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: 0962422
• 负责人: Jack Porter
• 金额: $ 22.5万
• 依托单位: University of Wisconsin-Madison
• 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=0962422&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 our project will develop decision-theoretic approaches to treatment and policy analysis. In this project, we 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. Our research takes a decision-theoretic approach, which connects the statistical analysis of the data to a formal policy decision. In recent work, we show 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 our work, we 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. Our 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. Our 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.

这个项目将使用渐近统计决策理论来开发新的程序和最优性结果的两个领域目前感兴趣的计量经济学：估计和推断部分确定的参数;和最佳治疗分配规则。部分识别模型最近在经济学中受到了相当大的关注。在部分识别的统计经济模型中，即使使用理想化的数据集，也不是所有感兴趣的量都可以完全恢复，但是可以获得感兴趣的量的界限。虽然这些模型可以通过放松辅助假设来增加实证分析的稳健性，但从统计学的角度来看，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，我们可以得到对统计过程性质的严格限制，简单地比较备选过程，并得到最优性结果。本研究的结果将为经济学家提供新的工具，以及选择最佳工具的方法，以进行边界分析。我们项目的第二部分将发展治疗和政策分析的决策理论方法。在这个项目中，我们考虑最优治疗分配问题。在社会和医学科学中，治疗评估的一个主要目标是为如何分配个人治疗提供指导。例如，一些研究已经研究了对个人进行分析以确定那些可能从社会计划中受益的人的问题。这些实证研究通常侧重于估计或推断治疗效果的大小。我们的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，我们展示了如何使用这样的方法来开发最佳的程序，在广泛的二元，静态的情况下，治疗分配。在我们工作的下一阶段，我们将扩大我们的分析，以实际相关的一些情况：设置与多值或连续的治疗;和动态治疗分配问题，其中可以作出决定，以响应中间outcome.Broader影响：模型与部分识别出现在整个社会和生命科学。 我们的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规划问题也有广泛的应用。我们的研究将为医学，生物统计学和许多其他领域的研究人员提供程序，以便根据过去的数据最佳地制定治疗和政策建议。