论文标题
关于采样网络的同伴效应的估计
On the Estimation of Peer Effects for Sampled Networks
论文作者
论文摘要
本文介绍了在新的推理设计识别范围下对部分观察到的网络的外生同伴效应的估计，该识别的新推论范式表征了采样网络引起的缺失数据挑战，其中心想法是，两个与观察到的数据在拓扑上兼容的完整数据版本可能会引起两种不同的概率分布。我们表明，当未观察到采样和未采样单元之间的网络链接时，无法通过设计确定同伴效应。在现实的建模条件下，并假设采样单位报告其接触网络的大小，渐近偏差是由估计具有不完整网络数据的同伴效应而产生的，并提出了偏置校正的估计器。通过模拟研究了我们方法的有限样本性能。
This paper deals with the estimation of exogeneous peer effects for partially observed networks under the new inferential paradigm of design identification, which characterizes the missing data challenge arising with sampled networks with the central idea that two full data versions which are topologically compatible with the observed data may give rise to two different probability distributions. We show that peer effects cannot be identified by design when network links between sampled and unsampled units are not observed. Under realistic modeling conditions, and under the assumption that sampled units report on the size of their network of contacts, the asymptotic bias arising from estimating peer effects with incomplete network data is characterized, and a bias-corrected estimator is proposed. The finite sample performance of our methodology is investigated via simulations.
