Gibbs Sampling Methods for Bayesian Quantile Regression

Hideo Kozumi and Genya Kobayashi
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Hideo Kozumi: Graduate School of Business Administration, Kobe University
Genya Kobayashi: Graduate School of Business Administration, Kobe University

No 2009-02, Discussion Papers from Kobe University, Graduate School of Business Administration

Abstract: This paper considers quantile regression models using an asymmetric Laplace distribution from a Bayesian point of view. We develop a simple and efficient Gibbs sampling algorithm for fitting the quantile regression model based on a location-scale mixture representation of the asymmetric Laplace distribution. It is shown that the resulting Gibbs sampler can be accomplished by sampling from either normal or generalized inverse Gaussian distribution. We also discuss some possible extensions of our approach, including the incorporation of a scale parameter, the use of double exponential prior, and a Bayesian analysis of Tobit quantile regression. The proposed methods are illustrated by both simulated and real data.

Keywords: Asymmetric Laplace distribution; Bayesian quantile regression; double exponential prior; generalized inverse Gaussian distribution; Gibbs sampler; Tobit quantile regression (search for similar items in EconPapers)
Pages: 15 pages
Date: 2009-02
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Citations: View citations in EconPapers (29)

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