Nonparametric Applications of Bayesian Inference
AbstractThe 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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Bibliographic InfoPaper provided by Harvard University Department of Economics in its series Scholarly Articles with number 3221493.
Date of creation: 1996
Date of revision:
Publication status: Published in NBER Technical Working Paper
Other versions of this item:
- Gary Chamberlain & Guido W. Imbens, 1996. "Nonparametric Applications of Bayesian Inference," NBER Technical Working Papers 0200, National Bureau of Economic Research, Inc.
- Gary Chamberlain & Guido W. Imbens, 1996. "Nonparametric Applications of Bayesian Inference," Harvard Institute of Economic Research Working Papers 1772, Harvard - Institute of Economic Research.
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