Machine Learning and Economic Forecasting: the role of international trade networks

This study examines the effects of deglobalization trends on international trade networks and their role in improving forecasts for economic growth. Using section-level trade data from more than 200 countries from 2010 to 2022, we identify significant shifts in the network topology driven by rising trade policy uncertainty. Our analysis highlights key global players through centrality rankings, with the United States, China, and Germany maintaining consistent dominance. Using a horse race of supervised regressors, we find that network topology descriptors evaluated from section-specific trade networks substantially enhance the quality of a country's economic growth forecast. We also find that non-linear models, such as Random Forest, eXtreme Gradient Boosting, and Light Gradient Boosting Machine, outperform traditional linear models used in the economics literature. Using SHapley Additive exPlanations values to interpret these non-linear model's predictions, we find that about half of the most important features originate from the network descriptors, underscoring their vital role in refining forecasts. Moreover, this study emphasizes the significance of recent economic performance, population growth, and the primary sector's influence in shaping economic growth predictions, offering novel insights into the intricacies of economic growth forecasting.