MST-Department of Statistics and Actuarial Science

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Browsing MST-Department of Statistics and Actuarial Science by Subject "Seasonal Naïve Model"

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    Seasonal Naïve Model Incorporating Trend Component for Tax Revenue Forecast in Kenya
    (kenyatta university, 2023) Samuel, Fredrick Kyalo; Titus K. Kibua
    Tax revenue is largest source of government revenue in Kenya. Nevertheless, the tax revenue collection has overtime fell below the planned targets. Besides, Kenya has witnessed continuous increase in public debt since public expenditures have maintained consistent growth pattern and continually surpassed revenues. The structure of tax revenue data in Kenya exhibit seasonality fluctuations with progressive increase (trend) in monthly tax revenue collections of the year. In order to facilitate government in proper fiscal planning and long-term projections, modelling and forecasting tax revenue is desirable. The objectives of this study were to develop a seasonal naïve model incorporating the trend component for forecasting tax revenue in Kenya and use the model to forecasting tax revenue collections in Kenya for the next two years. This research used time series approach to build the model. The monthly tax revenue data comprising of 192 months spanning July 2000 to June 2016 was used in this study. The study found that seasonal naïve model with trend was appropriate model for forecasting tax revenue data since it recognized both seasonal and trend components in the data and recommended application of the developed model in forecasting tax revenue collections in Kenya. Modelling the causal relationship of tax revenue with other variables that account for seasonality such as inflation, exchange rates, public expenditure and public debt was identified for future area of study.