Bayesian Variants of Some Classical Semiparametric Regression Techniques

Gary Koop and D. Poirier

Working Papers from California Irvine - School of Social Sciences

Abstract: This paper develops new Bayesian methods for semiparametric inference in the partial linear Normal regression model. These methodes draw solely on teh Normal linear regression model with natural conjugate prior. Hence, analytical finite sample results are available which do not suffer form problems of theoretical and computational complexity which plague the existing literature.

Keywords: MODELS; TESTS; MATHEMATICAL ANALYSIS (search for similar items in EconPapers)
JEL-codes: C11 C14 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2000
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Bayesian variants of some classical semiparametric regression techniques (2004)
Working Paper: Bayesian Variants of Some classical Semiparametric Regression Techniques (2001)
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Persistent link: https://EconPapers.repec.org/RePEc:fth:calirv:00-01-22

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