Collaborative Research: New Techniques for High-Dimensional and Incomplete Network Data

协作研究：高维不完整网络数据新技术

• 批准号: 2149422
• 负责人: Eric Auerbach
• 金额: $ 29.12万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2149422&HistoricalAwards=false
• 关键词: Collaborative; Research; New; Techniques; Dimensional

This research project will develop new statistical and econometric resources for researchers working with social network data. Social networks often play a central role in shaping the outcomes and decisions of economic agents. For example, they can predict the diffusion of a new technology or spread of a contagion, determine the influence of key agents in crime or research production, or account for treatment spillovers in many social programs. However, incorporating social network data into empirical work can be difficult. Networks are fundamentally high-dimensional objects, and often it is unclear which features will be relevant for a particular social or economic phenomenon of interest. In addition, the researcher may only observe some of the relevant connections between agents in the social network for reasons of privacy or cost of data collection. This project will develop new techniques for incorporating social network structure into econometric and statistical modeling that accommodates these limitations. The project will train graduate students and develop a new course on the use of network data in causal inference. Software also will be developed and disseminated.This research project will build new econometric methodology for models with high-dimensional and incomplete social network data. First, the project will develop a new nonparametric regression framework for networks, characterize its statistical properties, demonstrate its use for empirical work in the social sciences, and provide software for implementation. Second, the project will investigate the informational content of partial network data generated by referral-based sampling and contingency-table data. The project will develop new theory, methodology, and empirical practices for identification, estimation, and inference using these nonstandard data types. New insights will be provided into exactly what features of social network structure are relevant for economic outcomes and decision making and when and how this information can be recovered from incomplete data in practice. The results of this research will help empiricists better understand the role that networks play in economics and the other social sciences.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.

该研究项目将为使用社交网络数据的研究人员开发新的统计和计量经济学资源。社交网络往往在塑造经济主体的结果和决策方面发挥核心作用。例如，它们可以预测新技术的传播或传染病的传播，确定犯罪或研究生产中关键代理人的影响，或解释许多社会项目中的治疗溢出效应。然而，将社交网络数据纳入实证工作可能很困难。网络基本上是高维对象，通常不清楚哪些特征与特定的社会或经济现象相关。此外，出于隐私或数据收集成本的原因，研究人员可能只观察社交网络中代理之间的一些相关联系。该项目将开发新的技术，将社会网络结构纳入计量经济学和统计建模，以适应这些限制。该项目将培训研究生，并开发一门关于在因果推理中使用网络数据的新课程。该研究项目将为高维和不完整的社会网络数据的模型建立新的计量经济学方法。首先，该项目将为网络开发一个新的非参数回归框架，描述其统计特性，展示其在社会科学实证工作中的用途，并提供实施软件。第二，本项目将调查基于抽样的部分网络数据和列联表数据的信息内容。该项目将开发新的理论，方法和经验实践，用于使用这些非标准数据类型进行识别，估计和推理。社会网络结构的哪些特征与经济结果和决策相关，以及在实践中何时以及如何从不完整的数据中恢复这些信息，将提供新的见解。这项研究的结果将帮助经济学家更好地理解网络在经济学和其他社会科学中的作用。该奖项反映了NSF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。