Forecasting Kenya's public debt using time series analysis

Abstract

ABSTRACT Public debt management and forecasting remain challenging for developing economies, including Kenya, where accurate predictions are essential for sustainable fiscal planning. This study aimed to analyze and forecast Kenya's public debt using two time series forecasting approaches: the Autoregressive Integrated Moving Average model and the Holt exponential smoothing model. The study sought to evaluate the performance of these models to determine the most efficient forecasting method for Kenya's debt forecasting. The research employed a cross-sectional study design, utilizing public debt data from the Central Bank of Kenya spanning January 2010 to December 2023. The methodology involved initial data preprocessing, stationarity testing, and pattern analysis, followed by dividing the data into training and testing sets. Both models were fitted to the training data, with parameters optimized through minimization of the Akaike Information Criterion and smoothing parameters. Results revealed that the Autoregressive Integrated Moving Average model demonstrated superior performance in forecasting domestic debt, with a Root Mean Square Error of 0.02649721 compared to 0.0311399 for the Holt exponential smoothing model. For external debt forecasting, the Holt exponential smoothing model showed marginally better results. In forecasting total public debt, the Autoregressive Integrated Moving Average model again proved more accurate, with a Root Mean Square Error of 0.05710133 compared to 0.06144849 for the Holt model. Based on these findings, the study recommends using the Autoregressive Integrated Moving Average model for forecasting domestic and total public debt in Kenya, while the Holt exponential smoothing method for external debt forecasting. Regular reassessment of model performance is encouraged to maintain accuracy as debt patterns evolve. Future research should consider incorporating multiple economic variables, exploring advanced time series models, and integrating debt sustainability frameworks to enhance forecasting accuracy.