docs/workingpapers/2006-07/Ragusa-14.pdf
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Bibliographic Info

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

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Keywords: Moment condition; GMM; GEL; Likelihood functions; Bayesian inference;

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References

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  1. Chamberlain, Gary & Imbens, Guido W, 2003. "Nonparametric Applications of Bayesian Inference," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 12-18, January.
  2. 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.
  3. Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August.
  4. 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.
  5. Kim, Jae-Young, 2002. "Limited information likelihood and Bayesian analysis," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 175-193, March.
  6. Whitney K. Newey & Richard J. Smith, 2004. "Higher Order Properties of Gmm and Generalized Empirical Likelihood Estimators," Econometrica, Econometric Society, vol. 72(1), pages 219-255, 01.
  7. 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.
  8. 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.
  9. Tony Lancaster & Sung Jae Jun, 2006. "Bayesian quantile regression," CeMMAP working papers CWP05/06, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  10. Susanne M. Schennach, 2005. "Bayesian exponentially tilted empirical likelihood," Biometrika, Biometrika Trust, vol. 92(1), pages 31-46, March.
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Citations

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Cited by:
  1. Jesús Fernández-Villaverde, 2010. "The econometrics of DSGE models," SERIEs, Spanish Economic Association, vol. 1(1), pages 3-49, March.
  2. Huigang Chen & Alin Mirestean & Charalambos G. Tsangarides, 2011. "Limited Information Bayesian Model Averaging for Dynamic Panels with An Application to a Trade Gravity Model," IMF Working Papers 11/230, International Monetary Fund.
  3. Dale Poirier, 2008. "Bayesian Interpretations of Heteroskedastic Consistent Covariance Estimators Using the Informed Bayesian Bootstrap," Working Papers 080905, University of California-Irvine, Department of Economics.
  4. Alin Mirestean & Charalambos G. Tsangarides & Huigang Chen, 2009. "Limited Information Bayesian Model Averaging for Dynamic Panels with Short Time Periods," IMF Working Papers 09/74, International Monetary Fund.

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