Bayesian Endogenous Tobit Quantile Regression
Bayesian Endogenous Tobit Quantile Regression
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DOI：
10.1214/16-ba996
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发表时间：
2017-03-01
影响因子：
4.4
通讯作者：
Kobayashi, Genya
中科院分区：
文献类型：
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作者：
Kobayashi, Genya
本文提出了带内生变量的p-th Tobit分位数回归模型。在内源变量对外源变量的第一阶段回归中，引入了误差项的α -th分位数为零的假设。然后，将该回归模型的残差包含在第p个分位数回归模型中，使新误差项的第p个条件分位数为零。第一阶段回归的误差分布通过参数和半参数方法围绕零α -th分位数假设建模。由于alpha的值是先验未知的，因此它被视为附加参数并从数据中估计。然后通过模拟数据和已婚妇女劳动力供给的真实数据来证明所提出的模型。
This study proposes p-th Tobit quantile regression models with endogenous variables. In the first stage regression of the endogenous variable on the exogenous variables, the assumption that the alpha-th quantile of the error term is zero is introduced. Then, the residual of this regression model is included in the p-th quantile regression model in such a way that the p-th conditional quantile of the new error term is zero. The error distribution of the first stage regression is modelled around the zero alpha-th quantile assumption by using parametric and semiparametric approaches. Since the value of alpha is a priori unknown, it is treated as an additional parameter and is estimated from the data. The proposed models are then demonstrated by using simulated data and real data on the labour supply of married women.