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ON NONPARAMETRIC INFERENCE IN THE REGRESSION DISCONTINUITY DESIGN

Published online by Cambridge University Press:  09 May 2017

Vishal Kamat
Vishal Kamat*
Affiliation:
Northwestern University
*
*Address correspondence to Vishal Kamat, Department of Economics, Northwestern University, Evanston, IL 60208, USA; e-mail: v.kamat@u.northwestern.edu.
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Abstract

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This paper studies the validity of nonparametric tests used in the regression discontinuity design. The null hypothesis of interest is that the average treatment effect at the threshold in the so-called sharp design equals a pre-specified value. We first show that, under assumptions used in the majority of the literature, for any test the power against any alternative is bounded above by its size. This result implies that, under these assumptions, any test with nontrivial power will exhibit size distortions. We next provide a sufficient strengthening of the standard assumptions under which we show that a version of a test suggested in Calonico, Cattaneo, and Titiunik (2014) can control limiting size.

Information

Type
MISCELLANEA
Information
Econometric Theory , Volume 34 , Issue 3 , June 2018, pp. 694 - 703
Copyright
Copyright © Cambridge University Press 2017 

Footnotes

I am grateful to Ivan Canay for his valuable guidance and suggestions. I thank the Co-Editor, two anonymous referees, Matias Cattaneo, Joel Horowitz, Pedro Sant’Anna and Max Tabord-Meehan for their helpful comments.

References

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