Hybrid ARIMA- Neural Network Model to Forecast VAT on Gasoline Consumption in Iran

Abstract

One of the major problems in budgeting is to predict the various kinds of future income precisely as possible. Since tax revenue is very important component in the combination of state income, the present paper considers the forecasting of VAT on gasoline consumption. The main purpose is to achieve an efficient method to forecast gasoline consumption and VAT on it in Iran. Hence, a Hybrid ARIMA- Neural Network model is used to forecast gasoline consumption. After confirming the good performance of this method compared with autoregressive integrated moving average processes(ARIMA), VAT on gasoline consumption is calculated by applying its tax rate. Results indicate that during the years 2013 to 2016, VAT on gasoline consumption will grow by 31.6 percent on average.

Keywords

• value added tax (vat)
• gasoline consumption
• multilayer neural networks
• hybrid methods