Local Composite Quantile Regression for Regression Discontinuity
Department
Economics, Finance and Quantitative Analysis
Document Type
Article
Publication Date
1-1-2021
Abstract
We introduce the local composite quantile regression (LCQR) to causal inference in regression discontinuity (RD) designs. Kai, Li and Zou study the efficiency property of LCQR, while we show that its nice boundary performance translates to accurate estimation of treatment effects in RD under a variety of data generating processes. Moreover, we propose a bias-corrected and standard error-adjusted t-test for inference, which leads to confidence intervals with good coverage probabilities. A bandwidth selector is also discussed. For illustration, we conduct a simulation study and revisit a classic example from Lee. A companion R package rdcqr is developed.
Journal Title
Journal of Business and Economic Statistics
Journal ISSN
07350015
Digital Object Identifier (DOI)
10.1080/07350015.2021.1990072