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Beyond PPML: Exploring Machine Learning Alternatives for Gravity Model Estimation in International Trade

Author

Listed:
  • Lucien Chaffa
  • Martin Trépanier
  • Thierry Warin

Abstract

This study investigates the potential of machine learning (ML) methods to enhance the estimation of the gravity model, a cornerstone of international trade analysis that explains trade flows based on economic size and distance. Traditionally estimated using methods such as the Poisson Pseudo Maximum Likelihood (PPML) approach, gravity models often struggle to fully capture nonlinear relationships and intricate interactions among variables. Leveraging data from Canada and the US, one of the largest bilateral trading relationships in the world, this paper conducts a comparative analysis of traditional and ML approaches. The findings reveal that ML methods can significantly outperform traditional approaches in predicting trade flows, offering a robust alternative for capturing the complexities of global trade dynamics. These results underscore the value of integrating ML techniques into trade policy analysis, providing policymakers and economists with improved tools for decision-making. Cette étude examine le potentiel des méthodes d'apprentissage automatique (ML) pour améliorer l'estimation du modèle de gravité, une méthode clé de l'analyse du commerce international qui explique les flux commerciaux en fonction de la taille de l'économie et de la distance. Traditionnellement estimés à l'aide de méthodes telles que l'approche du pseudo-maximum de vraisemblance de Poisson (PPML), les modèles de gravité ont souvent du mal à saisir pleinement les relations non linéaires et les interactions complexes entre les variables. En s'appuyant sur les données du Canada et des États-Unis, l'une des relations commerciales bilatérales les plus importantes au monde, cet article effectue une analyse comparative des approches traditionnelles et des approches par apprentissage automatique. Les résultats révèlent que les méthodes de ML peuvent être nettement plus performantes que les approches traditionnelles pour prédire les flux commerciaux, offrant ainsi une alternative robuste pour saisir les complexités de la dynamique du commerce mondial. Ces résultats soulignent la valeur de l'intégration des techniques de ML dans l'analyse de la politique commerciale, fournissant aux décideurs politiques et aux économistes des outils améliorés pour la prise de décision.

Suggested Citation

  • Lucien Chaffa & Martin Trépanier & Thierry Warin, 2025. "Beyond PPML: Exploring Machine Learning Alternatives for Gravity Model Estimation in International Trade," CIRANO Working Papers 2025s-14, CIRANO.
  • Handle: RePEc:cir:cirwor:2025s-14
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    File URL: https://cirano.qc.ca/files/publications/2025s-14.pdf
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    More about this item

    Keywords

    Gravity Model; PPML Machine Learning; International Trade; Trade Policy Analysis; Modèle de gravité; PPML; apprentissage automatique; commerce international; analyse de la politique commerciale;
    All these keywords.

    JEL classification:

    • F10 - International Economics - - Trade - - - General
    • F14 - International Economics - - Trade - - - Empirical Studies of Trade
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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