Forecasting tourism demand: The Hamilton filter

Abstract

Studies on tourism demand forecasting techniques can be classified into two main groups: quantitative and qualitative methods (Song & Li, 2008). The use of quantitative methods in tourism demand forecasting is more popular than qualitative methods (Song & Li, 2008). The types of time series models include naive, autoregressive, single exponential, moving average and historical averages (Song, Qiu, & Park, 2019). The decomposition of time series has been found to improve forecasting accuracy (Shabri, 2016; Zhang et al., 2017). The common techniques for time series decomposition in tourism forecasting are filters (Li, Wong, Song, & Witt, 2006); spectral analysis (Coshall, 2000); and empirical mode decompositions (Chen, Lai, & Yeh, 2012). Causal structural time series models perform less satisfactorily than univariate models (Turner & Witt, 2001). Li and Law (2019) examined the effectiveness of decomposed search engine data in forecasting tourism demand in Hong Kong. The proposed technique of using decomposed online search engine data was viable based on the out-of-sample forecast evaluation (Li & Law, 2019). When decomposing time series, it is important to account for seasonality as most tourism destinations are affected by seasonal patterns (Saayman & Botha, 2017; Vergori, 2012). Chen, Li, Wu, and Shen (2019) proposed a multiseries structural time series method as an alternative technique to seasonal tourism demand forecasting. The forecast evaluation support that the structural model is viable. Chu (2004) applied a cubic polynomial model to forecast tourist arrivals in Singapore. The cubic polynomial model was found to be less effective than the sine wave and ARIMA applications. The use of seasonal fractional models for modelling the seasonal component of Spanish tourism demand was pioneered by Gil-Alana, De Gracia, and Cunado (2004). The authors found that the number of foreigners and foreign guest nights exhibit seasonal long memory behaviour.