Optimal bandwidth choice for robust bias-corrected inference in regression discontinuity designs

Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell

Research output: Contribution to journalArticlepeer-review

Abstract

Summary: Modern empirical work in regression discontinuity (RD) designs often employs local polynomial estimation and inference with a mean square error (MSE) optimal bandwidth choice. This bandwidth yields an MSE-optimal RD treatment effect estimator, but is by construction invalid for inference. Robust bias-corrected (RBC) inference methods are valid when using the MSE-optimal bandwidth, but we show that they yield suboptimal confidence intervals in terms of coverage error. We establish valid coverage error expansions for RBC confidence interval estimators and use these results to propose new inference-optimal bandwidth choices for forming these intervals. We find that the standard MSE-optimal bandwidth for the RD point estimator is too large when the goal is to construct RBC confidence intervals with the smaller coverage error rate. We further optimize the constant terms behind the coverage error to derive new optimal choices for the auxiliary bandwidth required for RBC inference. Our expansions also establish that RBC inference yields higher-order refinements (relative to traditional undersmoothing) in the context of RD designs. Our main results cover sharp and sharp kink RD designs under conditional heteroskedasticity, and we discuss extensions to fuzzy and other RD designs, clustered sampling, and pre-intervention covariates adjustments. The theoretical findings are illustrated with a Monte Carlo experiment and an empirical application, and the main methodological results are available in R and Stata packages.

Original languageEnglish (US)
Pages (from-to)192-210
Number of pages19
JournalEconometrics Journal
Volume23
Issue number2
DOIs
StatePublished - 2021
Externally publishedYes

Keywords

  • Coverage error
  • Edgeworth expansions
  • Local polynomial regression
  • Treatment effects
  • Tuning parameter selection

ASJC Scopus subject areas

  • Economics and Econometrics

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