Nonparametric Applications of Bayesian Inference /
Chamberlain, Gary.
Nonparametric Applications of Bayesian Inference / Gary Chamberlain, Guido W. Imbens. - Cambridge, Mass. National Bureau of Economic Research 1996. - 1 online resource: illustrations (black and white); - NBER technical working paper series no. t0200 . - Technical Working Paper Series (National Bureau of Economic Research) no. t0200. .
August 1996.
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.
System requirements: Adobe [Acrobat] Reader required for PDF files.
Mode of access: World Wide Web.
Nonparametric Applications of Bayesian Inference / Gary Chamberlain, Guido W. Imbens. - Cambridge, Mass. National Bureau of Economic Research 1996. - 1 online resource: illustrations (black and white); - NBER technical working paper series no. t0200 . - Technical Working Paper Series (National Bureau of Economic Research) no. t0200. .
August 1996.
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.
System requirements: Adobe [Acrobat] Reader required for PDF files.
Mode of access: World Wide Web.