Inference in Regression Discontinuity Designs with a Discrete Running Variable

Michal Kolesár () and Christoph Rothe
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Michal Kolesár: Princeton University

No 9990, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: 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. We derive theoretical results and present simulation and empirical evidence showing that these CIs have poor coverage properties and therefore recommend that they not be used in practice. We also suggest alternative CIs with guaranteed coverage properties under easily interpretable restrictions on the conditional expectation function.

Keywords: discrete running variable; regression discontinuity design; clustered standard errors (search for similar items in EconPapers)
JEL-codes: C13 C14 C21 C25 (search for similar items in EconPapers)
Pages: 41 pages
Date: 2016-06
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (14)

Published - published in: American Economic Review, 2018, 108 (8), 2277 - 2304

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Journal Article: Inference in Regression Discontinuity Designs with a Discrete Running Variable (2018)
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