Decentralization Estimators for Instrumental Variable Quantile Regression Models

Hiroaki Kaido and Kaspar Wüthrich

Papers from arXiv.org

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.

Date: 2018-12, Revised 2020-09
New Economics Papers: this item is included in nep-ecm
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Published in Quantitative Economics, Volume 12, Issue 2 (May 2021)

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http://arxiv.org/pdf/1812.10925 Latest version (application/pdf)

Related works:
Working Paper: Decentralization estimators for instrumental variable quantile regression models (2021)
Working Paper: Decentralization estimators for instrumental variable quantile regression models (2019)
Working Paper: Decentralization estimators for instrumental variable quantile regression models (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1812.10925

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