Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/253599 
Erscheinungsjahr: 
2021
Quellenangabe: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 12 [Issue:] 2 [Publisher:] The Econometric Society [Place:] New Haven, CT [Year:] 2021 [Pages:] 443-475
Verlag: 
The Econometric Society, New Haven, CT
Zusammenfassung: 
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.
Schlagwörter: 
bootstrap
contraction mapping
fixed-point estimator
Instrumental variables
quantile regression
JEL: 
C21
C26
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