Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/211135 
Erscheinungsjahr: 
2019
Schriftenreihe/Nr.: 
cemmap working paper No. CWP42/19
Verlag: 
Centre for Microdata Methods and Practice (cemmap), London
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 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.
Schlagwörter: 
instrumental variables
quantile regression
contraction mapping
fixed pointestimator
bootstrap
JEL: 
C21
C26
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
Working Paper

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