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date: 01 December 2020

Abstract and Keywords

This chapter surveys econometric network formation models. Its goal is to acquaint the readers, in a self-contained manner, with a number of network formation models used in the graph theory, statistics, sociology, and econometrics literature, with a view to how well they map to real-world data, the sorts of economic microfoundations they implicitly assume, and their econometric properties. A major difficulty in the study of network formation is that the researcher typically has a data set consisting of a single network observed in a single period. Key questions include whether a researcher can identify, and develop consistent estimators of, the parameters driving network formation when observing a single large network. Estimation involves a number of challenges, including but not limited to the degree of correlation between linking decisions, the ability to reproduce realistic patterns of network structure, concerns about multiple equilibria, and missing data.

Keywords: network formation, random graphs, heterogeneity, sparsity, clustering, subgraphs, large networks, consistency, asymptotic normality

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