Machine learning and economic forecasting: the role of international
  trade networks
Thiago C. Silva Paulo V. B. Wilhelm Diego R. Amancio
This study examines the effects of de-globalization trends on international
trade networks and their role in improving forecasts for economic growth. Using
section-level trade data from nearly 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 GDP growth forecast. We also
find that non-linear models, such as Random Forest, XGBoost, and LightGBM,
outperform traditional linear models used in the economics literature. Using
SHAP values to interpret these non-linear model's predictions, we find that
about half of 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.