Local Polynomial Order in Regression Discontinuity Designs

David Card, Zhuan Pei, David Lee and Andrea Weber
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David Lee: Princeton University and NBER

No 81, Working Papers from Brandeis University, Department of Economics and International Business School

Abstract: The local linear estimator has become the standard in the regression discontinuity design literature, but we argue that it should not always dominate other local polynomial estimators in empirical studies. We show that the local linear estimator in the data generating processes (DGP’s) based on two well- known empirical examples does not always have the lowest (asymptotic) mean squared error (MSE). Therefore, we advocate for a more flexible view towards the choice of the polynomial order, p, and suggest two complementary approaches for picking p: comparing the MSE of alternative estimators from Monte Carlo simulations based on an approximating DGP, and comparing the estimated asymptotic MSE using actual data.Length: 47 pages

Keywords: Regression Discontinuity Design; Regression Kink Design; Local Polynomial Estima- tion; Polynomial Order (search for similar items in EconPapers)
Date: 2014-10
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (7)

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http://www.brandeis.edu/economics/RePEc/brd/doc/Brandeis_WP81.pdf First version, 2014 (application/pdf)

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