Regression discontinuity design with potentially many covariates

Yoichi Arai, Taisuke Otsu and Myung Hwan Seo

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: This article examines high-dimensional covariates in regression discontinuity design (RDD) analysis. We introduce estimation and inference methods for the RDD models that incorporate covariate selection while maintaining stability across various numbers of covariates. The proposed methods combine a localization approach using kernel weights with ℓ1-penalization to handle high-dimensional covariates. We provide both theoretical and numerical evidence demonstrating the efficacy of our methods. Theoretically, we present risk and coverage properties for our point estimation and inference methods. Conditions are given under which the proposed estimator becomes more efficient than the conventional covariate adjusted estimator at the cost of an additional sparsity condition. Numerically, our simulation experiments and empirical examples show the robust behaviors of the proposed methods to the number of covariates in terms of bias and variance for point estimation and coverage probability and interval length for inference.

JEL-codes: J1 (search for similar items in EconPapers)
Pages: 36 pages
Date: 2025-02-14
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Published in Econometric Theory, 14, February, 2025. ISSN: 0266-4666

Downloads: (external link)
http://eprints.lse.ac.uk/123669/ Open access version. (application/pdf)

Related works:
Working Paper: Regression Discontinuity Design with Potentially Many Covariates (2024)
Working Paper: Regression discontinuity design with potentially many covariates (2022)
Working Paper: Regression Discontinuity Design with Potentially Many Covariates (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:123669

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