The relevance of business cycles in forecasting international tourist arrivals

Casual empiricism suggests that there may be a cyclical trend associated with international tourist arrivals in which variation around the linear trend can be formed by the interaction with other cyclical phenomena. This paper employs a simple model that incorporates a linear trend and sine function to capture these two characteristics in forecasting international tourist arrivals in Hong Kong. The model is extended to include a set of sine functions through the application of Fourier analysis to account for situations in which more than one phenomenon may be present in the time series. The forecasting accuracy of the model is compared with other forecasting approaches. Evaluation of the results using the mean absolute percentage error measure show that the forecasting performance of the extended model with a linear trend and two sine functions is superior in terms of accuracy when compared with other forecasting models.