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

Thiago Silva (), Paulo Wilhelm and Diego Amancio

No 597, Working Papers Series from Central Bank of Brazil, Research Department

Abstract: 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.

Date: 2024-08
New Economics Papers: this item is included in nep-big, nep-cmp, nep-eur, nep-gro, nep-int and nep-net
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Journal Article: Machine learning and economic forecasting: The role of international trade networks (2024)
Working Paper: Machine learning and economic forecasting: the role of international trade networks (2024)
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