Function Space Trend Determination using Machine Learning

使用机器学习确定函数空间趋势

• 批准号: 1850860
• 负责人: Peter Phillips
• 金额: $ 24.9万
• 依托单位: Yale 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 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1850860&HistoricalAwards=false
• 关键词: Function; Space; Trend; Determination; using

Trends are found everywhere in socio-economic discourse about economic growth, inequality, welfare, and economic disparities across regions and nations. They play an important role in much economic theory and other areas of scientific research. Methods of trend determination have been intensively studied in economics for a long time, yet economists have little guidance on the source and nature of trend behavior and therefore rely on a limited class of models to use in applications. This research program will explore a new approach to studying trends that involves modern machine learning methods. The goal is to model trends when there is little practical or theoretical guidance about the nature of the trending behavior. Methods will be developed to learn about trends in a general function space environment that will enable wide application to study socio-economic phenomena. These methods will be useful in other scientific fields, such as climate change, where trends figure prominently. The project will develop and analyze an easy-to-implement machine learning procedure that enhances the properties of existing methods of smoothing and filtering data. The central idea is to iterate such filters in a controlled manner to make them a smarter smoothing device for trend determination. This approach involves methods of machine learning based on boosting and the project will develop the necessary theory to justify their application to non-stationary data that manifest trend behavior. In particular, the project will develop a large sample limit theory for boosted filters that will reveal its enhanced capabilities, focusing on the complex task of analyzing properties of the boosting methodology in the context of non-stationary data characterized by deterministic and stochastic trends allowing also for possible structural breaks. The resulting theory will be applicable to a wide class of underlying processes, thereby facilitating informed use of such machine learning devices in practical work in economics and other disciplines where trend determination is needed.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.

在关于经济增长、不平等、福利和各地区和国家间经济差距的社会经济讨论中，趋势随处可见。它们在许多经济理论和其他科学研究领域中发挥着重要作用。长期以来，经济学对趋势确定方法进行了深入研究，但经济学家对趋势行为的来源和性质几乎没有指导，因此在应用中依赖于有限的一类模型。该研究计划将探索一种新的方法来研究涉及现代机器学习方法的趋势。我们的目标是在几乎没有关于趋势行为性质的实践或理论指导的情况下对趋势进行建模。将制定各种方法，以了解一般功能空间环境中的趋势，从而能够广泛应用于研究社会经济现象。这些方法将在其他科学领域，如气候变化，趋势的数字突出有用。该项目将开发和分析一种易于实现的机器学习程序，增强现有平滑和过滤数据方法的性能。其中心思想是以受控的方式对这些滤波器进行优化，使其成为用于趋势确定的更智能的平滑设备。这种方法涉及基于boosting的机器学习方法，该项目将开发必要的理论，以证明其应用于表现趋势行为的非平稳数据的合理性。特别是，该项目将开发一个大样本极限理论的提升过滤器，将揭示其增强的能力，重点是在非平稳数据的背景下分析的提升方法的属性的复杂任务，其特征是确定性和随机趋势，也允许可能的结构性断裂。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。