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A weak law for moments of pairwise stable networks

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  • Leung, Michael P.

Abstract

This paper studies the dependence structure of sparse networks realized according to a strategic model of network formation with homophilous agents. We argue that equilibrium selection and chains of indirect dependence generated by strategic interactions are the main drivers of network dependence. Drawing on results in random-graph theory, we derive weak-dependence conditions under which a law of large numbers holds for a general class of network moments. A key condition restricts the strength of strategic interactions, which constitutes the network analog of conventional weak-dependence assumptions in time series and spatial econometrics that bound the magnitude of autoregressive parameters. We apply our result to estimating models of strategic network formation and treatment effects under network interference.

Suggested Citation

  • Leung, Michael P., 2019. "A weak law for moments of pairwise stable networks," Journal of Econometrics, Elsevier, vol. 210(2), pages 310-326.
    • Handle: RePEc:eee:econom:v:210:y:2019:i:2:p:310-326
    DOI: 10.1016/j.jeconom.2019.01.010
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    3. Bryan S. Graham, 2019. "Network Data," NBER Working Papers 26577, National Bureau of Economic Research, Inc.
    4. Ming Li & Zhentao Shi & Yapeng Zheng, 2024. "Estimation and Inference in Dyadic Network Formation Models with Nontransferable Utilities," Papers 2410.23852, arXiv.org.
    5. Shuyang Sheng, 2020. "A Structural Econometric Analysis of Network Formation Games Through Subnetworks," Econometrica, Econometric Society, vol. 88(5), pages 1829-1858, September.
    6. Kojevnikov, Denis & Song, Kyungchul, 2023. "Econometric inference on a large bayesian game with heterogeneous beliefs," Other publications TiSEM aca0631e-4f8a-45c7-af3a-4, Tilburg University, School of Economics and Management.
    7. Cristina Gualdani, 2021. "An Econometric Model of Network Formation with an Application to Board Interlocks between Firms," Post-Print hal-03548907, HAL.
    8. Michael P. Leung, 2022. "Dependence‐robust inference using resampled statistics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(2), pages 270-285, March.
    9. Yiran Chen & Hanming Fang, 2017. "Inferring the Ideological Affliations of Political Committees via Financial Contributions Networks," PIER Working Paper Archive 17-022, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 10 Dec 2017.
    10. Gualdani, Cristina, 2021. "An econometric model of network formation with an application to board interlocks between firms," Journal of Econometrics, Elsevier, vol. 224(2), pages 345-370.
    11. Gao, Wayne Yuan, 2020. "Nonparametric identification in index models of link formation," Journal of Econometrics, Elsevier, vol. 215(2), pages 399-413.
    12. Konrad Menzel, 2021. "Bootstrap With Cluster‐Dependence in Two or More Dimensions," Econometrica, Econometric Society, vol. 89(5), pages 2143-2188, September.
    13. Alejandro Sanchez-Becerra, 2022. "The Network Propensity Score: Spillovers, Homophily, and Selection into Treatment," Papers 2209.14391, arXiv.org.
    14. Boucher, Vincent, 2020. "Equilibrium homophily in networks," European Economic Review, Elsevier, vol. 123(C).
    15. Bryan S. Graham, 2019. "Network Data," CeMMAP working papers CWP71/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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    More about this item

    Keywords

    Social networks; Network formation; Multiple equilibria; Objective method;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C57 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Econometrics of Games and Auctions
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

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