Decentralization estimators for instrumental variable quantile regression models

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.

Automated summary

The IVQR estimation problem can be decomposed into a set of conventional quantile regression subproblems which are convex and can be efficiently solved. This leads to new identification results and fast, easy to implement, tuning-free estimators that do not require high-level black box optimization routines.