Time Series Analysis of Monthly Internally Generated Revenue in Hulbarag Woreda, Silte Zone, Central Ethiopia

The effective mobilization of internal revenue is crucial for sustaining local government operations and supporting development initiatives. This study aimed to analyze the dynamics of monthly internally generated revenue (MIGR) in Hulbarag Woreda, Silte Zone, over the period 2008-2023, with a focus on trend, volatility, seasonality, and forecasting. The study utilized secondary data obtained from Hulbarag Woreda Finance and Economic Development Office, encompassing monthly revenue collections across direct taxes, indirect taxes, non-tax revenue, and municipality fees. Data preparation included log transformation and differencing to address non-stationarity, confirmed through Augmented Dickey-Fuller (ADF) and KPSS tests. Time series modeling was performed using ARIMA for mean dynamics and GARCH (1, 1) for conditional variance, allowing the identification of volatility clustering and predictive forecasting. The results indicate a consistent upward trend in revenue, with June recording the highest collections and August the lowest. Direct taxes contributed the largest share (52.66%), followed by non-tax revenue (17.37%), indirect taxes (16.25%), and municipality revenue (13.71%). The GARCH (1, 1) model demonstrated strong volatility persistence (α + β = 0.91) and effectively forecasted a total revenue of 164,902,287 ETB for 2023/2024, exceeding planned projections by 47,737,198 ETB. These findings highlight the potential of advanced time series models in guiding fiscal planning, resource allocation, and budget optimization. The study is limited by its reliance on secondary administrative data and its focus on a single woreda, which may affect generalizability. Future research could incorporate socio-economic and macroeconomic factors and extend the analysis to multiple woredas to enhance predictive accuracy and policy relevance.