Identification and Estimation of Large Network Games with Private Link Information

We study the identification and estimation of large network games where each individual holds private information about its links and payoffs. Extending Galeotti, Goyal, Jackson, Vega-Redondo and Yariv (2010), we build a tractable empirical model of network games where the individuals are heterogenous with private link and payoff information, and characterize its unique, symmetric pure-strategy Bayesian Nash equilibrium. We then show that the parameters in individual payoffs are identified under "large market" asymptotics, whereby the number of individuals increases to infinity in a fixed and small number of networks. We also propose a consistent two-step m-estimator for individual payoffs. Our method is distribution-free in that it does not require parametrization of the distribution of shocks in individual payoffs. Monte Carlo simulation show that our estimator has good performance in moderate-sized samples.