<p><p>We investigate the problem of optimal choice of the smoothing parameter (bandwidth) for the regression discontinuity estimator. We focus on estimation by local linear regression, which was shown to be rate optimal (Porter, 2003). We derive the optimal bandwidth. This optimal bandwidth depends on unknown functionals of the distribution of the data and we propose specific, consistent, estimators for these functionals to obtain a fully data-driven bandwidth choice that has the "asymptotic no-regret" property. We illustrate our proposed bandwidth, and the sensitivity to the choices made in this bandwidth proposal, using a data set previously analyzed by Lee (2008), as well as a small simulation study based on the Lee data set. The simulations suggest that the proposed rule performs well.</p></p>
Authors
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Stanford Graduate School of Business
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
American Enterprise Institute
Working Paper details
- DOI
- 10.1920/wp.cem.2010.0510
- Publisher
- IFS
Suggested citation
Imbens, G and Kalyanaraman, K. (2010). Optimal bandwidth choice for the regression discontinuity estimator. London: IFS. Available at: https://ifs.org.uk/publications/optimal-bandwidth-choice-regression-discontinuity-estimator (accessed: 1 July 2024).
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