Bayesian analysis of quantile regression for censored dynamic panel data

被引：1
作者：
Genya Kobayashi
Hideo Kozumi
机构：
[1] Kobe University,Graduate School of Business Administration
来源：
Computational Statistics | 2012年 / 27卷
关键词：
Asymmetric Laplace distribution; Bayesian quantile regression; Censored dynamic panel; Gibbs sampler; Marginal likelihood; Monte Carlo EM algorithm;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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页码：359 / 380
页数：21
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