分析斯里兰卡32年汇款数据，发现外部经济因素主导汇款波动。
The Remittance Blueprint: Data-driven Intelligence for Sri Lanka
- 基于384个月数据，用时序模型与机器学习分析汇款驱动因素。
- 汇率和国际油价是主要影响因素，机器学习预测准确率提升73.8%。
- 适合关注发展中国家经济韧性与移民政策的研究者参考。
本研究分析了1994至2025年间斯里兰卡的移民与汇款情况。基于384个月的标准化数据集，采用探索性数据分析、修正平稳性的时间序列建模（ADF、Johansen、VAR/VECM）及监督学习方法。结果表明，汇款流入主要受外部宏观经济变量驱动，特别是汇率变动和全球油价，而非国内指标。脉冲响应分析证实货币贬值与油价冲击存在非对称影响。预测方面，多变量机器学习模型优于传统单变量方法；岭回归相比SARIMA在年化均方根误差（RMSE）上提升73.8%（美元494.8百万）。优化框架预测2026年汇款额为90.01亿美元。研究凸显汇款对全球经济结构的依赖性，强调需加强汇率政策、技能型移民管理以及正规金融渠道建设，以提升长期经济韧性。
原文摘要 · Abstract (English)
This study analyzes Sri Lankan migration and remittances over 32 years (1994-2025). Using a 384-month harmonized dataset, we apply exploratory data analysis, stationarity corrected time-series modeling (ADF, Johansen, VAR/VECM), and supervised learning. Results reveal remittance inflows are primarily driven by external macroeconomic variables, specifically exchange rate dynamics and global oil prices, rather than domestic indicators. Impulse response analysis confirms the asymmetric impact of currency depreciation and oil price shocks. Predictively, multivariate machine learning models outperform traditional univariate approaches; Ridge Regression achieves a 73.8% accuracy improvement over SARIMA (Annualized RMSE: USD 494.8 Mn). The optimized framework projects 2026 remittances at USD 9,001 million under stable conditions. These findings highlight the structural dependence of remittances on global economies, emphasizing the need for robust exchange rate policies, skilled migration, and formal financial channels to enhance long-term economic resilience.
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