This study investigates the macroeconomic determinants and dynamic behaviour of personal remittances as a share of Gross Domestic Product (GDP) in Nepal, emphasizing external demand in major destination countries and domestic monetary policy. Using annual data (1993-2024), we construct composite indices via Principal Component Analysis (PCA) for multi-country external demand and a domestic Monetary Conditions Index (MCI). Our small-sample econometric pipeline includes Autoregressive Distributed Lag (ARDL) bounds testing, Engle-Granger cointegration, Dynamic OLS (DOLS), and a two-step Error Correction Model (ECM). We also employ Granger causality tests and multi-model forecasting using machine learning and ECM scenarios. The analysis reveals a strong positive long-run effect of external demand on remittances and a significant negative impact of tighter domestic monetary conditions. The ECM confirms a stable cointegrating relationship, correcting approximately 26% of disequilibria annually. Medium-term projections indicate remittances will remain structurally important, reaching around 28.3% of GDP by 2030 under baseline conditions, while exhibiting high sensitivity to external demand shocks. This study advances the literature by integrating PCA-derived external demand and monetary conditions indices within a unified ARDL-ECM framework for small samples. Focusing on one of the world's most remittance-dependent economies, it offers actionable insights for monetary policy calibration, migration diversification, and the productive utilization of remittance inflows.

翻译：本研究考察了尼泊尔个人汇款占国内生产总值（GDP）比重的宏观经济决定因素及动态行为，重点分析了主要目的地国家的外部需求与国内货币政策。利用1993-2024年年度数据，我们通过主成分分析法（PCA）构建了多国外部需求综合指数及国内货币状况指数（MCI）。针对小样本特征，计量经济学分析流程包括自回归分布滞后（ARDL）边界检验、恩格尔-格兰杰协整、动态最小二乘法（DOLS）及两步误差修正模型（ECM）。此外，我们采用格兰杰因果检验，并结合机器学习与ECM场景进行多模型预测。分析表明，外部需求对汇款具有显著正向长期效应，而紧缩性国内货币状况则产生显著负向影响。ECM确认了稳定的协整关系，每年可修正约26%的失衡。中期预测显示，在基准情景下，汇款至2030年将保持结构性重要地位，占GDP约28.3%，同时对外部需求冲击高度敏感。本研究通过将PCA衍生的外部需求与货币状况指数整合至统一的小样本ARDL-ECM框架中，推动了相关文献发展。聚焦于全球最依赖汇款的经济体之一，本研究为货币政策校准、移民多样化及汇款流入的生产性利用提供了可操作的洞见。