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.

中文翻译：

---

随机右删失下的工具变量分位数回归

本文研究了具有内生变量和随机右删失的半参数分位数回归模型。内生性问题可以通过工具变量来解决。假设结果变量的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。回归量和工具可以是连续的或离散的。该规范生成方程的连续体，其中分位数回归系数是其解。当该方程组具有唯一解时，即可获得辨识。我们的估计程序解决了方程组的经验模拟。我们推导出估计量渐近正态的条件，并证明自举程序推理的有效性。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应用说明了该方法。