Statistical Theory for Economic Models of Network Formation

网络形成经济模型的统计理论

• 批准号: 1755100
• 负责人: Michael Leung
• 金额: $ 11.19万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-01 至 2020-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1755100&HistoricalAwards=false
• 关键词: Statistical; Theory; Economic; Models; Network

Social scientists have long theorized about the influence of networks on behavior, how they sustain cooperation, transmit information, and facilitate economic transactions. With the increasing availability of network data in a variety of social contexts, scientists now have the opportunity to subject theory to rigorous empirical testing and uncover new facts to improve understanding of social behavior and better design economic policies to advance the national welfare. Toward this end, this research develops new econometric tools for the analysis of network models, especially models of network formation. New tools are needed to account for the unique structure of network data and complexity of network models. The investigator studies econometric models of network formation with strategic interactions. These are latent-index models that allow links formed by other individuals to directly influence one's own propensity to form links. A main objective of this research is to prove a central limit theorem relevant for inference both in static models which use as data only a single snapshot of a large network, and dynamic models which exploit a short time series of a large network. The investigator also develops practical methods for inference. In studying the dependence structure of large networks, this research is also relevant for inference in models of social interactions when the network is endogenous.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.

长期以来，社会科学家一直在理论上探讨网络对行为的影响，以及网络如何维持合作、传递信息和促进经济交易。随着网络数据在各种社会背景下的可用性越来越高，科学家现在有机会对理论进行严格的实证检验，并发现新的事实，以提高对社会行为的理解，更好地设计经济政策，以促进国家福利。为此，本研究开发了新的计量经济学工具，用于分析网络模型，特别是网络形成模型。需要新的工具来解释网络数据的独特结构和网络模型的复杂性。研究者研究了具有战略互动的网络形成的计量经济学模型。这些是潜在指数模型，允许其他个体形成的链接直接影响自己形成链接的倾向。本研究的一个主要目的是证明一个中心极限定理相关的推理，在静态模型中使用的数据只有一个单一的快照的一个大型网络，和动态模型，利用一个短的时间序列的一个大型网络。调查员还开发实用的推理方法。在研究大型网络的依赖结构时，该研究也与网络内生时社会互动模型的推断相关。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。