Local Composite Quantile Regression for Regression Discontinuity

Xiao Huang and Zhaoguo Zhan

Journal of Business & Economic Statistics, 2022, vol. 40, issue 4, 1863-1875

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.

Date: 2022
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Working Paper: Local Composite Quantile Regression for Regression Discontinuity (2021)
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DOI: 10.1080/07350015.2021.1990072

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