Bayesian analysis of a Tobit quantile regression model
Bayesian analysis of a Tobit quantile regression model
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DOI：
10.1016/j.jeconom.2005.10.002
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发表时间：
2007-03
影响因子：
6.3
通讯作者：
Keming Yu;J. Stander
中科院分区：
文献类型：
--
作者：
Keming Yu;J. Stander
本文提出了一个Tobit分位数回归的贝叶斯框架。我们的方法是围绕一个似然函数，这是基于不对称的拉普拉斯分布，在这种情况下，一个选择，原来是自然的。我们讨论的家庭的先验分布的分位数回归向量，导致适当的后验分布有限的时刻。我们展示了如何后验分布可以抽样和总结马尔可夫链蒙特卡罗方法。比较替代分位数回归模型的方法也开发和说明。该技术与模拟和真实的数据说明。特别是，在实证比较中，我们的方法优于其他两个常见的经典估计。
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 distribution, a choice that turns out to be natural in this context. We discuss families of prior distributions on the quantile regression vector that lead to proper posterior distributions with finite moments. We show how the posterior distribution can be sampled and summarized by Markov chain Monte Carlo methods. A method for comparing alternative quantile regression models is also developed and illustrated. The techniques are illustrated with both simulated and real data. In particular, in an empirical comparison, our approach out-performed two other common classical estimators.