Bayesian variants of some classical semiparametric regression techniques

Autor:	Gary Koop, Dale J. Poirier
Rok vydání:	2004
Předmět:	Economics and Econometrics Applied Mathematics HB HA Linear model HG Conjugate prior Censoring (statistics) Semiparametric model Prior probability Econometrics Applied mathematics Semiparametric regression Truncation (statistics) Bayesian linear regression Mathematics
Zdroj:	Journal of Econometrics. 123:259-282
ISSN:	0304-4076
DOI:	10.1016/j.jeconom.2003.12.008
Popis:	This paper develops new Bayesian methods for semiparametric inference in the partial linear Normal regression model: y=zβ+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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f1c5a0b4b2b4909dfb728bbf67d5d72c https://doi.org/10.1016/j.jeconom.2003.12.008 Zobrazit plný text záznamu Full Text from ScienceDirect