Decentralization estimators for instrumental variable quantile regression models

Hiroaki Kaido and Kaspar Wüthrich

University of California at San Diego, Economics Working Paper Series from Department of Economics, UC San Diego

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 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.

Keywords: Instrumental variables; quantile regression; contraction mapping; fixed-point estimator; bootstrap; Econometrics (search for similar items in EconPapers)
Date: 2021-01-01
New Economics Papers: this item is included in nep-dcm
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Citations: View citations in EconPapers (8)

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Related works:
Working Paper: Decentralization Estimators for Instrumental Variable Quantile Regression Models (2020)
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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