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Bayesian variants of some classical semiparametric regression techniques [An article from: Journal of Econometrics]
Book Details
Author(s)G. Koop, D.J. Poirier
PublisherElsevier
ISBN / ASINB000RR47CS
ISBN-13978B000RR47C3
MarketplaceFrance 🇫🇷
Description
This digital document is a journal article from Journal of Econometrics, published by Elsevier in 2004. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
This paper develops new Bayesian methods for semiparametric inference in the partial linear Normal regression model: y=z@b+f(x)+@e 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.
Description:
This paper develops new Bayesian methods for semiparametric inference in the partial linear Normal regression model: y=z@b+f(x)+@e 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.
