ISM UNIVERSITY OF MANAGEMENT AND ECONOMICS

Abstract. The gravity model, long the empirical workhorse for modeling in-ternational trade, ignores network dependencies in bilateral trade data, instead assuming that dyadic trade is independent, conditional on a hierarchy of covari-ates over country, time, and dyad. We argue that there are theoretical reasons as well as empirical reasons to expect network dependencies in international trade. Consequently standard gravity models are empirically inadequate. We combine a gravity model specification with “latent space ” networks to develop a dynamic mixture model for real-valued directed graphs. The model incorpo-rates network dependencies in both trade incidence and trade volumes at both levels simultaneously. We estimate this model using bilateral trade data from 1990-2008. The model substantially outperforms standard accounts in terms of both in- and out-of-sample predictive heuristics. We illustrate the model’s usefulness by tracking trading propensities between the USA and China