Networks are ubiquitous in economic research on organizations, trade, and many other topics. However, while economic theory extensively considers networks, no general framework for their empirical modeling has yet emerged. We thus introduce two different statistical models for this purpose -- the Exponential Random Graph Model (ERGM) and the Additive and Multiplicative Effects network model (AME). Both model classes can account for network interdependencies between observations, but differ in how they do so. The ERGM allows one to explicitly specify and test the influence of particular network structures, making it a natural choice if one is substantively interested in estimating endogenous network effects. In contrast, AME captures these effects by introducing actor-specific latent variables which affect their propensity to form ties. This makes the model a good choice if the researcher is interested in capturing the effect of exogenous covariates on tie formation without having a specific idea of the endogenous dependence structures at play. We introduce the two model classes and apply them to networks stemming from international arms trade and foreign exchange activity. Moreover, we provide full replication materials to facilitate the adoption of these methods in empirical economic research.

翻译：网络在有关组织、贸易及众多其他主题的经济研究中无处不在。然而，尽管经济理论广泛涉及网络，但尚未形成对其实证建模的一般性框架。为此，我们引入了两种适用于此目的的统计模型——指数随机图模型（ERGM）和加性乘性效应网络模型（AME）。这两类模型均可解释观测值之间的网络相互依赖性，但其实现方式存在差异。ERGM允许明确设定并检验特定网络结构的影响，因此在研究者着重于估计内生网络效应时成为自然之选。相比之下，AME通过引入影响个体关系形成倾向的主体特定潜变量来捕捉这类效应，这使得该模型成为研究者关注外生协变量对关系形成影响、但对具体的内生依赖结构缺乏明确认识时的理想选择。我们介绍了这两类模型，并将其应用于源自国际武器贸易和外汇活动的网络。此外，我们提供了完整的复制资料，以促进这些方法在实证经济研究中的推广应用。