Networks are ubiquitous in economic research on organizations, trade, and many other areas. 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 affecting their propensity to form ties. This makes the latter a good choice if the researcher is interested in capturing the effect of exogenous covariates on tie formation without having a specific theory on the endogenous dependence structures at play. After introducing the two model classes, we showcase them through real-world applications to networks stemming from international arms trade and foreign exchange activity. We further provide full replication materials to facilitate the adoption of these methods in empirical economic research.

翻译：网络在组织、贸易及众多其他领域的经济研究中无处不在。然而，尽管经济理论广泛探讨网络问题，至今尚未形成一套用于其经验建模的通用框架。为此，我们引入两种不同的统计模型——指数随机图模型（ERGM）与加乘效应网络模型（AME）。两类模型均能解释观测值之间的网络相互依存性，但实现方式有所不同。ERGM允许研究者明确指定并检验特定网络结构的影响，因此当研究重点是估计内生网络效应时，该模型成为自然之选。相比之下，AME通过引入影响行动者连接倾向的个体特定潜变量来捕捉这些效应，若研究者旨在捕捉外生协变量对连接形成的影响，且缺乏关于内生依赖结构的具体理论时，后者则是更优选择。在介绍两类模型后，我们通过国际武器贸易与外汇活动网络的实际应用案例加以阐释。同时提供完整的重复性研究材料，以促进这些方法在实证经济研究中的推广应用。