A Structural Model of Segregation in Social Networks
65 Pages Posted: 19 Jul 2013
Date Written: July 16, 2013
The main challenges in estimating strategic network formation models are the presence of multiple equilibria, and the fact that the number of possible network configurations increases exponentially with the number of players. I propose a dynamic model of strategic network formation with heterogeneous players, which converges to a unique stationary equilibrium. Hence, the structural preference parameters can be estimated using a single network observation. In addition, the model provides the first equilibrium micro-foundation of exponential random graphs. Because of the curse of dimensionality, the likelihood is computationally intractable. Therefore, I propose a Bayesian estimation strategy that samples from the posterior, interleaving parameter and network simulations, without evaluating the likelihood. I prove that the proposed algorithm converges to the correct posterior distribution. A mean-field analysis shows that the algorithm converges fast for practical applications. Estimation is tested with artificial and Add Health data, confirming evidence of homophily in high schools.
Keywords: Social Networks, Bayesian Estimation, Markov Chain Monte Carlo
JEL Classification: D85, C15, C73
Suggested Citation: Suggested Citation
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