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Statistical Network Data Analysis in Economics

by   Giacomo De Nicola, et al.
Universität München

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 effects, such as reciprocity and triadic closure, 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.


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