REVENUE PERFORMANCE, ANALYSIS AND FORECASTING USING TIME SERIES ANALYSIS
Abstract
In any economy, tax analysis and revenue forecasting are of paramount importance for informing sound economic and fiscal policies. This paper is particularly relevant for Nigeria as it aims to identify significant variables affecting tax revenue and improve planning and policy outcomes. Despite its importance, tax revenue generation in Nigeria faces persistent challenges, necessitating accurate forecasting tools to enhance decision-making. The paper forecast the tax revenue (TR) of Nigeria for the fiscal year 2023–2024 using three different time series techniques. The study employed the autoregressive model with seasonal dummies (AR), the autoregressive integrated moving average model (ARIMA), and the vector auto-regression (VAR) model. Annual data from 1990 to 2023 was used, with a forecasting focus on the July–December 2024 period. For the forecasting of total tax revenue, key components such as petroleum profit tax (PPT), company income tax (CIT), value-added tax (VAT), and tertiary education tax (EDT) were analyzed. The results revealed that the ARIMA model produced the most accurate forecast for the 2023–24 fiscal year, estimating revenue at N3, 279.88 billion, which closely aligns with the government’s target of N3, 521 billion for the Federal Inland Revenue Service. This study recommended that the government should introduce new tax reforms and broaden the tax net.
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Revenue Performance, Fiscal Policies, Forecasting, Time SeriesDownloads
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Copyright (c) 2025 Bilkisu M, Ibrahim A., Ismaila A. M, Abubakar M. A.
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