Inference in Regression Discontinuity Designs with a Discrete Running Variable

• 成果类型: Article
• 署名作者: Kolesar, Michal; Rothe, Christoph
• 署名单位: Princeton University; Princeton University; University of Mannheim
• 刊物名称: AMERICAN ECONOMIC REVIEW
• ISSN/ISSBN: 0002-8282
• DOI: 10.1257/aer.20160945
• 发表日期: 2018
• 页码: 2277-2304
• 关键词: education

摘要

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.