Optimal Bandwidth Choice for the Regression Discontinuity Estimator
TL;DR: In this article, the authors investigate the problem of optimal choice of the smoothing parameter (bandwidth) for the regression discontinuity estimator, and propose an optimal, data dependent, bandwidth choice rule.
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Abstract: 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). Investigation of an expected-squared-error-loss criterion reveals the need for regularization. We propose an optimal, data dependent, bandwidth choice rule. We illustrate the proposed bandwidth choice using data previously analyzed by Lee (2008), as well as in a simulation study based on this data set. The simulations suggest that the proposed rule performs well.
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