Inference in Regression Discontinuity Designs with a Discrete Running Variable
American Economic Review
vol. 108,
no. 8, August 2018
(pp. 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.Citation
Kolesár, Michal, and Christoph Rothe. 2018. "Inference in Regression Discontinuity Designs with a Discrete Running Variable." American Economic Review, 108 (8): 2277-2304. DOI: 10.1257/aer.20160945Additional Materials
JEL Classification
- C13 Estimation: General
- C51 Model Construction and Estimation
- J13 Fertility; Family Planning; Child Care; Children; Youth
- J31 Wage Level and Structure; Wage Differentials
- J64 Unemployment: Models, Duration, Incidence, and Job Search
- J65 Unemployment Insurance; Severance Pay; Plant Closings