New Methodological Developments for Inference in the Regression-Discontinuity Design

回归-不连续性设计中推理的新方法论发展

• 批准号: 1357561
• 负责人: Matias Cattaneo
• 金额: $ 27.68万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1357561&HistoricalAwards=false
• 关键词: New; Methodological; Developments; Inference; Regression

This award funds the development of new methods for the statistical analysis of social science data. The project focuses on 'regression discontinuity' (RD) models, which are very widely used in practice. This kind of statistical model is used to estimate the effect of a treatment on an outcome when unobserved factors may threaten the validity of the statistical analysis. While RD models are widely used to analyze data, many important methodological and statistical features of the models are not well understood. This project develops novel methods for the analysis of RD models and applies the results to concrete empirical problems in Economics and other social sciences.The research develops new methodological and practical tools for estimation, inference and falsification of RD designs. This includes a method that accounts for the fact that RD estimators are sensitive to the choice of bandwidth, a method that is valid in finite samples, the analysis of RDs with multiple cutoffs and the development of new tools for spatial RD designs.Broader impacts of this research will come from the use of these new methods to provide better insight for economic policy.

该奖项资助社会科学数据统计分析新方法的开发。 该项目重点关注在实践中广泛使用的“不连续回归”（RD）模型。 当未观察到的因素可能威胁统计分析的有效性时，这种统计模型用于估计治疗对结果的影响。 虽然 RD 模型广泛用于分析数据，但模型的许多重要方法论和统计特征尚未得到很好的理解。 该项目开发了分析 RD 模型的新方法，并将结果应用于经济学和其他社会科学中的具体实证问题。该研究为 RD 设计的估计、推理和证伪开发了新的方法论和实用工具。 这包括一种考虑到 RD 估计量对带宽选择敏感这一事实的方法、一种在有限样本中有效的方法、对具有多个截止值的 RD 进行分析以及开发用于空间 RD 设计的新工具。这项研究的更广泛影响将来自于使用这些新方法为经济政策提供更好的见解。