34. International Public Finance Conference
Comparison of Tax Revenue Forecasting Models for TurkeyHamza Erdoğdu, Recep Yorulmaz
The objective of this study is to compareperformance of threeforecasting tax revenue models for Turkeyover the period of 2006: 01 to 2018: 12. Three different time series forecasting techniques such as Random Walk, SARIMA (Seasonal Autoregressive Integrated Moving Average) and BATS (Exponential Smoothing State Space Model with Box-Cox Transformation, ARMA Errors, Trend and Seasonal Components) are used in the study. At the beginning of the analysis, the data set was apportioned into two parts: training and testing. The training period is from 2006: 01 to 2014: 12 and the testing part is from 2015: 01 to 2018: 12. Based on different evaluation criteria, forecast points of 36 months are obtained for each forecasting model. We find that using the BATS model, rather than classical S(ARIMA) in forecasting series of monthly tax revenues of Turkey, provide more accurate forecasts. The empirical findings of this study help the experts in the preparation process of government’s budgets.