网络中的测量误差与同伴效应
Measurement Error and Peer Effects in Networks
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中文总结 AI 辅助
本文针对网络中测量误差与同伴效应的问题，利用网络结构实现识别，提出一致GMM和2SLS估计量，并通过蒙特卡洛模拟验证结果。
中文摘要 AI 辅助
在许多实际应用中，仅能获取真实回归元的含噪代理变量，通常认为这会导致衰减偏差。但在均值线性模型中，估计的同伴效应可能被高估，进而可能导致假阳性结果。本文表明，渐近偏差取决于个体特征与网络连接的相互作用，并论证了网络结构如何无需额外外部信息即可助力识别。基于这些识别结果，本文提出了易于实现的一致GMM和2SLS估计量，通过蒙特卡洛模拟对结果进行了验证。
英文摘要
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.
