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Decentralization estimators for instrumental variable quantile regression models

Authors: Hiroaki Kaido and Kaspar Wüthrich
Date: 16 August 2019
Type: cemmap Working Paper, CWP42/19
DOI: 10.1920/wp.cem.2019.4219

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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Hiroaki Kaido and Kaspar Wüthrich December 2018, Decentralization estimators for instrumental variable quantile regression models, cemmap Working Paper, The IFS

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