Machine learning and economic forecasting: the role of international trade networks

TL;DR

This paper investigates how international trade network structures, affected by de-globalization, can improve economic growth forecasts, highlighting the importance of network descriptors and non-linear models in predicting GDP growth.

Contribution

It introduces the use of trade network topology features in economic forecasting and demonstrates their effectiveness with advanced non-linear machine learning models.

Findings

Network topology descriptors improve GDP growth forecasts.

Non-linear models outperform traditional linear models.

Network features are among the most important predictors.

Abstract

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.