International Trade Flow Prediction with Bilateral Trade Provisions

TL;DR

This paper introduces a novel two-stage machine learning approach combining explainability and interaction modeling to improve the prediction of international trade flows influenced by bilateral trade provisions.

Contribution

It presents a new methodology integrating SHAP and Factorization Machines to better predict trade flows and understand the impact of trade provisions, surpassing traditional models.

Findings

Enhanced predictive accuracy of trade flow models

Deeper insights into the role of trade provisions

Effective identification of key trade provisions

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.