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