Modelling and forecasting the demand for Thai tourism

By H. Aiyan, S. Ong, S. Tephen, F. W. Itt. 2005

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Haiyan Song
2. D. Wu
3. Gang Li
4. Kevin K. F. Wong
5. Stephen F. Witt
6. Carl Bonham
7. Bo Peng
8. B. Fei

Citations in the corpus

• Forecasting of Hong Kong airport's passenger throughput (2014, methodology): “fractionally integrated ARMA (ARFIMA) and autoregressive distributed lag model (ADLM)), they are the useful techniques to forecast HKIA’s future passenger throughput as tourist demand can be accurately forecasted based on these techniques (Chu, 2008; Song et al., 2003).”
• Tourism Demand Modelling and Forecasting: How Should Demand Be Measured? (2010, methodology): “Following Song et al (2003) and Gallet and Braun (2001), the substitute price variable is defined as a weighted average index of the selected countries’/regions’ tourism prices, namely CPIjtPst = 5 Σ j=1 –––– wijt, EXjt where j = 1, 2, 3, 4 and 5 represent the five substitute destinations,…”
• Tourism demand modelling and forecasting—A review of recent research (2008, methodology): “ADLM was employed in such studies as Song et al (2003a) and Song et al (2003c).”
• The Impact of 9/11 and Other Terrible Global Events on Tourism in the United States and Hawaii (2006, background): “Examples of applications in tourism-demand forecasting include Dritsakis (2004), Kulendran and Witt (2003), and Song, Witt, and Li (2003).”
• Recent Developments in Econometric Modeling and Forecasting (2005, background): “Twelve studies focused on this particular tourism market (for example, Ashworth and Johnson 1990; Kulendran and Witt 2003b; Song, Romilly, and Liu 2000; Song, Witt, and Li 2003).”
• Tourism forecasting: To combine or not to combine? (2007, methodology): “Song et al (2003b) compared the forecasting performance of the ECM, ADLM, TVP and VAR models with those generated by the two univariate time series models in forecasting the demand for Denmark tourism and found that the TVP model generates the most accurate one-year-ahead forecasts.”
• Competitiveness Indicators in the Travel and Tourism Industry (2005, background): “Most empirical studies on tourism demand (Durbarry and Sinclair, 2003; Lim, 1997; Song et al, 2003) find that price has a significant impact on tourism demand.”
• Tourism Demand Forecasting: A Time Varying Parameter Error Correction Model (2006, methodology): “directional change and trend change forecasting (see, for example, Witt and Witt 1989, 1991, respectively). Another category of forecasting techniques—the econometric approach—has been the most widely used in tourism forecasting. Such modern econometric methods as the error correction model (ECM) an”