Nonparametric Applications of Bayesian Inference

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Nonparametric Applications of Bayesian Inference

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Title: Nonparametric Applications of Bayesian Inference
Author: Imbens, Guido; Chamberlain, Gary

Note: Order does not necessarily reflect citation order of authors.

Citation: Chamberlain, Gary, and Guido W. Imbens. 1996. Nonparametric applications of Bayesian inference. NBER Technical Working Paper 200.
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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.
Published Version: http://www.nber.org/papers/t0200
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:3221493

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  • FAS Scholarly Articles [7219]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University
 
 

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