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.

本研究研究了反全球化趋势对国际贸易网络的影响以及它们在提高经济增长预测方面所起的作用。通过使用 2010 年至 2022 年来自近 200 个国家的部门级贸易数据，我们确定了由不断上升的贸易政策不确定性所推动的网络拓扑结构的显著变化。我们的分析突出了重要的全球参与者，通过中心性排名，美国、中国和德国一直保持着持续的主导地位。使用一系列有监督回归模型的竞赛，我们发现从部门特定贸易网络中评估的网络拓扑描述符极大地提高了国家 GDP 增长预测的质量。我们还发现非线性模型，如随机森林 (Random Forest)、XGBoost 和 LightGBM，优于经济文献中使用的传统线性模型。使用 SHAP 值解释这些非线性模型的预测时，我们发现大约一半的最重要特征源于网络描述符，强调了它们在改进预测中的重要作用。此外，本研究强调了近期经济表现、人口增长和初级产业的影响在塑造经济增长预测中的意义，为经济增长预测的复杂性提供了新的见解。