Statistical Theory for Economic Models of Network Formation

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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.