Inference in Regression Discontinuity Designs with a Discrete Running Variable
By Michael J. Kolesar, Christoph Rothe. The American Economic Review, 2016
306 citations recorded by the source corpus.
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 C13, C51, J13, J31, J64, J65)
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/e1a4f35739d2f87afbb313cc8d064380715a6fef
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In Statistics and Probability
- Michael J. Kolesar
- T. Armstrong
- Fan Li
- Andrea Silvestrini
- Andrea Mercatanti
- T. Makinen
- D. Chetverikov
- Stefan Wager
Citations in the corpus
- Simple and honest confidence intervals in nonparametric regression (2016, methodology): “In this case, one can combine our method with the bandwidth snooping adjustment of Armstrong and Kolesár (2018b).”
- Regression Discontinuity Designs (2021, unknown): “The analogy between RD designs and randomized experiments took greater force after the work of Lee (2008), who advocated for interpreting the RD design as inducing “as good as randomized” (p.”
- A regression discontinuity design for ordinal running variables: Evaluating central bank purchases of corporate bonds (2019, background): “However, in many empirical applications, assignment to treatment is determined by covariates which are inherently discrete or only take on a limited number of values (Lee and Card, 2008; Kolesár and Rothe, 2017).”