Collaborative Research: Econometric Methods for Models with Clustered Data and Covariate-Adaptive Randomization

协作研究：具有聚类数据和协变量自适应随机化模型的计量经济学方法

• 批准号: 1530534
• 负责人: Ivan Canay
• 金额: $ 23.7万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1530534&HistoricalAwards=false
• 关键词: Collaborative; Research; Econometric; Methods; Models

This award funds research that will develop new statistical theories and methods for analyzing three different kinds of data that are used to test economic theories. The first project considers how to analyze data when the individual data points can be grouped into a small number of "clusters". Since statistical methods begin with assumptions about randomization, the PI team will develop a theory of randomization tests that work in these settings. The second project considers problems associated with using randomized controlled methods to test the effects of an intervention, policy, or technology. These methods generally involve assigning people to a treatment or control group based on complicated rules that are designed to assure the two groups are comparable. These rules have implications for the best ways to analyze the resulting data, and the PIs plan to develop a new method that takes this into account. Finally, the team will consider how to best develop a method that can combine several separate statistical tests into a single framework. The project advances science by developing new methods that will be used to test theories from economics and other social sciences.The PIs pursue three different projects in econometrics. First, in joint work with Joseph Romano, they will develop a theory of randomization tests under an approximate symmetry assumption. A leading example where approximate symmetry holds is settings in which the data can be grouped into a small number of clusters. The hope is that their new methodology will work well in such settings, for which inference is known to be challenging. Second, in joint work with Federico Bugni, they will study how complicated treatment assignment rules designed to balance baseline covariates across treatment and control groups impacts inference about the average treatment effect. In preliminary work, they have shown that the usual two-sample t-test can be quite conservative in such settings. We plan to develop methods that do not have this feature. Finally, in joint work with Andres Santos, the PIs will develop methods for testing unions of functional moment inequalities. This framework encompasses many problems that have so far been treated separately in the econometrics literature, including (subvector) inference for (conditional) moment inequalities and tests of stochastic dominance.

该奖项资助的研究将开发新的统计理论和方法，用于分析用于测试经济理论的三种不同类型的数据。 第一个项目考虑当单个数据点可以被分组为少量的“集群”时如何分析数据。 由于统计方法开始时假设随机化，因此PI团队将开发在这些环境中有效的随机化检验理论。 第二个项目考虑与使用随机对照方法来测试干预措施，政策或技术的影响相关的问题。 这些方法通常涉及根据复杂的规则将人分配到治疗组或对照组，这些规则旨在确保两组具有可比性。 这些规则对分析结果数据的最佳方法有影响，PI计划开发一种考虑到这一点的新方法。 最后，该团队将考虑如何最好地开发一种方法，该方法可以将联合收割机几个独立的统计测试组合到一个框架中。 该项目通过开发新的方法来推进科学，这些方法将用于测试经济学和其他社会科学的理论。首先，在与约瑟夫·罗马诺的合作中，他们将在近似对称假设下开发一种随机化测试理论。近似对称性保持的一个主要示例是数据可以分组到少量集群中的设置。希望他们的新方法能在这样的环境中很好地工作，因为推理是众所周知的挑战。其次，在与Federico Bugni的联合工作中，他们将研究旨在平衡治疗组和对照组之间基线协变量的复杂治疗分配规则如何影响对平均治疗效果的推断。在初步工作中，他们已经表明，通常的双样本t检验在这种情况下可能相当保守。我们计划开发不具有此功能的方法。最后，在与安德烈斯桑托斯联合工作，PI将开发测试工会的功能矩不等式的方法。这个框架包含了许多问题，到目前为止已被单独处理的计量经济学文献，包括（子向量）推断（条件）矩不等式和测试的随机优势。