A non-conventional model to explain and forecast prices and trade flows of food staples across developing nations in Southeast Asia

用于解释和预测东南亚发展中国家主食价格和贸易流量的非传统模型

• 批准号: 17H07180
• 负责人: Quek Olivia
• 金额: $ 1.66万
• 依托单位: Waseda University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Research Activity Start-up
• 财政年份: 2017
• 资助国家: 日本
• 起止时间: 2017-08-25 至 2019-03-31
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-17H07180/
• 关键词: Spatial economics; Food security; Spatial integration; Food market integration; Transport theory; Trade costs; Arbitrage; Spatial economics; Trade

The purpose of this research is to develop an unconventional model to understand and explain the trade flows of homogenous and perishable food products across geographically distinct markets. When working with rice price data from developing countries, I used web mining methods to obtain as many sources of data as possible, and performed data cleaning and visualization of the data. I then employed unsupervised machine learning techniques such as Prrincipal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE), to better understand the co-movements of food prices across groups of trading partners. These data-compressing techniques allowed me to identify groups of trading markets whose food prices co-move to a high degree.

本研究的目的是开发一个非常规模型来理解和解释同质和易腐食品在不同地理市场的贸易流动。在处理发展中国家的大米价格数据时，我使用网络挖掘方法来获取尽可能多的数据源，并对数据进行数据清洗和可视化。然后，我采用了无监督机器学习技术，例如主成分分析 (PCA) 和 t-分布式随机邻域嵌入 (t-SNE)，以更好地了解贸易伙伴群体之间食品价格的联动。这些数据压缩技术使我能够识别食品价格高度同步波动的交易市场组。