Regression discontinuity design with potentially many covariates

Arai, Yoichi, Otsu, Taisuke ORCID: 0000-0002-2307-143X and Seo, Myung Hwan (2024) Regression discontinuity design with potentially many covariates. Econometric Theory. ISSN 0266-4666 (In Press)

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Abstract

This paper examines high-dimensional covariates in regression discontinuity design (RDD) analysis. We introduce estimation and inference methods for 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.

Item Type:	Article
Additional Information:	© 2024 The Author
Divisions:	Economics
Subjects:	H Social Sciences > HB Economic Theory
Date Deposited:	28 May 2024 10:51
Last Modified:	17 Oct 2024 18:15
URI:	http://eprints.lse.ac.uk/id/eprint/123669

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