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Bayesian Empirical Likelihood: Data Analysis Tools with Applications in Econometrics

贝叶斯经验似然：数据分析工具在计量经济学中的应用

基本信息

批准号：
1921523
负责人：
Mario Peruggia
金额：
$ 54万
依托单位：
Ohio State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1921523&HistoricalAwards=false
关键词：
Bayesian Empirical Likelihood Data Analysis

项目摘要

This research project will develop a cohesive set of Bayesian data analysis tools for non-generative models. A generative statistical model provides a complete technical description of a phenomenon. Such a model is detailed enough that one can generate a full data set from it, including observations at the individual level as well as the population level. In essence, the generative model provides access to an artificial world. These models are prevalent in the physical sciences where there is the possibility of having a full and complete technical description of the world. In contrast, many applied fields make use of non-generative models. This type of model is based on theory that describes the key features of a phenomenon while leaving minor features unspecified. These models have proven their worth in a variety of fields, including econometrics, the main area of application considered in this project. The project will focus on Bayesian methods that have traditionally relied on generative statistical models. The Bayesian paradigm provides a rich environment for the development of data-analytic techniques for identification of deficiencies in models and remediation of the effects of shortcomings of the data. The project will develop a full suite of analogous Bayesian inferences and diagnostics for non-generative models and will implement them in substantive empirical contexts from econometrics. The project involves international and multidisciplinary collaboration between the three investigators with direct opportunities for their students. Often working with students from underrepresented groups in STEM fields, including women and minorities, the investigators will engage in cross-mentoring to deepen the students' views of both statistics and econometrics and to provide them with insight into the strengths and weaknesses of the educational systems in the US and Australia.This research project will take techniques developed for data analysis with generative models and adapt them for use with non-generative models specified by a set of (generalized) moment constraints. Within this context, empirical likelihood enables a form of likelihood-driven inference based on an empirically derived likelihood function satisfying the moment constraints. As many existing data analysis techniques are likelihood based, the project will consider empirical likelihood versions of these models. The eventual goal is to improve moment-based model data analysis by expanding the toolkit for the moment-based modeler. The researchers will: 1) Develop a suite of case influence diagnostics within the Bayesian empirical likelihood context and investigate the theoretical and empirical properties of these diagnostics; 2) Develop Bayesian empirical likelihood methods for hypothesis testing, model comparison, and model averaging, with attention to formulation of the null hypothesis; and 3) Apply the tools developed under points 1 and 2 to a range of econometric applications; for example, to the modeling of asset prices and short-term interest rates.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

本研究项目将为非生成模型开发一套内聚的贝叶斯数据分析工具。生成统计模型提供了对现象的完整技术描述。这样的模型足够详细，人们可以从中生成完整的数据集，包括个人水平和总体水平的观察结果。本质上，生成模型提供了进入人工世界的途径。这些模型在物理科学中很流行，因为物理科学有可能对世界进行全面而完整的技术描述。相比之下，许多应用领域使用非生成模型。这种类型的模型是建立在理论的基础上的，它描述了一种现象的关键特征，而不指明次要特征。这些模型已经在许多领域证明了它们的价值，包括计量经济学，这是本项目考虑的主要应用领域。该项目将重点关注传统上依赖于生成统计模型的贝叶斯方法。贝叶斯范式为数据分析技术的发展提供了一个丰富的环境，用于识别模型中的缺陷和纠正数据缺陷的影响。该项目将为非生成模型开发一整套类似的贝叶斯推断和诊断，并将在计量经济学的实质性经验背景下实施它们。该项目涉及三位研究者之间的国际和多学科合作，并为他们的学生提供直接机会。研究人员经常与STEM领域中代表性不足的群体（包括女性和少数族裔）的学生合作，他们将进行交叉指导，以加深学生对统计学和计量经济学的看法，并为他们提供对美国和澳大利亚教育系统优缺点的洞察。该研究项目将采用为生成模型开发的数据分析技术，并将其用于由一组（广义）力矩约束指定的非生成模型。在这种情况下，经验似然使一种基于满足矩约束的经验推导似然函数的似然驱动推断成为可能。由于许多现有的数据分析技术是基于似然的，该项目将考虑这些模型的经验似然版本。最终的目标是通过扩展基于矩的建模器的工具包来改进基于矩的模型数据分析。研究人员将：1)在贝叶斯经验似然背景下开发一套病例影响诊断方法，并研究这些诊断方法的理论和经验性质；2)发展贝叶斯经验似然方法，用于假设检验、模型比较和模型平均，并注意零假设的制定；3)将第1点和第2点开发的工具应用于一系列计量经济学应用；例如，对资产价格和短期利率的建模。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。