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International Trade Flow Prediction with Bilateral Trade Provisions

Zijie Pan, Stepan Gordeev, Jiahui Zhao, Ziyi Meng, Caiwen Ding, Sandro Steinbach, Dongjin Song

TL;DR

This work tackles the prediction of international bilateral trade flows in the presence of Preferential Trade Agreements by moving beyond the traditional Gravity Model. It introduces a two-stage pipeline: first, SHAP Explainer-based variable selection identifies the most impactful PTA provisions; second, a Factorization Machine regressor models the log trade flows and captures pairwise interactions among provisions. Using UN Comtrade agricultural data (1968–2017) and the Deep Trade Agreements dataset (282 PTAs), the approach achieves strong predictive performance (MLP training accuracy $96.09\%$, test accuracy $88.29\%$, F1 $0.926$) and a FM RMSE of $3.26$ for log flows, while revealing interaction patterns via a provision interaction heatmap. The results provide both improved predictive power and actionable insights into which provisions and their interactions most strongly shape trade, offering a structured framework for policy analysis and further research in trade dynamics.

Abstract

This paper presents a novel methodology for predicting international bilateral trade flows, emphasizing the growing importance of Preferential Trade Agreements (PTAs) in the global trade landscape. Acknowledging the limitations of traditional models like the Gravity Model of Trade, this study introduces a two-stage approach combining explainable machine learning and factorization models. The first stage employs SHAP Explainer for effective variable selection, identifying key provisions in PTAs, while the second stage utilizes Factorization Machine models to analyze the pairwise interaction effects of these provisions on trade flows. By analyzing comprehensive datasets, the paper demonstrates the efficacy of this approach. The findings not only enhance the predictive accuracy of trade flow models but also offer deeper insights into the complex dynamics of international trade, influenced by specific bilateral trade provisions.

International Trade Flow Prediction with Bilateral Trade Provisions

TL;DR

This work tackles the prediction of international bilateral trade flows in the presence of Preferential Trade Agreements by moving beyond the traditional Gravity Model. It introduces a two-stage pipeline: first, SHAP Explainer-based variable selection identifies the most impactful PTA provisions; second, a Factorization Machine regressor models the log trade flows and captures pairwise interactions among provisions. Using UN Comtrade agricultural data (1968–2017) and the Deep Trade Agreements dataset (282 PTAs), the approach achieves strong predictive performance (MLP training accuracy , test accuracy , F1 ) and a FM RMSE of for log flows, while revealing interaction patterns via a provision interaction heatmap. The results provide both improved predictive power and actionable insights into which provisions and their interactions most strongly shape trade, offering a structured framework for policy analysis and further research in trade dynamics.

Abstract

This paper presents a novel methodology for predicting international bilateral trade flows, emphasizing the growing importance of Preferential Trade Agreements (PTAs) in the global trade landscape. Acknowledging the limitations of traditional models like the Gravity Model of Trade, this study introduces a two-stage approach combining explainable machine learning and factorization models. The first stage employs SHAP Explainer for effective variable selection, identifying key provisions in PTAs, while the second stage utilizes Factorization Machine models to analyze the pairwise interaction effects of these provisions on trade flows. By analyzing comprehensive datasets, the paper demonstrates the efficacy of this approach. The findings not only enhance the predictive accuracy of trade flow models but also offer deeper insights into the complex dynamics of international trade, influenced by specific bilateral trade provisions.
Paper Structure (11 sections, 8 equations, 2 figures, 1 table)

This paper contains 11 sections, 8 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work and Contribution
  3. Data
  4. Methodology
  5. SHAP Explainer
  6. Factorization Machine
  7. Two-Stage Analysis
  8. Results
  9. Identification of Significant Provisions
  10. Trade Flow Prediction and Provision Interactions
  11. Conclusion

Figures (2)

  • Figure 1: PTA Summary Plot
  • Figure 2: Provision Interaction Plot