International Trade Flow Prediction with Bilateral Trade Provisions
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866909261746929664 |
|---|---|
| author | Pan, Zijie Gordeev, Stepan Zhao, Jiahui Meng, Ziyi Ding, Caiwen Steinbach, Sandro Song, Dongjin |
| author_facet | Pan, Zijie Gordeev, Stepan Zhao, Jiahui Meng, Ziyi Ding, Caiwen Steinbach, Sandro Song, Dongjin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_13698 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | International Trade Flow Prediction with Bilateral Trade Provisions Pan, Zijie Gordeev, Stepan Zhao, Jiahui Meng, Ziyi Ding, Caiwen Steinbach, Sandro Song, Dongjin Statistical Finance Computational Engineering, Finance, and Science Machine Learning 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. |
| title | International Trade Flow Prediction with Bilateral Trade Provisions |
| topic | Statistical Finance Computational Engineering, Finance, and Science Machine Learning |
| url | https://arxiv.org/abs/2407.13698 |