Inference in regression discontinuity designs with a discrete running variable

Michal Kolesár, Christoph Rothe

Research output: Contribution to journalReview articlepeer-review

134 Scopus citations


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).

Original languageEnglish (US)
Pages (from-to)2277-2304
Number of pages28
JournalAmerican Economic Review
Issue number8
StatePublished - Aug 2018

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics


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