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Loganathan Nanthakumar, Thirunaukarasu Subramaniam & Mori KogidSong, Wong and Chon (2003) introduced general-to-specificmodelling approach to forecast international tourist arrivals from 16major countries to Hong Kong for the period from 2001 to 2008. Thespecification of econometric model used is known as auto-regressivedistributed lag model (ADLM). This study shows that, several importantfactors that determine the demand for Hong Kong tourism include thecost of tourism in Hong Kong, the economic scenario in the origincountries, the costs of tourism in the competing destinations and the‘word of mouth’ effect. Again, ADLM measurement of tourismforecasting is suitable for multivariate modelling and by using thismethod we are able to determine various factors that cause tourist arrivalsin the future. Greenidge (2001) used structural time-series modelling(STM) to evaluate tourism demand forecasting in Barbados. STMmodelling has its own strength, where time-varying components can beincorporated in the regression equation to capture the movement of touristarrivals using explanatory variables. Besides using basic structuralmodelling (BSM), STM model is also able to include general structuralmodelling (GSM) with seasonal effect. Loganathan, Ibrahim and Harun(2008) on the other hand, used standard Johansen-Juselius co-integrationmethod to study the importance of the tourism industry in enhancing tradeperformance and economic development in Malaysia. Thus, these studiesprovide valuable insights into the stylized facts of tourism behaviour andprovide reliable out-of-sample forecasts of tourism demand.Athanasopoulos and Hyndman (2008) modelled Australian domestictourism demand using regression model, exponential smoothing viainnovations state space model and innovations state space model withexogenous variables. Cross-sectional data was applied in this study, andthe data was collected using computer-assisted telephone interviews from120,000 Australians aged 15 years and above. All the models used in thestudy highlighted the impact of world events on Australian domestictourism. Such events are the increase in business travel immediately afterthe Sydney Olympic in the year 2000 and the significant increase invisiting relatives and friends after the 2002 Bali bombings. Oneinteresting aspect that can be found from this study is that, all threestatistical models used in this study outperform the Tourism ForecastCommittee (TFC) results for short-term demand of Australian domestictourism. Meanwhile, the long-term forecasts results from this study alsoindicate that the TFC forecasts may be too optimistic. Finally, from theforecasts outputs, this study found that the Australian domestic tourism ison the declining stage. Psillakis, Panagopoulos and Kanellopoulos (2009)forecasted tourism demand in accommodation industry in Greece using370
TOURISMOS: AN INTERNATIONAL MULTIDISCIPLINARY JOURNAL OF TOURISMVolume 7, Number 1, Spring-Summer 2012, pp. 367-381UDC: 338.48+640(050)implemented forecasting model and Box-Jenkins approach. Bothapproaches used in this study have its own strength, where the forecastedvalues indicate expected value of the predictor of a future time seriesvalue. Botti et al. (2007) also used a simple econometric model to studythe demand for tourism in France, and concluded that tourism demand inFrance is dependent on available income and relative prices.Unlike most of the tourism forecasting studies discussed earlier,forecasting expo demand involves both qualitative technique andquantitative forecasting models (Lee, Song & Mjelde, 2008). The mainreason to use both techniques is because of the limitation of dataavailability. Combining quantitative technique with willingness-to-visit(WTV) surveys, the number of visitors expected to visit internationaltourism expo to be held in Korea in 2012 is predicted. Preez and Witt(2003) compared two types of methods to analyze tourism forecasting. Intheir study, univariate and multivariate modelling were used separately toforecast tourism demand from four European countries to Seychelles. Thefindings of the study revealed that the univariate forecasting modelsoutperform compare to multivariate models. The empirical result showsthat ARIMA estimation exhibits better forecasting performance thanunivariate and multivariate state space modelling. According to Kim andWong (2006), the volatility in tourism demand data can be influenced bythe effects of new shocks such as economic crises, natural disaster or war.In tourism literature, modelling the volatility in tourism demand isimportant because it can capture the occurrence of unexpected events.Volatility of tourism demand is modelled using conditional volatilitymodels, and the models that appears in tourism literature are univariategeneralized autoregressive conditional heterokedasticity (GARCH),univariate asymmetric GARCH, vector autoregressive moving averageGARCH (VARMA-GARCH); and VARMA asymmetry GARCH(VARMA-AGARCH) models (Chan, Lim & McAleer (2005); Kim &Wong (2006); Shareef & McAleer (2005); Shareef & McAleer (2007).In middle of 1990s, dynamic specification such as vector autoregressivemodel (VAR), error correction model (ECM) andautoregressive distributed lag model (ADLM) began to appear in thetourism literature. VAR model is able to accommodate various types ofindependent variables to determine tourist arrivals and from there we areable to forecast future tourist arrivals. Besides that, VAR model enablesinnovative use of impulse response analysis in tourism context, besidesproviding results on co-integrating analysis and forecasting. Song andWitt (2004) used VAR model to forecast international tourist flows toMacau for the period from 2003 to 2008. The forecast generated by VAR371
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