Local Polynomial Order in Regression Discontinuity Designs

Zhuan Pei, David S. Lee, David Card and Andrea Weber

No 27424, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: Treatment effect estimates in regression discontinuity (RD) designs are often sensitive to the choice of bandwidth and polynomial order, the two important ingredients of widely used local regression methods. While Imbens and Kalyanaraman (2012) and Calonico, Cattaneo and Titiunik (2014) provide guidance on bandwidth, the sensitivity to polynomial order still poses a conundrum to RD practitioners. It is understood in the econometric literature that applying the argument of bias reduction does not help resolve this conundrum, since it would always lead to preferring higher orders. We therefore extend the frameworks of Imbens and Kalyanaraman (2012) and Calonico, Cattaneo and Titiunik (2014) and use the asymptotic mean squared error of the local regression RD estimator as the criterion to guide polynomial order selection. We show in Monte Carlo simulations that the proposed order selection procedure performs well, particularly in large sample sizes typically found in empirical RD applications. This procedure extends easily to fuzzy regression discontinuity and regression kink designs.

JEL-codes: C21 (search for similar items in EconPapers)
Date: 2020-06
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Citations: View citations in EconPapers (9)

Published as Zhuan Pei & David S. Lee & David Card & Andrea Weber, 2022. "Local Polynomial Order in Regression Discontinuity Designs," Journal of Business & Economic Statistics, vol 40(3), pages 1259-1267.

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Related works:
Journal Article: Local Polynomial Order in Regression Discontinuity Designs (2022)
Working Paper: Local Polynomial Order in Regression Discontinuity Design (2020)
Working Paper: Local Polynomial Order in Regression Discontinuity Designs (2018)
Working Paper: Local Polynomial Order in Regression Discontinuity Designs (2014)
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