Econometric models for networks and matching with heterogeneous agents

该奖项资助的研究将开发新的统计方法，用于分析网络环境中人类行为的数据。 一群人可以通过明确描述个体之间的联系来描述为一个社会网络。 例如，你的社交网络包括你的家人，你的邻居，同事等，以及更多的间接联系，如你通过你的孩子与他们的朋友和老师等的联系。人们当然会对他们的社交网络做出决定;你可能会结交新朋友或花更少的时间与邻居在一起。 最近的经济学研究集中在将网络关系的决策建模为战略决策;网络中的每个人都形成、维持或切断关系，以实现自己的目标。 PI将开发新的方法来测试网络使用统计方法的影响的假设。他还计划开发新方法，用于分析联合生产情况下的数据，即多人共同努力实现一个目标。 研究结果将推动网络科学领域的发展，并将促进国家的繁荣，因为企业越来越多地通过网络效应的透镜来观察客户关系。PI将制定动态网络形成的经验模型，该模型将（i）具有随机效用最大化基础（ii）使未观察到的代理异质性的分布灵活，以及（iii）使生成初始网络结构的过程未被指定。 关于联合生产的项目超越了以前关于在观察到的匹配满足强有力的随机分配型假设的情况下分析匹配的计量经济学方法的工作。 这里资助的新研究将开发在随机分配不成立的情况下恢复匹配输出的方法。 该项目将有助于我们了解半参数模型的有效估计。 所产生的软件和数据将在网上免费提供。

This award funds research that will develop new statistical methods for analyzing data about human behavior in network contexts. A group of people can be described as a social network by explicit description of the links between individuals. For example, your social network includes your family, your neighbors, co-workers, etc, as well as more indirect links such as your link through your children to their friends and teachers, etc. People of course make decisions about their social networks; you may make new friends or spend less time with a neighbor. Recent work in economics has focused on modeling those decisions about network relationships as strategic; each individual in the network forms, maintains, or severs relationships to meet his/her own goals. The PI will develop new methods for testing hypotheses about the effects of networks using statistical methods. He also plans to develop new methods that can be used to analyze data from joint production situations, where multiple people work together to achieve a goal. The results will advance the growing field of network science and will promote the national prosperity because businesses are increasing viewing customer relationships through the lens of network effects.The PI will formulate an empirical model of dynamic network formation that will (i) have a random utility maximization foundation (ii) leave the distribution of unobserved agent heterogeneity flexible, and (iii) leave the process generating the initial network structure unspecified. The project on joint production goes beyond previous work on the econometric methods for the analysis of matchings in situations where the observed matching satisfies strong, random-assignment type assumptions. The new research funded here will develop methods for recovering match output under settings where random assignment does not hold. The project will contribute to our understanding of efficient estimation of semi-parametric models. Software and data produced will be made freely available online.