Statistical Testing in Forecasting Model Selection

Abstract

The ability of various econometric and univariate time-series models to generate accurate forecasts of international tourism demand is evaluated. Accuracy is assessed in terms of error magnitude and also directional change error. Statistical testing for both forecasting bias and directional change forecasting performance is introduced. The empirical results show that for 1-year-ahead forecasting, the time-varying parameter model performs consistently well. However, for 2- and 3-years-ahead forecasting, the best model varies according to the forecasting error criterion under consideration. This highlights the importance (for longer term forecasts) of selecting a forecasting method that is appropriate for the particular objective of the forecast user.