Bayesian analysis of quantile regression for censored dynamic panel data
Bayesian analysis of quantile regression for censored dynamic panel data
复制标题
DOI：
10.1007/s00180-011-0263-3
复制
发表时间：
2012-06
影响因子：
1.3
通讯作者：
Genya Kobayashi;H. Kozumi
中科院分区：
文献类型：
--
作者：
Genya Kobayashi;H. Kozumi
本文提出了一种分析截尾动态面板数据分位数回归模型的贝叶斯方法。我们采用了基于似然的方法，使用不对称的拉普拉斯误差分布，并引入滞后观测响应的条件分位数函数。通过在动态面板数据模型中引入相关随机效应，讨论了模型的初始条件问题。对于后验推理，我们提出了一个吉布斯抽样算法的基础上的位置-尺度混合表示的非对称拉普拉斯分布。结果表明，混合表示提供了完全听话的条件后验密度，大大简化了现有的估计程序的分位数回归模型。此外，我们解释了如何建议吉布斯采样器可以用于计算边际似然和模态估计。我们的方法是说明与医疗支出的真实的数据。
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.