000			02114cam a22003137 4500
001			t0200
003			NBER
005			20211020114116.0
006			m o d
007			cr cnu||||||||
008			210910s1996 mau fo 000 0 eng d
100	1		_aChamberlain, Gary. _97744
245	1	0	_aNonparametric Applications of Bayesian Inference / _cGary Chamberlain, Guido W. Imbens.
260			_aCambridge, Mass. _bNational Bureau of Economic Research _c1996.
300			_a1 online resource: _billustrations (black and white);
490	1		_aNBER technical working paper series _vno. t0200
500			_aAugust 1996.
520	3		_aThe 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.
530			_aHardcopy version available to institutional subscribers
538			_aSystem requirements: Adobe [Acrobat] Reader required for PDF files.
538			_aMode of access: World Wide Web.
588	0		_aPrint version record
700	1		_aImbens, Guido W. _913314
710	2		_aNational Bureau of Economic Research.
830		0	_aTechnical Working Paper Series (National Bureau of Economic Research) _vno. t0200.
856	4	0	_uhttps://www.nber.org/papers/t0200
856			_yAcceso en línea al DOI _uhttp://dx.doi.org/10.3386/t0200
942			_2ddc _cW-PAPER
999			_c342625 _d301187