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.

该奖项资助开发社会科学数据统计分析的新方法。 该项目的重点是“回归不连续”（RD）模型，这是在实践中非常广泛使用。 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模型被广泛用于分析数据，但模型的许多重要方法和统计特征尚未得到很好的理解。 本项目旨在开发新的RD模型分析方法，并将其应用于经济学和其他社会科学中的具体实证问题。本研究为RD设计的估计、推断和证伪开发了新的方法和实用工具。 这包括一个方法，占的事实，RD估计是敏感的带宽的选择，一种方法，是有效的，在有限的样本中，与多个截止点的RD分析和空间RD designs.broader的新工具的发展，这项研究的影响将来自于使用这些新的方法，以提供更好的洞察经济政策。