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.

    Creators(s): Koop, Gary ORCID logoORCID: https://orcid.org/0000-0002-6091-378X and Poirier, Dale J.;
    Item type:Article
    ID code:6912
    Notes: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 > Statistics
    Department:Strathclyde Business School > Economics
    Depositing user: Strathprints Administrator
    Date deposited:25 Sep 2008
    Last modified:03 Mar 2021 01:02
    URI:https://strathprints.strath.ac.uk/id/eprint/6912
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