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http://ssrn.com/abstract=226611
 
 

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Nonparametric Applications of Bayesian Inference


Gary Chamberlain


Harvard University - Department of Economics; National Bureau of Economic Research (NBER)

Guido W. Imbens


Stanford Graduate School of Business

August 1996

NBER Working Paper No. t0200

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.

Number of Pages in PDF File: 24

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Date posted: July 20, 2000  

Suggested Citation

Chamberlain, Gary and Imbens, Guido W., Nonparametric Applications of Bayesian Inference (August 1996). NBER Working Paper No. t0200. Available at SSRN: http://ssrn.com/abstract=226611

Contact Information

Gary Chamberlain (Contact Author)
Harvard University - Department of Economics ( email )
Littauer Center
Room 123
Cambridge, MA 02138
United States
617-495-1869 (Phone)
National Bureau of Economic Research (NBER)
1050 Massachusetts Avenue
Cambridge, MA 02138
United States
Guido W. Imbens
Stanford Graduate School of Business ( email )
518 Memorial Way
Stanford, CA 94305-5015
United States

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