TY  - JOUR
AU  - Chamberlain,Gary
AU  - Imbens,Guido W.
TI  - Nonparametric Applications of Bayesian Inference
JF  - National Bureau of Economic Research Technical Working Paper Series
VL  - No. 200
PY  - 1996
Y2  - August 1996
UR  - http://www.nber.org/papers/t0200
L1  - http://www.nber.org/papers/t0200.pdf
N1  - Author contact info:
Gary Chamberlain
Department of Economics
Littauer Center 123
Harvard University
Cambridge, MA  02138
Tel: 617/495-1869
Fax: 617/495-8570
E-Mail: gary_chamberlain@harvard.edu

Guido Imbens
Department of Economics
Littauer Center
Harvard University
1805 Cambridge Street
Cambridge, MA 02138
Tel: 617/384-7485
Fax: 617/495-7730
E-Mail: imbens@fas.harvard.edu
AB  - 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.
ER  -