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Social Networks with Mismeasured Links

Author

Listed:
  • Arthur Lewbel

    (Boston College)

  • Xi Qu

    (Shanghai Jiao Tong University)

  • Xun Tang

    (Rice University)

Abstract

We consider estimation of peer effects in social network models where some network links are incorrectly measured. We show that if the number of mismeasured links does not grow too quickly with the sample size, then standard instrumental variables estimators that ignore the measurement error remain consistent, and standard asymptotic inference methods remain valid. These results hold even when measurement errors in the links are correlated with regressors, or with the model errors. Monte Carlo simulations and real data experiments confirm our results in finite samples. These findings imply that researchers can ignore small amounts of measurement errors in networks.

Suggested Citation

  • Arthur Lewbel & Xi Qu & Xun Tang, 2021. "Social Networks with Mismeasured Links," Boston College Working Papers in Economics 1031, Boston College Department of Economics.
    • Handle: RePEc:boc:bocoec:1031
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    References listed on IDEAS

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    Cited by:

    1. Yong Cai, 2022. "Linear Regression with Centrality Measures," Papers 2210.10024, arXiv.org.

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    More about this item

    Keywords

    Social networks; Peer e§ects; MisclassiÖed links; Missing links; Mismeasured network;
    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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