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Identification and Estimation of Large Network Games with Private Link Information

Author

Listed:
  • Eraslan, Hulya

    (Rice University)

  • Tang, Xun

    (Rice University)

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.

Suggested Citation

  • Eraslan, Hulya & Tang, Xun, 2017. "Identification and Estimation of Large Network Games with Private Link Information," Working Papers 17-002, Rice University, Department of Economics.
    • Handle: RePEc:ecl:riceco:17-002
    as

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    References listed on IDEAS

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    1. Áureo de Paula & Seth Richards-Shubik & Elie Tamer, 2015. "Identification of preferences in network formation games," CeMMAP working papers CWP29/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Nicholas Christakis & James Fowler & Guido Imbens & Karthik Kalyanaraman, 2010. "An empirical model for strategic network formation," CeMMAP working papers CWP16/10, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Abhijit Banerjee & Arun G Chandrasekhar & Esther Duflo & Mathew O. Jackson, 2014. "Gossip: Identifying Central Individuals in a Social Network," Working Papers id:5925, eSocialSciences.
    4. Anton Badev, 2014. "Discrete Games in Endogenous Networks: Theory and Policy," 2014 Meeting Papers 901, Society for Economic Dynamics.
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    Cited by:

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    3. Michael P. Leung, 2019. "Inference in Models of Discrete Choice with Social Interactions Using Network Data," Papers 1911.07106, arXiv.org.

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