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Measurement error and peer effects in networks

Author

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  • Bramoullé, Yann
  • Maes, Sebastiaan

Abstract

In many practical applications, only noisy proxies for the true regressors are available, which is commonly believed to induce an attenuation bias in OLS estimates. In the linear-in-means model, however, the estimates for the peer effect might be inflated, potentially leading to false positives. This paper explores how this expansion bias depends on the structure of the underlying social network and demonstrates how this network structure can facilitate identification without the need for additional external information. Based on these identification results, we present consistent GMM and IV estimators that are easily implementable. Our results are illustrated by means of a Monte Carlo simulation.

Suggested Citation

  • Bramoullé, Yann & Maes, Sebastiaan, 2024. "Measurement error and peer effects in networks," CEPR Discussion Papers 19054, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:19054
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    More about this item

    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
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation

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