KOBAYASHI GENYA    Department   Undergraduate School  , School of Commerce    Position   Professor
Language	English
Publication Date	2012/06
Type	Academic Journal
Peer Review	Peer reviewed
Title	Bayesian analysis of quantile regression for censored dynamic panel data
Contribution Type	Co-authored (first author)
Journal	COMPUTATIONAL STATISTICS
Journal Type	Another Country
Publisher	SPRINGER HEIDELBERG
Volume, Issue, Page	27(2),pp.359-380
Author and coauthor	Genya Kobayashi, Hideo Kozumi
Details	This paper develops a Bayesian approach to analyzing quantile regression models for censored dynamic panel data. We employ a likelihood-based approach using the asymmetric Laplace error distribution and introduce lagged observed responses into the conditional quantile function. We also deal with the initial conditions problem in dynamic panel data models by introducing correlated random effects into the model. For posterior inference, we propose a Gibbs sampling algorithm based on a location-scale mixture representation of the asymmetric Laplace distribution. It is shown that the mixture representation provides fully tractable conditional posterior densities and considerably simplifies existing estimation procedures for quantile regression models. In addition, we explain how the proposed Gibbs sampler can be utilized for the calculation of marginal likelihood and the modal estimation. Our approach is illustrated with real data on medical expenditures.
DOI	10.1007/s00180-011-0263-3
ISSN	0943-4062