Title

Local Polynomial Order in Regression Discontinuity Designs

Author(s)

Zhuan Pei Zhuan Pei (Cornell University and IZA)

David S. Lee David Lee (Princeton University and NBER)

David Card David Card (UC Berkeley, NBER and IZA)

Andrea Weber Andrea Weber (Central European University and IZA)

Abstract

It has become standard practice to use local linear regressions in regression discontinuity designs. This paper highlights that the same theoretical arguments used to justify local linear regression suggest that alternative local polynomials could be preferred. We show in simulations that the local linear estimator is often dominated by alternative polynomial specifications. Additionally, we provide guidance on the selection of the polynomial order. The Monte Carlo evidence shows that the order-selection procedure (which is also readily adapted to fuzzy regression discontinuity and regression kink designs) performs well, particularly with large sample sizes typically found in empirical applications.

Creation Date

2018-08

Section URL ID

IRS

Paper Number

622

URL

https://dataspace.princeton.edu/bitstream/88435/dsp01v118rh27h/3/622.pdf

File Function

Jel

Keyword(s)

Regression Discontinuity Design; Regression Kink Design; Local Polynomial Estimation; Polynomial Order

Suppress

false

Series

1