Ignoring measurement errors in social networks
Ignoring measurement errors in social networks
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忽略社交网络中的测量误差
DOI：
10.1093/ectj/utad028
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Tang, Xun
中科院分区：
文献类型：
--
作者：
Lewbel, Arthur;Qu, Xi;Tang, Xun
在一些网络链接被错误测量的社交网络模型中，我们考虑了节点效应估计。我们证明，如果错误测量的环节的数量或幅度没有随着样本量的增加而增长得太快，那么忽略这些测量误差的标准工具变量估计保持一致，并且标准渐近推断方法仍然有效。即使当链路测量误差与回归变量或模型中的结构误差相关时，这些结果仍然成立。模拟和真实数据实验在有限样本中证实了我们的结果。这些发现意味着，研究人员可以忽略网络中少量错误测量的链路。
We consider peer effect estimation in social network models where some network links are incorrectly measured. We show that if the number or magnitude of mismeasured links does not grow too quickly with the sample size, then standard instrumental variables estimators that ignore these measurement errors remain consistent, and standard asymptotic inference methods remain valid. These results hold even when the link measurement errors are correlated with regressors or with structural errors in the model. Simulations and real data experiments confirm our results in finite samples. These findings imply that researchers can ignore small numbers of mismeasured links in networks.