关静, 王力群. 带测量误差的线性分位数回归模型的工具变量估计[J]. 应用概率统计, 2017, 33(5): 475-486. DOI: 10.3969/j.issn.1001-4268.2017.05.004
引用本文: 关静, 王力群. 带测量误差的线性分位数回归模型的工具变量估计[J]. 应用概率统计, 2017, 33(5): 475-486. DOI: 10.3969/j.issn.1001-4268.2017.05.004
GUAN Jing, WANG LiQun. Instrumental Variable Estimation in Linear Quantile Regression Models with Measurement Error[J]. Chinese Journal of Applied Probability and Statistics, 2017, 33(5): 475-486. DOI: 10.3969/j.issn.1001-4268.2017.05.004
Citation: GUAN Jing, WANG LiQun. Instrumental Variable Estimation in Linear Quantile Regression Models with Measurement Error[J]. Chinese Journal of Applied Probability and Statistics, 2017, 33(5): 475-486. DOI: 10.3969/j.issn.1001-4268.2017.05.004

带测量误差的线性分位数回归模型的工具变量估计

Instrumental Variable Estimation in Linear Quantile Regression Models with Measurement Error

  • 摘要: 本文将工具变量法由研究带变量误差的均值回归模型推广到研究带变量误差的线性分位数回归模型.所得到的估计量是一致的且在一般条件下具有渐近正态分布. 这种方法可行且易于操作.模拟研究表明该估计量在有限样本下性质表现非常好. 最后这种方法被应用到实际问题,研究工资与教育程度之间的关系.

     

    Abstract: We extend the instrumental variable method for the mean regression models to linear quantile regression models with errors-in-variables. The proposed estimator is consistent and asymptotically normally distributed under some fairly general conditions. Moreover, this approach is practical and easy to implement. Simulation studies show that the finite sample performance of the estimator is satisfactory. The method is applied to a real data study of education and wages.

     

/

返回文章
返回