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Nonparametric Applications of Bayesian Inference

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Author Info
Gary Chamberlain
Guido W. Imbens

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Abstract

The paper evaluates the usefulness of a nonparametric approach to Bayesian inference by presenting two applications. The approach is due to Ferguson (1973, 1974) and Rubin (1981). Our first application considers an educational choice problem. We focus on obtaining a predictive distribution for earnings corresponding to various levels of schooling. This predictive distribution incorporates the parameter uncertainty, so that it is relevant for decision making under uncertainty in the expected utility framework of microeconomics. The second application is to quantile regression. Our point here is to examine the potential of the nonparametric framework to provide inferences without making asymptotic approximations. Unlike in the first application, the standard asymptotic normal approximation turns out to not be a good guide. We also consider a comparison with a bootstrap approach.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Technical Working Papers with number 0200.

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Date of creation: Aug 1996
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Handle: RePEc:nbr:nberte:0200

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  1. repec:cup:etheor:v:11:y:1995:i:1:p:105-21 is not listed on IDEAS
  2. Koenker, Roger & Bassett, Gilbert, Jr, 1982. "Robust Tests for Heteroscedasticity Based on Regression Quantiles," Econometrica, Econometric Society, vol. 50(1), pages 43-61, January. [Downloadable!] (restricted)
  3. Angrist, Joshua D & Krueger, Alan B, 1991. "Does Compulsory School Attendance Affect Schooling and Earnings?," The Quarterly Journal of Economics, MIT Press, vol. 106(4), pages 979-1014, November. [Downloadable!] (restricted)
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  4. Meyer, Bruce D & Viscusi, W Kip & Durbin, David L, 1995. "Workers' Compensation and Injury Duration: Evidence from a Natural Experiment," American Economic Review, American Economic Association, vol. 85(3), pages 322-40, June. [Downloadable!] (restricted)
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  5. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-54, July. [Downloadable!] (restricted)
  6. Sims, Christopher A & Uhlig, Harald, 1991. "Understanding Unit Rooters: A Helicopter Tour," Econometrica, Econometric Society, vol. 59(6), pages 1591-99, November. [Downloadable!] (restricted)
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  7. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January. [Downloadable!] (restricted)
  8. Florens, J.P. & Rolin, J.M., 1994. "Bayes, Bootsrap, Moments," Papers 94.336, Toulouse - GREMAQ.
  9. Hahn, Jinyong, 1995. "Bootstrapping Quantile Regression Estimators," Econometric Theory, Cambridge University Press, vol. 11(01), pages 105-121, February. [Downloadable!]
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  1. 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. [Downloadable!]
  2. Giuseppe Ragusa, 2007. "Bayesian Likelihoods for Moment Condition Models," Working Papers 060714, University of California-Irvine, Department of Economics. [Downloadable!]
  3. Fernández-Villaverde, Jesús, 2009. "The Econometrics of DSGE Models," CEPR Discussion Papers 7157, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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  4. Tony Lancaster & Sung Jae Jun, 2006. "Baysian Quantile Regression," Working Papers 2006-05, Brown University, Department of Economics. [Downloadable!]
    Other versions:
  5. Paserman, M. Daniele, 2004. "Bayesian Inference for Duration Data with Unobserved and Unknown Heterogeneity: Monte Carlo Evidence and an Application," IZA Discussion Papers 996, Institute for the Study of Labor (IZA). [Downloadable!]
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