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1	Forecasting annual tax revenue of the South African taxes using time series Holt-Winters and ARIMA/SARIMA Models Makananisa, Mangalani P. 10 1900 (has links) This study uses aspects of time series methodology to model and forecast major taxes such as Personal Income Tax (PIT), Corporate Income Tax (CIT), Value Added Tax (VAT) and Total Tax Revenue(TTAXR) in the South African Revenue Service (SARS). The monthly data used for modeling tax revenues of the major taxes was drawn from January 1995 to March 2010 (in sample data) for PIT, VAT and TTAXR. Due to higher volatility and emerging negative values, the CIT monthly data was converted to quarterly data from the rst quarter of 1995 to the rst quarter of 2010. The competing ARIMA/SARIMA and Holt-Winters models were derived, and the resulting model of this study was used to forecast PIT, CIT, VAT and TTAXR for SARS fiscal years 2010/11, 2011/12 and 2012/13. The results show that both the SARIMA and Holt-Winters models perform well in modeling and forecasting PIT and VAT, however the Holt-Winters model outperformed the SARIMA model in modeling and forecasting the more volatile CIT and TTAXR. It is recommended that these methods are used in forecasting future payments, as they are precise about forecasting tax revenues, with minimal errors and fewer model revisions being necessary. / Statistics / M.Sc. (Statistics) SARS Personal Income Tax (PIT) Corporate Income Tax (CIT) Value Added Tax (VAT) Total Tax Revenue (TTAXR) Holt-Winters 519.55 Box-Jenkins forecasting Forecasting -- Statistical methods Forecasting Economic Forecasting
2	Forecasting annual tax revenue of the South African taxes using time series Holt-Winters and ARIMA/SARIMA Models Makananisa, Mangalani P. 10 1900 (has links) This study uses aspects of time series methodology to model and forecast major taxes such as Personal Income Tax (PIT), Corporate Income Tax (CIT), Value Added Tax (VAT) and Total Tax Revenue(TTAXR) in the South African Revenue Service (SARS). The monthly data used for modeling tax revenues of the major taxes was drawn from January 1995 to March 2010 (in sample data) for PIT, VAT and TTAXR. Due to higher volatility and emerging negative values, the CIT monthly data was converted to quarterly data from the rst quarter of 1995 to the rst quarter of 2010. The competing ARIMA/SARIMA and Holt-Winters models were derived, and the resulting model of this study was used to forecast PIT, CIT, VAT and TTAXR for SARS fiscal years 2010/11, 2011/12 and 2012/13. The results show that both the SARIMA and Holt-Winters models perform well in modeling and forecasting PIT and VAT, however the Holt-Winters model outperformed the SARIMA model in modeling and forecasting the more volatile CIT and TTAXR. It is recommended that these methods are used in forecasting future payments, as they are precise about forecasting tax revenues, with minimal errors and fewer model revisions being necessary. / Statistics / M.Sc. (Statistics) SARS Personal Income Tax (PIT) Corporate Income Tax (CIT) Value Added Tax (VAT) Total Tax Revenue (TTAXR) Holt-Winters 519.55 Box-Jenkins forecasting Forecasting -- Statistical methods Forecasting Economic Forecasting
3	Employing Bayesian Vector Auto-Regression (BVAR) method as an altenative technique for forecsating tax revenue in South Africa Molapo, Mojalefa Aubrey 11 1900 (has links) Statistics / M. Sc. (Statistics) Corporate income tax Personal income tax Total tax revenue Forecasting Bayesian Vector Autoregressive (BVAR) Error Trend Seasonal (ETS) Autoregressive moving Average (ARMA) Root mean squared error (RMSE) Autocorrelation 519.542 Bayesian statistical decision theory Vector automobile Tax revenue estimating Corporations -- Taxation Income tax Standard deviations

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