Discussion paper
DP19054 Measurement error and peer effects in networks
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.
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