AI 中文总结
本研究针对同伴效应的条件随机分配情境，开发了含方差偏差修正的GMM框架，经模拟验证后用于大学生人格研究，发现正情境同伴效应被负内生同伴效应部分抵消。
AI 中文摘要
同伴效应的实证研究常利用 urn 内对同伴组的条件随机分配。我们为此情境开发了一套 GMM（广义矩估计）框架，用于估计与推断。该框架可分别识别内生同伴效应与情境同伴效应，且将同伴组随机分配的检验作为特例纳入；它允许未知异方差性，并在方差估计中修正有限 urn 偏差。其渐近理论允许同伴组数量通过增加更多 urn、更多 urn 内的组或两者兼而有之来增长。我们证明了该方法的渐近有效性，并通过蒙特卡洛模拟评估其有限样本表现。我们将该方法应用于研究大学生间的同伴效应对人格的影响：对于具有简化形式正同伴效应的特质，估计结果显示正情境效应被负内生效应部分抵消。
英文摘要
Empirical studies of peer effects often exploit conditional random assignment to peer groups within urns. We develop a GMM framework for estimation and inference in this setting. The framework separately identifies endogenous and contextual peer effects and nests tests of random peer-group assignment as a special case. It permits unknown heteroskedasticity and corrects finite-urn bias in variance estimation. Its asymptotic theory allows the number of peer groups to grow through more urns, more groups within urns, or both. We establish the asymptotic validity of the procedures and evaluate their finite-sample performance through Monte Carlo simulations. We apply the method to study peer effects on personality among university students. For traits with positive reduced-form peer effects, the estimates indicate that positive contextual effects are partly offset by negative endogenous effects.
