Forecasting Domestic Tax Revenues in Kenya Using Sarima & Holt-Winters Methods

  • Micheni Nelson Kirimi Postgraduate Student, Department of Mathematics and Statistics, University of Embu, Embu, Kenya
  • Atitwa Edwin Benson Senior Lecturer, Department of Mathematics and Statistics, University of Embu, Embu, Kenya
  • Kimani Patrick Senior Lecturer, Department of Mathematics and Statistics, University of Embu, Embu, Kenya
Keywords: Kenya Revenue Authority(KRA), Domestic Taxes Revenues, Holt-Winters (HW), Seasonal Autoregressive Integrated Moving Average (SARIMA)


Forecasting of tax revenues is an important factor in fiscal planning. Underestimation and overestimation of tax revenues lead to unstable economies. The study sought to find suitable Holt-Winters and SARIMA models that could be used to forecast Domestic tax revenues in Kenya. The study utilized the Domestic tax revenues collected in Kenya between Jan 2015 to December 2020. Analysis of data was done using R-software (version 4.1.0) where SARIMA and Holt-Winters time series forecasting methods were applied to the revenue data. SARIMA(0,1,1)(0,1,1)[12] model was found to be the best model since it had the least Bayesian Information Criterion (BIC=1236.49) and least forecasting errors (MAPE=6.9, MASE=0.37).The multiplicative Holt-Winters method was slightly superior to the additive method due to its lower error (MAPE=7.43). The study recommends the use of the two methods to forecast Domestic taxes in Kenya be used to capture the Domestic taxes revenues with high precision.


Amadala, V. (2021). ‘Treasury told to set logical budget to cut excess borrowing’. The Star-News
Chang, Y.W. & Liao, M.Y. (2010). A seasonal ARIMA model of tourism forecasting: The case of Taiwan.Asia Pacific Journal of Tourism Research,15(2), pp.215-221
Ergüven, M. H., Yılmaz, A., & Kutlu, D. (2015). Hybrid tourism within the context of touristic product diversification: glamping. Journal of Academic Social Science Studies, (41), 255-265.
Harelimana, J. B. (2018). The role of taxation on the resilient economy and development of Rwanda. J. Financ. Mark, 2(01).
Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: the forecast package for R. Journal of statistical software, 27(1), 1-22.
Hyndman, R., Koehler, A. B., Ord, J. K., & Snyder, R. D. (2008). Forecasting with exponential smoothing: the state space approach. Springer Science & Business Media.
ICPAK (2016). Kenya’s revenue analysis 201015. A Historical Perspective to Revenue Performance in Kenya.

Jenkins, G. P., Kuo, C. Y., & Shukla, G. (2000). Tax analysis and revenue forecasting. Cambridge, Massachusetts: Harvard Institute for International Development, Harvard University.
Lilian, O. (2015). Kenya loses over Sh600bn every year in tax evasion.
Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (2008). Forecasting methods and applications. John wiley & sons.
Otu, O. A., Osuji, G. A., Opara, J., Mbachu, H. I., & Iheagwara, A. I. (2014). Application of Sarima models in modelling and forecasting Nigeria’s inflation rates. American Journal of Applied Mathematics and Statistics, 2(1), 16-28.
Pelinescu, E., Anton, L. V., Ionescu, R., & Tasca, R. (2010). The analysis of local budgets and their importance in the fight against the economic crisis effects. Romanian Journal of Economic Forecasting, 17-32.
Pindyck, R. S., Hall, B. H., & Rubinfeld, D. L. (1998). TSP Handbook to Accompany Econometric Models and Economic Forecasts. McGraw-Hill/Irwin.
Rahman, M. H., Salma, U., Hossain, M. M., & Khan, M. T. F. (2016). Revenue forecasting using holt–winters exponential smoothing. Research & Reviews: Journal of Statistics, 5(3), 19-25.
Saayman, A., & Saayman, M. (2008). Determinants of inbound tourism to South Africa. Tourism economics, 14(1), 81-96.
Signé, L. (2016). How to Implement Domestic Resource Mobilization (DRM) Successfully for Effective Delivery of Sustainable Development Goals (SDGs) in Africa Illustrative Actionable Solutions for Policy Leaders.
Susan, W. G., Anthony, G. W., & John, M. K (2015). Forecasting Inflation Rate in Kenya Using SARIMA Model. American Journal of Theoretical and Applied Statistics. Vol. 4, No. 1, 2015, pp.15-18. doi: 10.11648/j.ajtas.20150401.13
How to Cite
Nelson Kirimi , M., Edwin Benson , A., & Patrick , K. (2022). Forecasting Domestic Tax Revenues in Kenya Using Sarima & Holt-Winters Methods. International Journal of Information Management Sciences, 6(1), 50-59. Retrieved from