AI 中文总结
研究回归断点设计中变量操纵问题，基于本福特定律检测结构失衡，消除影响结果的参数，深入分析密度行为，能找出偏差源头与隐藏操纵，引入创新带宽选择方法及构建互补测试，实证证明该框架有增强保护价值。
AI 中文摘要
本文探讨回归断点设计中运行变量操纵的问题。利用操纵常改变断点周围密度平衡这一观察结果，我们使用本福特定律检测这些结构失衡，该定律广泛应用于欺诈检测。我们的框架是传统麦克拉里型测试的重要预防保障。它消除了可能扭曲结果的研究者选定参数，同时对密度行为进行更深入的诊断分析。经典麦克拉里测试因刚性对称设置可能忽略系统性失衡，而我们的方法将数据分为方向分量，能精确找出偏差源头和隐藏操纵。为此，我们引入创新方法选择与本福特定律一致的带宽，构建两个不同的互补测试，通过改编自尼格里尼（2012）的阈值成功将该定律应用从数字转换为概率。实证应用证实了此诊断框架的增强保护价值。
英文摘要
This paper addresses the problem of running variable manipulation in Regression Discontinuity Designs. Leveraging the observation that manipulation often alters the density balance around the cutoff, we detect these structural imbalances using Benford's Law -a natural statistical regularity widely applied in fraud detection. Our framework serves as a vital precautionary safeguard alongside traditional McCrary-type tests. It eliminates researcher-chosen parameters that can skew outcomes, while delivering a deeper diagnostic breakdown of the density's behavior. Crucially, whereas the classic McCrary test can overlook systemic imbalances due to its rigid symmetric setup, our method separates the data into directional components. This allows researchers to pinpoint the exact origin of a deviation and spot hidden manipulation that standard frameworks fail to capture. To achieve this, we introduce an innovative method for selecting a bandwidth consistent with BL, and construct two distinct, complementary tests using threshold values adapted from Nigrini (2012) that successfully transition the law's application from digits to probabilities. Empirical applications confirm the enhanced protective value of this diagnostic framework.
Comments5 figures, 22 pages
