FORECASTING INTERNALLY GENERATED REVENUE OF KADUNA STATE USING ARFIMA MODEL

Abstract

Accurate forecasting of internally generated revenue (IGR) is crucial for effective fiscal planning and sustainable economic development. This study applies the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model to forecast the IGR of Kaduna State, Nigeria. ARFIMA is particularly useful for modeling long-memory processes, which are common in financial and economic time series. The data used for the study was obtained secondarily from Kaduna State Internal Revenue Service (KADIRS). The stationarity of the data was assessed using Augmented Dickey Fuller (ADF) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests. The long memory parameter d of the ARFIMA model was estimated using the Geweke and Porter-Hudak (GPH) method. The presence of a long memory structure was revealed by the sample autocorrelation function. Based on the information selection criteria, using AIC, BIC, and HQC, two optimal time series models were selected. But the prediction power of ARFIMA (3,0.423636,4) model is better and suitable for monthly periods forecasting, as such the model best fit the data. Thus, the findings can be used to provide accurate and reliable forecast of Kaduna State IGR for better revenue planning and economic policy formulation.

Keywords: ARFIMA model, forecasting, internally generated revenue, long-memory.