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二元结果与线性交互AI 解读

We consider general models of linear interactions in binary outcomes. We first characterize the conditions under which these models are coherent and complete. Next, we show that models of linear interactions admit sound microfoundations under assumptions of incomplete information and independence. To help guide practitioners, we discuss key modeling choices and testable restrictions. We then consider a specification that is linear in parameters, and propose a simple…

利用部分网络数据估计同伴效应AI 解读

Abstract We study the estimation of peer effects through social networks when researchers do not observe the entire network structure. Special cases include sampled networks, censored networks, and misclassified links. We assume that researchers can obtain a consistent estimator of the distribution of the network. We show that this assumption is sufficient for estimating peer effects using a linear-in-means model. We provide an empirical application to the study of peer…

采用指数概率分布的网络形成与互动模型：设定与估计AI 解读

We model network formation and interactions under a unified framework by considering that individuals anticipate the effect of network structure on the utility of network interactions when choosing links. There are two advantages of this modeling approach: first, we can evaluate whether network interactions drive friendship formation or not. Second, we can control for the friendship selection bias on estimated interaction effects. We provide microfoundations of this…

SUMMARY We provide the first empirical application of a new approach proposed by Lee ( Journal of Econometrics 2007; 140 (2), 333–374) to estimate peer effects in a linear‐in‐means model when individuals interact in groups. Assumingsufficient group size variation, this approach allows to control for correlated effects at the group level and to solve the simultaneity (reflection) problem. We clarify the intuition behind identification of peer effects in the model. We…