The Remittance Blueprint: Data-driven Intelligence for Sri Lanka

2026-06-26

AI Summary

This study addresses the identification of key drivers of remittance inflows to Sri Lanka and enhances predictive accuracy to inform macroeconomic policymaking. Leveraging a harmonized monthly dataset spanning 384 observations from 1994 to 2025, the research integrates time series and supervised learning approaches to systematically examine the influence of external variables—such as exchange rates and international oil prices—on remittance dynamics. The findings reveal that remittances are significantly driven by both exchange rates and oil prices, with notable asymmetric effects. A multivariate Ridge regression model is developed, achieving a 73.8% improvement in forecasting accuracy over the conventional SARIMA model, yielding an annualized RMSE of USD 494.8 million. The model forecasts remittance inflows of USD 9.001 billion for 2026, underscoring the superior predictive capability of machine learning in remittance forecasting.

Abstract

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.

Problem

• remittances
• exchange rate
• oil prices
• macroeconomic variables
• economic resilience

Innovation

• remittance forecasting
• machine learning
• time-series modeling
• exchange rate dynamics
• asymmetric shocks