2018
DOI: 10.1257/aer.20160945
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Inference in Regression Discontinuity Designs with a Discrete Running Variable

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 th… Show more

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Cited by 251 publications
(224 citation statements)
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“…βRDDAM compares pupils with age at school entry of either 76 or 77 months. BSD and BME CI refer to the bounded second derivative and bounded misspecification error “honest” confidence intervals as discussed in Kolesar and Rothe (). BSD and BME normalized standard errors also reported.…”
Section: Resultsmentioning
confidence: 99%
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“…βRDDAM compares pupils with age at school entry of either 76 or 77 months. BSD and BME CI refer to the bounded second derivative and bounded misspecification error “honest” confidence intervals as discussed in Kolesar and Rothe (). BSD and BME normalized standard errors also reported.…”
Section: Resultsmentioning
confidence: 99%
“…In addition, as a robustness check, we also report bounded misspecification error (BME) honest CIs. These are based on the assumption that the specification bias at zero is no worse at the cutoff than away from it (see Section 5.2 of Kolesar and Rothe ). Although this type of CI is specifically designed for discrete running variables, it tends to be quite conservative and, in practice, uninformative if there is substantial uncertainty about the magnitude of the specification errors.…”
Section: Resultsmentioning
confidence: 99%
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