A fractionally integrated autoregressive moving average approach to forecasting tourism demand

The primary aim of this paper is to incorporate fractionally integrated ARMA (p, d, q) (ARFIMA) models into tourism forecasting, and to compare the accuracy of forecasts with those obtained by previous studies. The models are estimated using the volume of monthly international tourist arrivals in Singapore. Empirical findings demonstrate the evidence that the approach we propose generates relatively lower sample mean absolute percentage errors (MAPEs). This study also deals with the volatile data faced by a forecaster. We use the Asian financial crisis and the September 11 event as examples of economic and political shocks. With respect to the objective of shaving the coefficient MAPE, forecasts based on the selected ARFIMA models dominate convincingly.