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

Gelman, Andrew, Imbens, Guido·2019·Journal of Business & Economic Statistics

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.

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Research works printed but not linked: 17

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Metadata

Authors
Gelman, A., & Imbens, G.
Affiliations
  • Department of Statistics and Department of Political Science, Columbia University, New York, NY, 10027 ()
  • Graduate School of Business, Stanford University, Stanford, CA 94305
Venue
Journal of Business & Economic Statistics
Published
2018-05-14
Volume
37
Issue
3
Pages
447-456
ISSN
  • 0735-0015
  • 1537-2707
Cited by
1,101
References
2
Language
en

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