论文标题
仪器可变的分位数回归在随机右审查下
Instrumental variable quantile regression under random right censoring
论文作者
论文摘要
本文研究了具有内源性变量和随机右审查的半参数分位数回归模型。内生性问题是使用仪器变量解决的。假定结果变量对数的结构分位数在协变量中是线性的，并且审查是独立的。回归器和仪器可以是连续的或离散的。该规范会生成一个连续的方程式，其分位数回归系数为解决方案。当该方程系统具有独特的解决方案时，获得识别。我们的估计程序解决了方程系统的经验类似物。我们得出估计量渐近正常的条件，并证明了引导程序的推理有效性。通过数值模拟评估该方法的有限样本性能。 《国家职业培训合作法》研究的申请说明了该方法。
This paper studies a semiparametric quantile regression model with endogenous variables and random right censoring. The endogeneity issue is solved using instrumental variables. It is assumed that the structural quantile of the logarithm of the outcome variable is linear in the covariates and censoring is independent. The regressors and instruments can be either continuous or discrete. The specification generates a continuum of equations of which the quantile regression coefficients are a solution. Identification is obtained when this system of equations has a unique solution. Our estimation procedure solves an empirical analogue of the system of equations. We derive conditions under which the estimator is asymptotically normal and prove the validity of a bootstrap procedure for inference. The finite sample performance of the approach is evaluated through numerical simulations. An application to the national Job Training Partnership Act study illustrates the method.
