📄 Abstract
This study investigates trends and forecasts of Uganda?s debt service burden using annual time-series data from 1990 to 2022, sourced from the World Bank. Employing a quantitative approach, the study utilizes the autoregressive integrated moving average (ARIMA) modelling technique to analyze Uganda?s debt service as a percentage of gross national income (GNI). The dependent variable, debt service (% of GNI), is modelled with autoregressive (AR) and moving average (MA) components as independent variables, while parameter estimation is conducted through maximum likelihood estimation (MLE). The AR(1) coefficient of -0.585507 indicates a negative and statistically significant relationship between past and present debt service burdens, implying that approximately 58.6% of the debt service burden is explained by its previous value. This suggests a persistence in the debt burden, with past debt service obligations significantly influencing current levels. The estimated ARIMA (1, 1, 5) model satisfies the criteria for covariance stationarity and invertibility, confirming its reliability for short- and long-term forecasting. Projections indicate a gradual decline in Uganda?s debt service burden, stabilizing around 1.06% of GNI by 2040, reflecting improvements in debt sustainability and repayment capacity. The study recommends strengthened fiscal discipline, effective debt management strategies, and diversification of revenue sources to sustain this favourable trajectory.
🏷️ Keywords
📚 How to Cite:
Nahabwe Patrick Kagambo John, Kagarura Willy Rwamparagi , MODELLING UGANDA'S DEBT SERVICE BURDEN , Volume 12 , Issue 1, january 2025, EPRA International Journal of Economics, Business and Management Studies (EBMS) ,