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Identification and Estimation of Large Network Games with Private Link Information

Hulya Eraslan and Xun Tang
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Hulya Eraslan: Rice University
Xun Tang: Rice University

Working Papers from Rice University, Department of Economics

Abstract: 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.

Date: 2017-12
New Economics Papers: this item is included in nep-gth and nep-net
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