| Autor: |
Marek Tiits, Tarmo Kalvet, Chahinez Ounoughi, Sadok Ben Yahia |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Journal of Open Innovation: Technology, Market and Complexity, Vol 10, Iss 2, Pp 100288- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2199-8531 |
| DOI: |
10.1016/j.joitmc.2024.100288 |
| Popis: |
Researchers have long used gravity models to analyze international trade patterns, identify export opportunities, and negotiate trade agreements. Recent research has emphasized the significance of relatedness and product complexity research in developing robust economic development strategies. This paper presents a novel approach, incorporating relatedness and product complexity as integral elements for interpreting export potential within gravity models powered by machine learning. Our approach stands out for its proficiency in accurately predicting bilateral trade values at a detailed product group level, providing valuable insights for policymakers and other stakeholders. The research leverages random forest machine learning models for predictions and incorporates relatedness and complexity to reveal new dimensions in international trade analysis. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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