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2018 | nr 31 | 24--49
Tytuł artykułu

Predicting South African Personal Income Tax - Using Holt-Winters and SARIMA

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Aim/purpose - Over estimation and under estimation of the Personal Income Tax (PIT) revenue results in an unstable economy and unreliable statistics in the public domain. This study aims to find a suitable SARIMA and Holt-Winters model that suits the sample monthly data for PIT well enough, from which a forecast can be generated. Design/methodology/approach - This study uses the aspects of time series model (Holt-Winters and SARIMA) and regression models with SARIMA errors to simulate the structure which followed the historical actual realization of PIT. The quarterly data were obtained from quarter1, 2009 to quarter 1, 2017 for the purpose of modelling and forecasting. The data were divided into training (quarter 1, 1995 to quarter 1, 2014) and testing (quarter 2, 2014 to quarter 1, 2017) data sets. The forecast from quarter 2, 2017 to quarter 1, 2020 were also derived and aggregated to annual forecast. Findings - Holt-Winters, SARIMA and Time Series Regression models fitted captured the movement of the historical PIT data with higher precession. Research implications/limitations - The generated forecast is recommended to avoid several model revisions when locating the actual PIT realisation. However, monitoring of this model is crucial as the prediction power deteriorate in a long run. Originality/value/contribution - The study recommends the use of these methods for forecasting future PIT payments because they are precise and unbiased when forecasting are made. This will assist the South African authorities in decision making for future PIT revenue.(original abstract)
Rocznik
Numer
Strony
24--49
Opis fizyczny
Twórcy
  • Operational Research, Tax, Customs and Excise Institute, South African Revenue Service
  • Operational Research, Tax, Customs and Excise Institute, South African Revenue Service
Bibliografia
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  • Maindonald, J., & Braun, J. (2003). Data analysis and graphics using R: An example based approach. Cambridge: Cambridge University.
  • SARS, & National Treasury. (2015). South African tax statistics (national publication). Pretoria, South Africa: Author.
  • SARS, & National Treasury. (2016). South African tax statistics (national publication), Pretoria, South Africa: Author.
  • SARS, & National Treasury. (2017). South African tax statistics (national publication). Pretoria, South Africa: Author.
  • Shumway, R. H., & Stoffer, D. S. (2006). Time Series Analysis and its applications with R examples (3rd ed.). USA: Springer.
  • Tong, H., & Lim, K. S. (1980). Threshold autoregression, limit cycles and cyclical data. Journal of the Royal Statistical Society, 42, 245-292.
  • Tsay, R. S. (2002). Analysis of financial time series (1st ed.). New York: John Wiley & Sons.
  • Van Heerden, Y. (2013). Personal income tax reform to secure the South African revenue base using a micro-simulation tax model. PhD thesis, Pretoria: University of Pretoria. Retrieved from https://repository.up.ac.za/handle/2263/40264
  • Wei, W. S. (2006). Time series analysis: Univariate and multivariate. Boston: Pearson.
  • 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, Supplement, 17-32.
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  • Statistics South Africa (STATS SA) (2016). Retrieved from http://www.statssa.gov. za/?page_id=1854&PPN=P0441&SCH=6639
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171505721

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