Nonparametric Applications of Bayesian Inference / Gary Chamberlain, Guido W. Imbens.

Format:

Book

Author/Creator:

Chamberlain, Gary.

Contributor:

National Bureau of Economic Research.

Imbens, Guido W.

Series:

Technical Working Paper Series (National Bureau of Economic Research) no. t0200.

NBER technical working paper series no. t0200

Language:

English

Subjects (All):

Bayesian statistical decision theory.

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 1996.

Cambridge, Mass : National Bureau of Economic Research, 1996.

Summary:

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.

Notes:

Print version record

August 1996.