Inference in Regression Discontinuity Designs with a Discrete Running Variable

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...

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Vydáno v:The American economic review Ročník 108; číslo 8; s. 2277 - 2304
Hlavní autoři: KolesÁr, Michal, Rothe, Christoph
Médium: Journal Article
Jazyk:angličtina
Vydáno: Nashville American Economic Association 01.08.2018
Témata:
Confidence intervals
Discontinuity
Economic models
Errors
Inference
Property
Regression analysis
Simulation
ISSN:0002-8282, 1944-7981
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:0002-8282
1944-7981
DOI:10.1257/aer.20160945