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Simultaneous selection of optimal bandwidths for the sharp regression discontinuity estimator

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  • Yoichi Arai
  • Hidehiko Ichimura

Abstract

A new bandwidth selection method that uses different bandwidths for the local linear regression estimators on the left and the right of the cut‐off point is proposed for the sharp regression discontinuity design estimator of the average treatment effect at the cut‐off point. The asymptotic mean squared error of the estimator using the proposed bandwidth selection method is shown to be smaller than other bandwidth selection methods proposed in the literature. The approach that the bandwidth selection method is based on is also applied to an estimator that exploits the sharp regression kink design. Reliable confidence intervals compatible with both of the proposed bandwidth selection methods are also proposed as in the work of Calonico, Cattaneo, and Titiunik (2014a). An extensive simulation study shows that the proposed method's performances for the samples sizes 500 and 2000 closely match the theoretical predictions. Our simulation study also shows that the common practice of halving and doubling an optimal bandwidth for sensitivity check can be unreliable.

Suggested Citation

  • Yoichi Arai & Hidehiko Ichimura, 2018. "Simultaneous selection of optimal bandwidths for the sharp regression discontinuity estimator," Quantitative Economics, Econometric Society, vol. 9(1), pages 441-482, March.
  • Handle: RePEc:wly:quante:v:9:y:2018:i:1:p:441-482
    DOI: 10.3982/QE590
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    Cited by:

    1. Sebastian Calonico & Matias D. Cattaneo & Max H. Farrell & Filippo Palomba & Rocio Titiunik, 2025. "rdhte: Conditional Average Treatment Effects in RD Designs," Papers 2507.01128, arXiv.org.
    2. Gary Cornwall & Beau Sauley, 2021. "Indirect effects and causal inference: reconsidering regression discontinuity," Journal of Spatial Econometrics, Springer, vol. 2(1), pages 1-28, December.
    3. Shi, Xunpeng & Tian, Binbin & Yang, Longjian & Yu, Jian & Zhou, Siyang, 2023. "How do regulatory environmental policies perform? A case study of China's Top-10,000 enterprises energy-saving program," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
    4. Jales, Hugo & Ma, Jun & Yu, Zhengfei, 2017. "Optimal bandwidth selection for local linear estimation of discontinuity in density," Economics Letters, Elsevier, vol. 153(C), pages 23-27.
    5. Arai, Yoichi & Ichimura, Hidehiko, 2016. "Optimal bandwidth selection for the fuzzy regression discontinuity estimator," Economics Letters, Elsevier, vol. 141(C), pages 103-106.
    6. Yoici Arai & Taisuke Otsu & Myung Hwan Seo, 2019. "Causal inference on regression discontinuity designs by high-dimensional methods," STICERD - Econometrics Paper Series 601, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    7. Octave De Brouwer & Elisabeth Leduc & Ilan Tojerow, 2019. "The Unexpected Consequences of Job Search Monitoring: Disability Instead of Employment ?," ULB Institutional Repository 2013/340666, ULB -- Universite Libre de Bruxelles.
    8. Sebastian Calonico & Matias D. Cattaneo & Max H. Farrell & Rocío Titiunik, 2019. "Regression Discontinuity Designs Using Covariates," The Review of Economics and Statistics, MIT Press, vol. 101(3), pages 442-451, July.
    9. Chiang, Harold D. & Hsu, Yu-Chin & Sasaki, Yuya, 2019. "Robust uniform inference for quantile treatment effects in regression discontinuity designs," Journal of Econometrics, Elsevier, vol. 211(2), pages 589-618.
    10. Masayuki Sawada & Takuya Ishihara & Daisuke Kurisu & Yasumasa Matsuda, 2024. "Local-Polynomial Estimation for Multivariate Regression Discontinuity Designs," Papers 2402.08941, arXiv.org, revised Jan 2026.
    11. Xu, Ke-Li, 2017. "Regression discontinuity with categorical outcomes," Journal of Econometrics, Elsevier, vol. 201(1), pages 1-18.
    12. Mauricio Villamizar‐Villegas & Freddy A. Pinzon‐Puerto & Maria Alejandra Ruiz‐Sanchez, 2022. "A comprehensive history of regression discontinuity designs: An empirical survey of the last 60 years," Journal of Economic Surveys, Wiley Blackwell, vol. 36(4), pages 1130-1178, September.
    13. Xu, Ke-Li, 2018. "A semi-nonparametric estimator of regression discontinuity design with discrete duration outcomes," Journal of Econometrics, Elsevier, vol. 206(1), pages 258-278.
    14. Carolina Caetano & Gregorio Caetano & Leonard Goff & Eric Nielsen, 2025. "Identification of Causal Effects with a Bunching Design," Papers 2507.05210, arXiv.org.
    15. YANAGI, Takahide & 柳, 貴英, 2015. "Regression Discontinuity Designs with Nonclassical Measurement Error," Discussion Papers 2015-09, Graduate School of Economics, Hitotsubashi University.
    16. Takahide Yanagi, 2014. "The Effect of Measurement Error in the Sharp Regression Discontinuity Design," KIER Working Papers 910, Kyoto University, Institute of Economic Research.
    17. Yang Lixiong, 2019. "Regression discontinuity designs with unknown state-dependent discontinuity points: estimation and testing," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 23(2), pages 1-18, April.
    18. Blaise Melly & Rafael Lalive, 2020. "Estimation, Inference, and Interpretation in the Regression Discontinuity Design," Diskussionsschriften dp2016, Universitaet Bern, Departement Volkswirtschaft.
    19. Jun Ma & Zhengfei Yu, 2020. "Empirical Likelihood Covariate Adjustment for Regression Discontinuity Designs," Papers 2008.09263, arXiv.org, revised May 2024.
    20. Yuta Okamoto & Yuuki Ozaki, 2024. "On Extrapolation of Treatment Effects in Multiple-Cutoff Regression Discontinuity Designs," Papers 2412.04265, arXiv.org, revised Sep 2025.

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