Local Composite Quantile Regression for Regression Discontinuity

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.

Keywords:

• Local composite quantile regression
• Regression discontinuity
• Treatment effect

Supplementary Materials

The related codes and data are provided in the R package rdcqr, which can be downloaded from the links in the article and the Supplement.

Acknowledgments

We thank the editor, the associate editor, and two anonymous referees for their comments that substantially improved the article.

Additional information

Funding

The authors gratefully acknowledge the financial support of the Education Economics Center and the computational support of the Office of Research at Kennesaw State University, and have no conflicts of interest to disclose.