Relatedness and product complexity meet gravity models of international trade

Tiits, Marek; Kalvet, Tarmo; Ounoughi, Chahinez; Ben Yahia, Sadok

doi:10.1016/j.joitmc.2024.100288

Cited by 1 publication

(5 citation statements)

References 39 publications

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“…Additionally, they can accommodate non-normal data distributions, such as the Poisson distribution. One of the most recent contributions proposes a novel practical approach to employing a Random Forest for estimating a multi-product-group gravity model of international trade [3]. This model covers monthly bilateral trade between any two countries in the world and allows for the prediction of exports across more than 5000 product groups.…”

Section: Random Forest-based Machine Learning Models Of International...mentioning

confidence: 99%

“…The fields of economic geography, international development, and innovation studies are currently benefiting from advancements using international trade data and rapidly evolving analytical methods [3,37]. These methods are instrumental in deciphering the complex relationships between spatial factors and economic development trajectories; they facilitate the identification of potential product categories for economic diversification, thereby equipping policymakers and development organizations with the tools to forecast and guide the direction of economic growth and to identify strategic investment areas that can promote sustainable and equitable development.…”

Section: Relevance For Business and Policy Stakeholdersmentioning

confidence: 99%

“…These elements are pivotal in influencing trade flows and, by extension, the success of export initiatives. A practical approach to overcoming these challenges involves a more comprehensive synthesis of international trade research with global marketing strategies [3,42,[44][45][46][47]. Consequently, future research should explore additional factors and integrate dynamic elements like trade agreements, market demand growth rates, exchange rates, political stability, geopolitical distance, and technological advancements to enhance the predictive accuracy and practical relevance of these models.…”

Section: Relevance For Business and Policy Stakeholdersmentioning

confidence: 99%

“…The utilization of machine learning techniques in estimating gravity models of international trade represents a significant shift, offering new possibilities. Recent research [3] has shown that a machine learning method based on random forest (RF) can accommodate thousands of product groups within a single integrated model, thereby predicting bilateral trade flows with high precision at the detailed six-digit HS product code level. However, alongside the RF methodology, other approaches are rapidly gaining traction and proving to be valuable in the fields of data science and predictive analytics.…”

Section: Introductionmentioning

confidence: 99%

“…Random Forest (RF): A RF-based study [3] provided compelling evidence supporting the efficacy of Random Forest models over traditional gravity models in trade prediction tasks. The authors found that Random Forest models demonstrate superiority in capturing the non-linear dynamics of international trade relationships.…”

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confidence: 99%

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Harnessing Graph Neural Networks to Predict International Trade Flows

Sellami,

Ounoughi,

Kalvet

et al. 2024

BDCC

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In the realm of international trade and economic development, the prediction of trade flows between countries is crucial for identifying export opportunities. Commonly used log-linear regression models are constrained due to difficulties when dealing with extensive, high-cardinality datasets, and the utilization of machine learning techniques in predictions offers new possibilities. We examine the predictive power of Graph Neural Networks (GNNs) in estimating the value of bilateral trade between countries. We work with detailed UN Comtrade data that represent annual bilateral trade in goods between any two countries in the world and more than 5000 product groups. We explore two different types of GNNs, namely Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), by applying them to trade flow data. This study evaluates the effectiveness of GNNs relative to traditional machine learning techniques such as random forest and examines the possible effects of data drift on their performance. Our findings reveal the superior predictive capability of GNNs, suggesting their effectiveness in modeling complex trade relationships. The research presented in this work offers a data-driven foundation for decision-making and is relevant for business strategies and policymaking as it helps in identifying markets, products, and sectors with significant development potential.

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Section: Random Forest-based Machine Learning Models Of International...mentioning

confidence: 99%

Section: Relevance For Business and Policy Stakeholdersmentioning

confidence: 99%