Inference in Regression Discontinuity Designs with a Discrete Running Variable

Michal Kolesár and Christoph Rothe

American Economic Review, 2018, vol. 108, issue 8, 2277-2304

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 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-codes: C13 C51 J13 J31 J64 J65 (search for similar items in EconPapers)
Date: 2018
Note: DOI: 10.1257/aer.20160945
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Citations: View citations in EconPapers (183)

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Working Paper: Inference in Regression Discontinuity Designs with a Discrete Running Variable (2016)
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