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Networks are ubiquitous in economic research on organizations, trade, andmany other areas. However, while economic theory extensively considersnetworks, no general framework for their empirical modeling has yet emerged. Wethus introduce two different statistical models for this purpose -- theExponential Random Graph Model (ERGM) and the Additive and MultiplicativeEffects network model (AME). Both model classes can account for networkinterdependencies between observations, but differ in how they do so. The ERGMallows one to explicitly specify and test the influence of particular networkstructures, making it a natural choice if one is substantively interested inestimating endogenous network effects. In contrast, AME captures these effectsby introducing actor-specific latent variables affecting their propensity toform ties. This makes the latter a good choice if the researcher is interestedin capturing the effect of exogenous covariates on tie formation without havinga specific theory on the endogenous dependence structures at play. Afterintroducing the two model classes, we showcase them through real-worldapplications to networks stemming from international arms trade and foreignexchange activity. We further provide full replication materials to facilitatethe adoption of these methods in empirical economic research.

Dependence matters: Statistical models to identify the drivers of tie formation in economic networks