Bayesian variants of some classical semiparametric regression techniques
Koop, Gary and Poirier, Dale J. (2004) Bayesian variants of some classical semiparametric regression techniques. Journal of Econometrics, 123 (2). pp. 259-282. ISSN 0304-4076
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Abstract
This paper develops new Bayesian methods for semiparametric inference in the partial linear Normal regression model: y=zβ+f(x)+var epsilon where f(.) is an unknown function. These methods draw solely on the Normal linear regression model with natural conjugate prior. Hence, posterior results are available which do not suffer from some problems which plague the existing literature such as computational complexity. Methods for testing parametric regression models against semiparametric alternatives are developed. We discuss how these methods can, at some cost in terms of computational complexity, be extended to other models (e.g. qualitative choice models or those involving censoring or truncation) and provide precise details for a semiparametric probit model. We show how the assumption of Normal errors can easily be relaxed.
ORCID iDs
Koop, Gary
Item type: Article ID code: 6912 Dates: DateEventDecember 2004PublishedNotes: Working paper version Keywords: partial linear model, nonparametric regression model, semiparametric probit, extreme bounds analysis, Finance, Economic Theory, Statistics, History and Philosophy of Science, Economics and Econometrics, Applied Mathematics Subjects: Social Sciences > Finance
Social Sciences > Economic Theory
Social Sciences > StatisticsDepartment: Strathclyde Business School > Economics Depositing user: Strathprints Administrator Date deposited: 25 Sep 2008 Last modified: 30 Mar 2021 00:23 URI: https://strathprints.strath.ac.uk/id/eprint/6912