Collaborative Research: Applications of Asymptotic Statistical Decision Theory in Econometrics

协作研究：渐近统计决策理论在计量经济学中的应用

• 批准号: 0962488
• 负责人: Keisuke Hirano
• 金额: $ 21.27万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0962488&HistoricalAwards=false
• 关键词: Collaborative; Research; Applications; Asymptotic; Statistical

This project will use asymptotic statistical decision theory to develop new procedures and optimality results for two areas of current interest in econometrics: estimation and inference for partially identified parameters; and optimal treatment assignment rules. Partially identified models have received considerable recent attention in economics. In partially identified statistical economic models, not all quantities of interest can be perfectly recovered even with an idealized data set, but one can obtain bounds on the quantities of interest. Although such models can increase the robustness of empirical analysis by relaxing auxiliary assumptions, they are nonstandard from a statistical viewpoint. By using tools from asymptotic statistical decision theory to analyze these models, we can obtain sharp restrictions on the properties of statistical procedures, compare alternative procedures simply, and obtain optimality results. The results of this research will provide economists with new tools, and methods for selecting the best tools, for conducting bounds analyses. The second component of this project will develop decision-theoretic approaches to treatment and policy analysis. In this component, the PIs consider optimal treatment assignment problems. A major goal of treatment evaluation in the social and medical sciences is to provide guidance on how to assign individuals to treatments. For example, a number of studies have examined the problem of profiling individuals to identify those likely to benefit from a social program. These empirical studies typically focus on estimation, or inference on the size of the treatment effect. This research takes a decision-theoretic approach, which connects the statistical analysis of the data to a formal policy decision. In recent work, the PIs have shown how such an approach can be used to develop optimal procedures for treatment assignment in a wide range of binary, static cases. In the next phase of their research program, the PIs will broaden our analysis to a number of situations of practical relevance: settings with multi-valued or continuous treatments; and dynamic treatment assignment problems, where decisions can be made sequentially in response to intermediate outcomes. Broader Impact: Models with partial identification arise throughout the social and life sciences. This research will provide estimation and inference tools for researchers in other social sciences, survey analysis, biostatistics, and other fields. Treatment assignment problems and related dynamic programming problems also have broad application. The research will provide researchers in medicine, biostatistics, and many other fields with procedures to make treatment and policy recommendations optimally in light of past data.

该项目将使用渐近统计决策理论来开发新程序和最佳结果，以针对计量经济学目前感兴趣的两个领域：部分确定的参数的估计和推断；和最佳治疗分配规则。部分确定的模型在经济学方面受到了广泛的关注。在部分识别的统计经济模型中，即使有理想的数据集，也不可以完美地恢复所有兴趣的量，但是人们可以在关注量的数量上获得界限。尽管这样的模型可以通过放松辅助假设来提高经验分析的鲁棒性，但从统计角度来看，它们是非标准的。通过使用渐近统计决策理论的工具来分析这些模型，我们可以对统计程序的性质进行急剧限制，简单比较替代程序，并获得最佳结果。这项研究的结果将为经济学家提供新工具，以及选择最佳工具的方法，用于进行界限分析。该项目的第二部分将开发决策理论方法进行治疗和政策分析。在此组件中，PI考虑最佳治疗分配问题。社会和医学科学评估评估的主要目标是提供有关如何将个人分配给治疗的指导。例如，许多研究检查了分析个人的问题，以确定可能从社会计划中受益的人。这些经验研究通常集中于估计或对治疗效果大小的推断。这项研究采用了决策理论方法，该方法将数据的统计分析与正式的政策决策联系起来。在最近的工作中，PI显示了如何使用这种方法来制定最佳的治疗分配程序，以在各种二进制静态病例中。在其研究计划的下一阶段，PI将我们的分析扩大到许多实际相关性的情况：具有多价或连续处理的环境；以及动态治疗分配问题，可以依次根据中间结果做出决策。更广泛的影响：在整个社会和生命科学中都会出现具有部分认同的模型。这项研究将为其他社会科学，调查分析，生物统计学和其他领域的研究人员提供估计和推理工具。治疗分配问题和相关的动态编程问题也具有广泛的应用。该研究将为医学，生物统计学和许多其他领域的研究人员提供根据过去数据最佳的治疗和政策建议的程序。