Why High-Order Polynomials Should Not Be Used in Regression Discontinuity Designs

A-Tier
Journal: Journal of Business & Economic Statistics
Year: 2019
Volume: 37
Issue: 3
Pages: 447-456

Authors (2)

Andrew Gelman (not in RePEc) Guido Imbens (Stanford University)

Score contribution per author:

2.011 = (α=2.01 / 2 authors) × 2.0x A-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

Abstract

It is common in regression discontinuity analysis to control for third, fourth, or higher-degree polynomials of the forcing variable. There appears to be a perception that such methods are theoretically justified, even though they can lead to evidently nonsensical results. We argue that controlling for global high-order polynomials in regression discontinuity analysis is a flawed approach with three major problems: it leads to noisy estimates, sensitivity to the degree of the polynomial, and poor coverage of confidence intervals. We recommend researchers instead use estimators based on local linear or quadratic polynomials or other smooth functions.

Technical Details

RePEc Handle
repec:taf:jnlbes:v:37:y:2019:i:3:p:447-456
Journal Field
Econometrics
Author Count
2
Added to Database
2026-01-25