Technology-Intensive Fdi and Economic Development in a Small Country – The Case of Estonia

Tiits, Marek

doi:10.3176/tr.2007.3.06

Cited by 6 publications

(1 citation statement)

References 42 publications

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“…By analyzing the potential export value of products, policymakers and entrepreneurs can identify opportunities that are poised to generate substantial export revenues. A detailed evaluation of potential market demand and accessibility is particularly vital for smaller economies that may not be competitive based on volume alone, making it essential to target high-value niches within the global market [40,41]. In this scenario, discerning which products are likely to succeed in foreign markets and the value that they could achieve overseas enables these economies to focus on products with promising prospects.…”

Section: Relevance For Business and Policy Stakeholdersmentioning

confidence: 99%

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: Relevance For Business and Policy Stakeholdersmentioning

confidence: 99%