Journal of Econometrics· 2026Q1
Measurement error and peer effects in networks
- 0citations
- Q1SCImago
- 2026year
Short summary
Measurement error in network data can inflate, not attenuate, estimates of peer effects in linear-in-means models, with bias depending on network structure and individual characteristics.
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Key points
- Measurement error in network data can lead to inflated, not attenuated, estimates of peer effects in linear-in-means models.
- The asymptotic bias is influenced by the interplay between individual characteristics and network structure.
- Network structure itself can facilitate identification of true peer effects.
- Consistent GMM and 2SLS estimators are proposed to address this bias.
AI-generated from the title and abstract; the full text is not read.
Abstract
In many practical applications, only noisy proxies for the true regressors are available, which is commonly believed to induce an attenuation bias. In the linear-in-means model, however, estimated peer effects might be inflated, potentially leading to false positives. This paper shows that the asymptotic bias depends on the interplay between individual characteristics and network links and demonstrates how the network structure can facilitate identification without the need for additional external information. Based on these identification results, we present consistent GMM and 2SLS estimators that are easily implementable. Our results are illustrated by means of a Monte Carlo simulation.
The authors' abstract, as published at the source. Journal of Econometrics, 2026 · DOI ↗
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Computer Networks and CommunicationsComputer Science