国际双边贸易条款下的贸易流预测
International Trade Flow Prediction with Bilateral Trade Provisions
AI总结：
本文提出两阶段方法（SHAP变量选择+因子分解机交互效应分析）预测国际双边贸易流，识别PTA关键条款，提升预测精度并揭示贸易动态。
摘要：
本文提出了一种预测国际双边贸易流的新方法，强调了优惠贸易协定（PTAs）在全球贸易格局中日益增长的重要性。鉴于传统模型（如贸易引力模型）的局限性，本研究引入了一种结合可解释机器学习与因子分解模型的两阶段方法。第一阶段采用SHAP解释器进行有效的变量选择，识别PTA中的关键条款；第二阶段利用因子分解机模型分析这些条款对贸易流的成对交互效应。通过分析综合数据集，本文证明了该方法的有效性。研究结果不仅提高了贸易流模型的预测准确性，还为受特定双边贸易条款影响的复杂国际贸易动态提供了更深入的见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
