Regression discontinuity design with potentially many covariates

Yoichi Arai, Taisuke Otsu and Myung Hwan Seo

STICERD - Econometrics Paper Series from Suntory and Toyota International Centres for Economics and Related Disciplines, LSE

Abstract: This paper studies the case of possibly high-dimensional covariates in the regression discontinuity design (RDD) analysis. In particular, we propose estimation and inference methods for the RDD models with covariate selection which perform stably regardless of the number of covariates. The proposed methods combine the local approach using kernel weights with l1-penalization to handle high-dimensional covariates, and the combination is new in the literature. We provide theoretical and numerical results which illustrate the usefulness of the proposed methods. Theoretically, we present risk and coverage properties for our point estimation and inference methods, respectively. Numerically, our simulation experiments and empirical example 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.

Keywords: Regression discontinuity design; Covariate selection; Lasso (search for similar items in EconPapers)
JEL-codes: C13 C14 (search for similar items in EconPapers)
Date: 2022-10
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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https://sticerd.lse.ac.uk/dps/em/em626.pdf (application/pdf)

Related works:
Working Paper: Regression discontinuity design with potentially many covariates (2025)
Working Paper: Regression Discontinuity Design with Potentially Many Covariates (2024)
Working Paper: Regression Discontinuity Design with Potentially Many Covariates (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:cep:stiecm:626

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