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Inference in regression discontinuity designs with a discrete running variable

Author(s): Kolesár, Michal; Rothe, Christoph

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Abstract: © 2018 American Economic Association. All rights reserved. We consider inference in regression discontinuity designs when the running variable only takes a moderate number of distinct values. In particular, we study the common practice of using confidence intervals (CIs) based on standard errors that are clustered by the running variable as a means to make inference robust to model misspecification (Lee and Card 2008). We derive theoretical results and present simulation and empirical evidence showing that these CIs do not guard against model misspecification, and that they have poor coverage properties. We therefore recommend against using these CIs in practice. We instead propose two alternative CIs with guaranteed coverage properties under easily interpretable restrictions on the conditional expectation function. (JEL C13, C51, J13, J31, J64, J65).
Publication Date: 1-Aug-2018
Citation: Kolesár, M, Rothe, C. (2018). Inference in regression discontinuity designs with a discrete running variable. American Economic Review, 108 (8), 2277 - 2304. doi:10.1257/aer.20160945
DOI: doi:10.1257/aer.20160945
ISSN: 0002-8282
Pages: 2277 - 2304
Type of Material: Journal Article
Journal/Proceeding Title: American Economic Review
Version: Final published version. Article is made available in OAR by the publisher's permission or policy.

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