Collaborative Research: Asymptotic Approximations for Sequential Decision Problems in Econometrics

合作研究：计量经济学中序列决策问题的渐近逼近

• 批准号: 2117260
• 负责人: Keisuke Hirano
• 金额: $ 30.55万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2117260&HistoricalAwards=false
• 关键词: Collaborative; Research; Asymptotic; Approximations; Sequential

Economic and social data are often collected over time. The data collection method may sometimes be adjusted to respond to lessons learnt during the data collection in earlier periods. In these situations, researchers may need to estimate policy effects, test hypotheses, or adjust experimental designs dynamically as new data become available. The estimation method will need to adjust as the data changes. There are currently no efficient methods for drawing inference from data collected in such sequential manner. This project will develop new and innovative methods for analyzing such sequential statistical problems. The project will devise methods that are easy to solve mathematically and allow researchers to properly evaluate dynamically collected data and maximize the efficiency with which the data is used to draw policy conclusions. These methods will be useful in several areas of economics, biostatistics, medicine, and other social sciences. The results of this research will improve methods of policy evaluation, hence improve the functioning of the US economy and governance.This project will develop new methods for analyzing statistical decision problem in dynamic settings. We will extend the limits of experiments framework to incorporate the informational structure in various forms of sequential data collection. The first part of the research will focus on sequential settings where the information available to the analyst is fixed or set exogenously to the collected data. The second part of the project will include settings where sequential collection of data evolves dynamically to reflect information gained from earlier portions of the data. For each of these settings, two key research outputs will be: (i) new information-adapted asymptotic representation theorems; and (ii) a new asymptotic optimality framework and findings. The results of this research will improve methods of policy evaluation, hence improve the functioning of the US economy and governance.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.

经济和社会数据往往是随着时间的推移而收集的。 有时可能会调整数据收集方法，以反映在早期数据收集期间吸取的经验教训。 在这些情况下，研究人员可能需要估计政策效果，测试假设，或动态调整实验设计，因为新的数据变得可用。 估计方法将需要随着数据的变化而调整。 目前还没有有效的方法来从以这种顺序方式收集的数据中得出推论。 该项目将开发新的和创新的方法来分析这种连续的统计问题。 该项目将设计易于数学求解的方法，使研究人员能够正确评估动态收集的数据，并最大限度地提高数据用于得出政策结论的效率。 这些方法在经济学、生物统计学、医学和其他社会科学的几个领域都很有用。 本研究的成果将改进政策评估方法，从而改善美国经济和治理的功能。本项目将开发分析动态环境下统计决策问题的新方法。我们将扩展实验框架的限制，将信息结构纳入各种形式的顺序数据收集。 研究的第一部分将重点关注顺序设置，其中分析师可用的信息是固定的或对收集的数据进行外部设置。 该项目的第二部分将包括数据的顺序收集动态演变的设置，以反映从数据的早期部分获得的信息。 对于每一种设置，两个关键的研究成果将是：（i）新的信息适应渐近表示定理;（ii）新的渐近最优性框架和发现。 该研究成果将改善政策评估方法，从而改善美国经济和治理的运作。该奖项反映了NSF的法定使命，并被认为值得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来支持。