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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.
Authors
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Hiroaki Kaido
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Kaspar Wüthrich
Working Paper details
- DOI
- 10.1920/wp.cem.2019.4219
- Publisher
- The IFS
Suggested citation
Kaido, H and Wüthrich, K. (2019). Decentralization estimators for instrumental variable quantile regression models. London: The IFS. Available at: https://ifs.org.uk/publications/decentralization-estimators-instrumental-variable-quantile-regression-models (accessed: 30 June 2024).
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