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Estimating local interactions among many agents who observe their neighbors

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  • Nathan Canen
  • Jacob Schwartz
  • Kyungchul Song

Abstract

In various economic environments, people observe other people with whom they strategically interact. We can model such information‐sharing relations as an information network, and the strategic interactions as a game on the network. When any two agents in the network are connected either directly or indirectly in a large network, empirical modeling using an equilibrium approach can be cumbersome, since the testable implications from an equilibrium generally involve all the players of the game, whereas a researcher's data set may contain only a fraction of these players in practice. This paper develops a tractable empirical model of linear interactions where each agent, after observing part of his neighbors' types, not knowing the full information network, uses best responses that are linear in his and other players' types that he observes, based on simple beliefs about the other players' strategies. We provide conditions on information networks and beliefs such that the best responses take an explicit form with multiple intuitive features. Furthermore, the best responses reveal how local payoff interdependence among agents is translated into local stochastic dependence of their actions, allowing the econometrician to perform asymptotic inference without having to observe all the players in the game or having to know the precise sampling process.

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  • Nathan Canen & Jacob Schwartz & Kyungchul Song, 2020. "Estimating local interactions among many agents who observe their neighbors," Quantitative Economics, Econometric Society, vol. 11(3), pages 917-956, July.
  • Handle: RePEc:wly:quante:v:11:y:2020:i:3:p:917-956
    DOI: 10.3982/QE923
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    Cited by:

    1. Li, Wei & Tan, Xu, 2021. "Cognitively-constrained learning from neighbors," Games and Economic Behavior, Elsevier, vol. 129(C), pages 32-54.
    2. Yingyao Hu & Zhongjian Lin, 2018. "Misclassification and the hidden silent rivalry," CeMMAP working papers CWP12/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Taehoon Kim & Jacob Schwartz & Kyungchul Song & Yoon-Jae Whang, 2019. "Monte Carlo Inference on Two-Sided Matching Models," Econometrics, MDPI, vol. 7(1), pages 1-15, March.
    4. Hulya Eraslan & Xun Tang, 2018. "Identification and Estimation of Large Network Games with Private Link Information," Koç University-TUSIAD Economic Research Forum Working Papers 1809, Koc University-TUSIAD Economic Research Forum.
    5. Nathan Canen & Ko Sugiura, 2022. "Inference in Linear Dyadic Data Models with Network Spillovers," Papers 2203.03497, arXiv.org, revised Jun 2023.

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