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

arXiv: https://arxiv.org/abs/2404.08712 · HTML: https://mlsanity.com/paper/2404.08712/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 63 references that are in the map.
- First posted on arXiv 11 Apr 2024.

## Builds on (most cited first, 5 of 5)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Machine Learning Advances for Time Series Forecasting](https://mlsanity.com/paper/2012.12802/) (2020)
- [How is Machine Learning Useful for Macroeconomic Forecasting?](https://mlsanity.com/paper/2008.12477/) (2020)
- [Futility Analysis in the Cross-Validation of Machine Learning Models](https://mlsanity.com/paper/1405.6974/) (2014)

## Built on by (most cited first, 1 of 1)

- [Detection and classification of DDoS flooding attacks by machine learning method](https://mlsanity.com/paper/2412.18990/) (2024)

## Abstract (arXiv)

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.