Strathprints logo
Strathprints Home | Open Access | Browse | Search | User area | Copyright | Help | Library Home | SUPrimo

Bayesian variants of some classical semiparametric regression techniques

Koop, G.M. and Poirier, D. (2004) Bayesian variants of some classical semiparametric regression techniques. Journal of Econometrics, 123 (2). pp. 259-282. ISSN 0304-4076

[img]
Preview
PDF (strathprints006912.pdf)
strathprints006912.pdf

Download (2MB) | Preview

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.

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: 21 May 2015 09:39
Related URLs:
URI: http://strathprints.strath.ac.uk/id/eprint/6912

Actions (login required)

View Item View Item