| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w27151 |
| 来源ID | Working Paper 27151 |
| Machine Learning in Gravity Models: An Application to Agricultural Trade | |
| Munisamy Gopinath; Feras A. Batarseh; Jayson Beckman | |
| 发表日期 | 2020-05-18 |
| 出版年 | 2020 |
| 语种 | 英语 |
| 摘要 | Predicting agricultural trade patterns is critical to decision making in the public and private domains, especially in the current context of trade disputes among major economies. Focusing on seven major agricultural commodities with a long history of trade, this study employed data-driven and deep-learning processes: supervised and unsupervised machine learning (ML) techniques – to decipher patterns of trade. The supervised (unsupervised) ML techniques were trained on data until 2010 (2014), and projections were made for 2011-2016 (2014-2020). Results show the high relevance of ML models to predicting trade patterns in near- and long-term relative to traditional approaches, which are often subjective assessments or time-series projections. While supervised ML techniques quantified key economic factors underlying agricultural trade flows, unsupervised approaches provide better fits over the long-term. |
| 主题 | Econometrics ; Estimation Methods ; International Economics ; Trade ; Environmental and Resource Economics ; Agriculture |
| URL | https://www.nber.org/papers/w27151 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/584824 |
| 推荐引用方式 GB/T 7714 | Munisamy Gopinath,Feras A. Batarseh,Jayson Beckman. Machine Learning in Gravity Models: An Application to Agricultural Trade. 2020. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w27151.pdf（326KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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