Replication Data for: A Boltzmann Generator Framework for Modeling and Forecasting International Trade Networks by Spelta & Bosone (2025)

This paper introduces a Boltzmann Generator–inspired framework for modeling and forecasting the global trade network. Departing from traditional econometric gravity models, the proposed method employs a conditional deep generative architecture that maps macroeconomic and geographic covariates to full probabilistic distributions of bilateral trade flows. Its energy-based formulation bridges statistical physics and international economics, enabling realistic simulations that capture both dyadic dependencies and higher-order network effects. Using data on 206 countries from 2001 to 2020, the model achieves superior in-sample and out-of-sample predictive accuracy relative to standard econometric and machine learning benchmarks, with formal statistical tests confirming robust predictive dominance across time and country pairs. Beyond forecasting, the framework facilitates counterfactual policy analysis: simulations reveal distinct propagation mechanisms of GDP shocks through the trade network, highlighting China’s central role in transmitting global disturbances.