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

该奖项资助的研究将开发新的统计理论和方法，用于分析用于检验经济理论的三种不同类型的数据。第一个项目考虑如何在单个数据点可以被分组到少量的“集群”中时分析数据。由于统计学方法始于关于随机化的假设，PI团队将开发一种在这些设置下工作的随机化测试理论。第二个项目考虑与使用随机控制方法来测试干预、政策或技术的效果相关的问题。这些方法通常涉及根据复杂的规则将受试者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，团队将考虑如何最好地开发一种方法，将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动科学的发展，这些方法将用于检验经济学和其他社会科学的理论。个人投资经理在计量经济学领域从事三个不同的项目。首先，在与约瑟夫·罗曼诺的合作中，他们将发展一种在近似对称假设下的随机测试理论。近似对称的一个主要例子是可以将数据分组到少数几个集群中的设置。人们希望，他们的新方法将在这种情况下很好地发挥作用，对这种情况进行推断是众所周知的具有挑战性的。其次，在与Federico Bugni的联合工作中，他们将研究复杂的治疗分配规则如何影响对平均治疗效果的推断，这些规则旨在平衡治疗组和对照组之间的基线协变量。在初步工作中，他们已经表明，在这种情况下，通常的两样本t检验可能相当保守。我们计划开发不具有此功能的方法。最后，在与安德烈斯·桑托斯的联合工作中，PI将开发测试函数矩不等并的方法。这个框架包含了许多迄今为止在计量经济学文献中被分开处理的问题，包括(条件)矩不等的(子矢量)推断和随机优势的检验。

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.