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为当前计量经济学感兴趣的两个领域开发新的程序和最优结果：部分确定参数的估计和推断；和最佳治疗分配规则。部分确定的模型最近在经济学中受到了相当多的关注。在部分确定的统计经济模型中，即使使用理想化的数据集，也不能完美地恢复所有感兴趣的数量，但我们可以获得感兴趣的数量的界限。尽管此类模型可以通过放宽辅助假设来提高实证分析的稳健性，但从统计角度来看，它们是不标准的。通过使用渐近统计决策理论的工具来分析这些模型，我们可以获得对统计过程性质的严格限制，简单地比较替代过程，并获得最优性结果。这项研究的结果将为经济学家提供新的工具以及选择最佳工具和进行边界分析的方法。该项目的第二部分将开发治疗和政策分析的决策理论方法。在此部分中，PI 考虑最佳治疗分配问题。社会科学和医学科学中治疗评估的一个主要目标是为如何分配个人接受治疗提供指导。例如，许多研究都研究了对个人进行分析的问题，以确定那些可能从社会计划中受益的人。这些实证研究通常侧重于估计或推断治疗效果的大小。这项研究采用决策理论方法，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 展示了如何使用这种方法来开发在各种二元静态病例中进行治疗分配的最佳程序。在他们的研究计划的下一阶段，PI 将把我们的分析扩大到许多与实际相关的情况：多值或连续治疗的设置；动态治疗分配问题，可以根据中间结果依次做出决策。更广泛的影响：具有部分识别性的模型出现在整个社会和生命科学领域。这项研究将为其他社会科学、调查分析、生物统计学等领域的研究人员提供估计和推理工具。处理分配问题和相关的动态规划问题也有广泛的应用。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的数据提出最佳治疗和政策建议的程序。