Inference in Regression Discontinuity Designs with a Discrete Running Variable
- American Economic Review (Forthcoming)
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.
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