ASSESSING THE PERFORMANCE OF ARIMA AND ARFIMA MODELS IN FORECASTING INTERNALLY GENERATED REVENUE OF KADUNA STATE
Abstract
Internally generated revenue (IGR) is an important source of revenue that can be used to fund public services and infrastructure projects. Accurate forecasting of IGR is essential for effective budgeting and financial planning. This study assessed the performance of ARIMA and ARFIMA models in forecasting internally generated revenue of Kaduna State. The study uses monthly IGR data from January 2003 to December 2023. The stationarity of the data was assessed using Augmented Dickey Fuller (ADF) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests. The findings showed that both ARIMA and ARFIMA models perform well in forecasting IGR, but ARFIMA model outperforms ARIMA model in terms of mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). The generated forecast values for 24 months using the model revealed that out-sample IGR forecasts fluctuated (decreasing and increasing). Thus, the study recommends the use of ARFIMA model for forecasting IGR in Kaduna State for better revenue planning and economic policy formulation.
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