Inference in Regression Discontinuity Designs with a Discrete Running Variable
研究了运行变量只有有限个不同取值时断点回归设计的推断问题，发现基于聚类标准误的置信区间不能有效应对模型误设，并提出了两种有保障覆盖性质的新方法。
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.