International Trade Flow Prediction with Bilateral Trade Provisions

ArXiv ID: 2407.13698 “View on arXiv”

Authors: Unknown

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.

Keywords: Trade Flow Prediction, Preferential Trade Agreements (PTAs), Explainable Machine Learning, Factorization Machines

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper features dense mathematical formalizations including Shapley value derivations, Factorization Machine formulations with linear-time complexity proofs, and specialized econometric models (Poisson PML with Lasso penalties), indicating high mathematical complexity. However, empirical rigor is limited as the methodology is presented as a proof-of-concept with described datasets (UN Comtrade, DTAs) but lacks backtesting metrics, implementation details, or live performance validation typical of deployable models.

  flowchart TD
    A["Research Goal: Predict Bilateral Trade Flows using PTA Provisions"] --> B["Data: Historical Trade Flows & PTA Text Data"]
    B --> C["Stage 1: Variable Selection via SHAP Explainer"]
    C --> D["Key Provisions Identified"]
    D --> E["Stage 2: Factorization Machine Model"]
    E --> F["Computational Process: Pairwise Interaction Analysis"]
    F --> G["Outcomes: Enhanced Predictive Accuracy & Dynamic Insights"]