Multilateral Resistance-Guided Graph Message Passing for Trade Flow Prediction

Trade flows are a central variable for studying international trade networks. Traditional economic models, grounded in economic theory and mathematical derivation, provide structured representations of bilateral trade flows. However, these representations often rely on fixed functional forms, have limited ability to incorporate high-dimensional features, do not directly capture trade inertia, and scale poorly to large and dynamic trade networks. In recent years, graph neural networks have been increasingly used to address some of these limitations because they are naturally compatible with networked trade data and offer strong scalability. However, the feature aggregation process in existing graph neural models remains weakly interpretable from an economic perspective. To address this limitation, we propose MRTGNN, a graph neural network model built on a theory-guided MRTGNN layer derived from the fixed-point equations of multilateral resistance in structural gravity. Theoretically, we prove that an $L$-layer MRTGNN can approximate $L$ iterations of a multilateral-resistance fixed-point operator on a compact domain, with an explicit bound on the iteration error. Empirically, we extensively evaluate MRTGNN on panel data covering 176 countries from 2002 to 2019 against various representative baselines. The full model achieves the best RMSE, $R^2$, and Spearman rank correlation across the reported baselines. Under counterfactual tariff shocks involving China, it produces a Network Spillover Ratio one order of magnitude higher than a generic graph-attention baseline. These results show that theory-guided message passing improves trade flow prediction while enabling more interpretable counterfactual reasoning in global trade networks.