Nonparametric Applications of Bayesian Inference
Gary Chamberlain and
Guido Imbens
Journal of Business & Economic Statistics, 2003, vol. 21, issue 1, 12-18
Abstract:
This article evaluates the usefulness of a nonparametric approach to Bayesian inference by presenting two applications. 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 relying on asymptotic approximations. Unlike in the first application, the standard asymptotic normal approximation turns out not to be a good guide.
Date: 2003
References: Add references at CitEc
Citations: View citations in EconPapers (77)
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
Working Paper: Nonparametric Applications of Bayesian Inference (1996)
Working Paper: Nonparametric Applications of Bayesian Inference (1996) 
Working Paper: Nonparametric Applications of Bayesian Inference (1996) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:bes:jnlbes:v:21:y:2003:i:1:p:12-18
Ordering information: This journal article can be ordered from
http://www.amstat.org/publications/index.html
Access Statistics for this article
Journal of Business & Economic Statistics is currently edited by Jonathan H. Wright and Keisuke Hirano
More articles in Journal of Business & Economic Statistics from American Statistical Association
Bibliographic data for series maintained by Christopher F. Baum ().