ON NONPARAMETRIC INFERENCE IN THE REGRESSION DISCONTINUITY DESIGN

Content Provider	Scilit
Author	Kamat, Vishal
Copyright Year	2017
Description	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.
Related Links	http://arxiv.org/pdf/1505.06483 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/EE55108405061F2B0A24EABD47818D58/S0266466617000196a.pdf/div-class-title-on-nonparametric-inference-in-the-regression-discontinuity-design-div.pdf
Ending Page	703
Page Count	10
Starting Page	694
ISSN	02664666
e-ISSN	14694360
DOI	10.1017/s0266466617000196
Journal	Econometric Theory
Issue Number	3
Volume Number	34
Language	English
Publisher	Cambridge University Press (CUP)
Publisher Date	2018-06-01
Access Restriction	Open
Subject Keyword	Econometric Theory Cybernetical Science
Content Type	Text
Resource Type	Article
Subject	Social Sciences Economics and Econometrics