Inference in Regression Discontinuity Designs with a Discrete Running Variable

Michal Kolesár; Christoph Rothe

doi:10.1257/aer.20160945

American Economic Review Vol. 108 No. 8 2018

Inference in Regression Discontinuity Designs with a Discrete Running Variable

Michal Kolesár¹; Christoph Rothe²

¹ Woodrow Wilson School and Department of Economics, Julis Romo Rabinowitz Building, Princeton University, Princeton, NJ 08540 (email: ) · ² Department of Economics, University of Mannheim, L7 3-5, D-68161 Mannheim, Germany (email: )

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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.

DOI: 10.1257/aer.20160945
Volume: 108
Issue: 8
Pages: 2277-2304
Language: en
Sources: bibtex:phds-export.bib openalex crossref

Inference in Regression Discontinuity Designs with a Discrete Running Variable

Abstract

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