A Structural Model of Dense Network Formation

Abstract

This paper proposes an empirical model of network formation, combining strategic and random networks features. Payoffs depend on direct links, but also link externalities. Players meet sequentially at random, myopically updating their links. Under mild assumptions, the network formation process is a potential game and converges to an exponential random graph model (ERGM), generating directed dense networks. I provide new identification results for ERGMs in large networks: if link externalities are non-negative, the ERGM is asymptotically indistinguishable from an Erdos-Renyi model with independent links. We can identify the parameters only when at least one of the externalities is negative and sufficiently large.

However, the standard estimation methods for ERGMs can have exponentially slow convergence, even when the model has asymptotically independent links. I thus estimate parameters using a Bayesian MCMC method. When the parameters are identifiable, I show evidence that the estimation algorithm converges in almost quadratic time.

Supplemental material can be found at: https://ssrn.com/abstract=2897576

Online Appendix can be found at: https://ssrn.com/abstract=2897579

Keywords: Network Formation, ERGM, Large networks, Bayesian Estimation, Potential Games

JEL Classification: D85, C15, C73