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.

经济和社会数据通常会随着时间的流逝而收集。 有时可以调整数据收集方法以响应早期数据收集期间所学的经验教训。 在这些情况下，研究人员可能需要估计政策效应，检验假设或随着新数据的可用性而动态调整实验设计。 估计方法将需要随着数据的变化而进行调整。 当前没有有效的方法可以从这种顺序的方式中从收集的数据中绘制推断。 该项目将开发新的创新方法来分析此类顺序统计问题。 该项目将设计易于通过数学解决的方法，并允许研究人员正确评估动态收集的数据并最大程度地提高数据用于得出策略结论的效率。 这些方法将在经济学，生物统计学，医学和其他社会科学领域有用。 这项研究的结果将改善政策评估方法，因此改善了美国经济和治理的功能。该项目将开发新的方法来分析动态环境中的统计决策问题。我们将扩展实验框架的限制，以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放在连续设置上，其中可用的分析师的信息已固定或外源于收集的数据。 该项目的第二部分将包括设置，其中的顺序收集数据会动态发展，以反映从数据的早期部分获得的信息。 对于这些设置中的每一种，将两个关键的研究输出是：（i）新信息适应的渐近表示定理； （ii）一个新的渐近最优框架和发现。 这项研究的结果将改善政策评估方法，从而改善美国经济和治理的功能。该奖项反映了NSF的法定使命，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准来通过评估来支持的。