Abstract
Abstract. The study seeks to evaluate the impact of the increase in VAT rate from 14% to 15% on the state revenue as well as on future VAT collections. VAT historical data spanning from April 2009 to March 2018 (108 observations) on a fixed rate of 14% was obtained. Assuming no change on the 14% VAT rate, was fitted to the data to predict the collection of R311.2bn and R326.7bn for 2018/19 and 2019/20 respectively. The difference between prediction (at 14% rate) and actual realisation of R324.8bn and R346.7bn for the same period (at 15%rate) was computed to get the impact. Based on the model fitted values, a percentage increase in VAT rate increased payments by 4.2%in 2018/19 and 5.8%in 2019/20.This results in a slight increase in the total state revenue of 1.1% and 1.5% in 2018/19 and 2019/20 respectively. Furthermore, the model forecast R313.9bn to be collected in 2020/21 at 15% rate, the lower collection is due to the covid-19 impact on revenue collection. The usage of these types of models will assist the South African government in their budgetary plans and future decisions by taking into account more accurate projected VAT collection. However, monitoring of the model is crucial as the prediction power deteriorate in the long run.
Keywords. South African Revenue Service (SARS), Value Added tax (VAT) and Seasonal Autoregressive Integrated Moving Averages (SARIMA).
JEL. H24, C15, E37.References
Budget Speech, (2018). Accessed 10 April 2020. [Retrieved from].
Edzie-Dadzie, J. (2013). Time series analysis of value added tax revenue collection in Ghana, A study conducted in partial fulfilment of the requirements for the degree of Masters of Science Industrial Mathematics. Kwame Nkrumah University of Science and Technology. Institute of Distance Learning. Accra, Ghana. [Retrieved from].
Erero, J.L. (2015). Effects of increases in value added tax: A dynamic CGE approach, Economic Research Southern Africa, Working Papers No.558. [Retrieved from].
Gumbo, V., & Dhliwayo, L. (2018). VAT revenue modelling: The case of Zimbabwe. Department of Finance, National University of Science and Technology, Bulawayo, Zimbabwe.
Gujarati, D.N. (2003). Basic Econometric. 4thed. New York: McGraw Hill.
Gujarati, D.N., & Porter D.C. (2009). Basic Econometric. 5thed. New York: McGrawHill.
Hyndman, R.J., Makridakis, S., & Wheelwright, S.C. (1998). Forecasting Methods and Application. 3rded. USA: John Wiley and Sons.
Legeida, N., & Sologoub, D. (2003). Modeling value added tax (VAT) revenues in a transition economy: Case of Ukraine, Institute for Economic Research and Policy Consulting, Working Paper No.22. [Retrieved from].
Maindonald, J., & Braun, J. (2003). Data Analysis and Graphics using R: An Example-based Approach. Cambridge: Cambridge University Press.
Nandi, B.K., Chaudhury, M., & Hasan, G.Q. (2014). Univariate time series forecasting: A study of monthly tax revenue of Bangladesh, Centre for Research and Training, Working Paper No.9. [Retrieved from].
National Treasury, (2020). Briefing by national treasury on financial implications of COVID-19 on both the economy and budget. Accessed 05 June 2020, [Retrieved from].
Ofori, M.S. (2018). VAT revenue forecasting in Ghana, A study conducted in partial fulfilment of the requirements for the degree of MPhil Economics. University of Ghana. [Retrieved from].
Okseniuk, O. (2015). Methods of forecasting VAT revenuesfor the state budget of Ukraine, Ekonomia Międzynarodowa, 10, 81-94.
Pindyck, R.S., & Rubinfeld, D.L. (1998). Econometric Models and Economic Forecast. 4thed. Singapore: McGraw Hill.
Streimikiene, D., Ahmed, R.R., Vveinhardt, J., Ghauri, S.P., & Zahid, S. (2018). Forecasting tax revenues using time series techniques – A case of Pakistan, Economic Research, 31(1), 722-754. doi. 10.1080/1331677X.2018.1442236
Tax Statistics, (2019). Accessed 10 April 2020. [Retrieved from].
Wei, W.S. (2006). Time Series Analysis: Univariate and Multivariate Methods, 2nd ed.Pearson: Addison Wesley.
Yurekli, K., Kurunc, A., & Ozturk, F. (2005). Testing the residuals of an ARIMA model on the Cekerk stream watershed in Turkey, Turkish Journal of Engineering and Environmental Sciences, 29(2), 61-74.
