Please use this identifier to cite or link to this item:
Title: Bayesian analysis of a Tobit quantile regression model
Authors: Yu, K
Stander, J
Keywords: Asymmetric Laplace distribution; Bayes factor; Bayesian inference; Bayesian;model
Issue Date: 2007
Publisher: Elsevier
Citation: Journal of Econometrics, 137(1): 260-276, Mar 2007
Abstract: This paper develops a Bayesian framework for Tobit quantile regression. Our approach is organized around a likelihood function that is based on the asymmetric Laplace dis- tribution, a choice that turns out to be natural in this context. We discuss families of prior distribution on the quantile regression vector that lead to proper posterior distributions with ┬»nite moments. We show how the posterior distribution can be sampled and summarized by Markov chain Monte Carlo methods. A method for com- paring alternative quantile regression models is also developed and illustrated. The techniques are illustrated with both simulated and real data. In particular, in an em- pirical comparison, our approach out-performed two other common classical estimators.
Appears in Collections:Dept of Mathematics Research Papers
Mathematical Sciences

Files in This Item:
File Description SizeFormat 
FullText.pdf207.87 kBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.