The instrumental variable quantile regression (IVQR) model (Chernozhukov and Hansen (2005)) is a popular tool for estimating causal quantile effects with endogenous covariates. However, estimation is complicated by the nonsmoothness and nonconvexity of the IVQR GMM objective function. This paper shows that the IVQR estimation problem can be decomposed into a set of conventional quantile regression subproblems which are convex and can be solved efficiently. This reformulation leads to new identification results and to fast, easy to implement, and tuning‐free estimators that do not require the availability of high‐level “black box” optimization routines.

中文翻译：

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工具变量分位数回归模型的分散估计量

工具变量分位数回归（IVQR）模型（Chernozhukov和Hansen（2005））是一种流行的工具，用于估计内生协变量的因果分位数效应。但是，IVQR GMM目标函数的不平滑和不凸性使估计变得复杂。本文表明，IVQR估计问题可以分解为凸的且可以有效解决的一组常规分位数回归子问题。这种重新制定带来了新的识别结果，并带来了快速，易于实现和无需调整的估计器，这些估计器不需要使用高级“黑匣子”优化例程。