Decentralization estimators for instrumental variable quantile regression models

• 成果类型: Article
• 署名作者: Kaido, Hiroaki; Wuthrich, Kaspar
• 署名单位: Boston University; University of California System; University of California San Diego
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE1440
• 发表日期: 2021
• 页码: 443-475
• 关键词: instrumental variables; quantile regression; contraction mapping; fixed-point estimator; bootstrap

摘要

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.