ABSTRACT
In the past decades, both causal and noncausal modeling techniques have been extensively applied in tourism demand forecasting (Song, Wong, and Chon, 2003). Noncausal modeling techniques which mainly feature time series modeling approaches typically forecast future tourism demand based on historic trends. Exponential smoothing and the Box–Jenkins procedure have been frequently used in these past studies. Causal modeling techniques often resort to econometric methods to forecast tourism demand taking into consideration the factors which purportedly influence demand changes in the model specification. A number of published empirical papers reveal that noncausal models are often superior to causal models in forecasting tourism demand (Chan et al., 1999; Dharmaratne, 1995; Kulendran and King, 1997; Kulendran and Witt, 2001; Turner et al., 1995; Witt et al., 1994; Witt et al., 2003; Witt and Witt, 1992). On the other hand, there exist in the literature many studies which provide evidence that econometric models are superior to time series models in forecasting tourism demand (Crouch et al., 1992; Hiemstra and Wong, 2002; Kim and Song, 1998; Song et al., 2000; Song and Witt, 2000; Song, Witt, and Jensen, 2003; Song, Wong, and Chon, 2003).
