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Decentralization Estimators for Instrumental Variable Quantile Regression Models

Hiroaki Kaido () and Kaspar Wüthrich ()

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Abstract: 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 non-smoothness and non-convexity of the IVQR GMM objective function. This paper shows that the IVQR estimation problem can be decomposed into a set of conventional quantile regression sub-problems 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.

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Date: 2018-12, Revised 2019-08
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Working Paper: Decentralization estimators for instrumental variable quantile regression models (2018) Downloads
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Handle: RePEc:arx:papers:1812.10925