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.

翻译：本文研究含有内生变量和随机右删失的半参数分位数回归模型。内生性问题通过工具变量解决。假设结果变量对数的结构分位数与协变量呈线性关系，且删失是独立的。回归变量和工具变量可以是连续的或离散的。该设定产生了一系列方程，其分位数回归系数构成方程的解。当该方程组具有唯一解时，模型可被识别。我们的估计过程求解了方程组的经验模拟形式。我们推导了估计量渐近正态的条件，并验证了用于推断的Bootstrap程序的有效性。通过数值模拟评估了该方法的有限样本表现。对美国《职业培训合作法案》全国研究的应用实例展示了该方法。