结合随机与观测网络数据估算社会效应
Estimating Social Effects with Randomized and Observational Network Data
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中文总结 AI 辅助
本文提出一种创新方法，结合随机与观测网络数据，通过广义矩估计（GMM）量估算线性均值回归模型的社会效应，经模拟验证其小样本性能良好，实证分析发现香港高中生中外生分配座位的学习伙伴对数学成绩有显著正溢出效应。
中文摘要 AI 辅助
本文提出一种创新方法，用于在同伴初始随机分配决定观测网络的条件下，识别和估算广泛认可的线性均值回归模型的感兴趣参数。我们认为，初始被随机分配的同伴不会产生社会效应，但随机分配后，智能体可内生形成重要关联，可能产生同伴影响。我们提出一个矩条件，汇总总体中所有智能体的局部异质性识别信息。在内生网络空间存在ψ-依赖的假设下，我们提出广义矩估计（GMM）量，该估计量具有一致性、渐近正态性，且因闭式表达式可通过常用统计软件简便实现。蒙特卡洛模拟表明，该GMM估计量具有良好的小样本性能。利用香港高中生数据的实证分析显示，若学习伙伴的座位由教师外生分配，样本中学习伙伴对数学考试成绩存在显著的正溢出效应。
英文摘要
This paper introduces an innovative approach to identifying and estimating the parameters of interest in the widely recognized linear-in-means regression model under conditions where the initial randomization of peers determines the observed network. We assert that peers who are initially randomized do not produce social effects. However, after randomization, agents can endogenously develop significant connections that potentially generate peer influences. We present a moment condition that compiles local heterogeneous identifying information for all agents within the population. Under the assumption of $ψ$-dependence in the endogenous network space, we propose a Generalized Method of Moments (GMM) estimator, which is proven to be consistent, asymptotically normally distributed, and straightforward to implement using commonly available statistical software due to its closed-form expression. Monte Carlo simulations demonstrate the GMM estimator's strong small-sample performance. An empirical analysis utilizing data from Hong Kong high school students reveals substantial positive spillover effects on math test scores among study partners in our sample, provided that their seatmates were exogenously assigned by their teachers.
