Tourism demand modelling and forecasting: modern econometric approaches

By S. Poukliakova. 2001

266 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Sociology and Political Science

How researchers see this paper

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

In Global and Planetary Change

1. L. Turner
2. S. Tephen
3. S. Ong
4. Shujie Shen
5. H. Aiyan
6. George Athanasopoulos
7. W. Naudé
8. Rob J Hyndman

In Sociology and Political Science

1. Haiyan Song
2. D. Wu
3. Gang Li
4. Shujie Shen
5. G. Crouch
6. Bo Peng
7. Stephen F. Witt
8. George Athanasopoulos

Citations in the corpus

• The Tourism Forecasting Competition (2011, background): “(5) In most cases, the random walk process is adequate for capturing the parameter changes in various economic models (see, for example, Bohara and Sauer, 1992; Kim, 1993; Greenslade and Hall, 1996; Song and Witt, 2000).”
• Tourism demand modelling and forecasting—A review of recent research (2008, methodology): “Although the explanatory variables included in the tourism demand models vary enormously with research objectives and researchers’ backgrounds, the employment of certain indicators as the measurement of tourism demand variables in modelling and forecasting tourism demand have been less controversial”
• Determinants of Tourist Arrivals in Africa: A Panel Data Regression Analysis (2005, background): “…of tourism demand and international tourism flows (Crouch, 1995; du Preez and Witt, 2003; Coshall, 2000; Smeral and Weber, 2000; Jud and Joseph, 1974; Divisekera, 2003; Eilat and Einav, 2003); and (5) forecasting tourism demand (Witt and Witt, 1992; Song et al, 2003; Song and Witt, 2000).”
• The impact of climate change on Caribbean tourism demand (2010, background): “Following Kim and Uysal (1997), Lathiras and Siriopoulos (1998), Lee, Var and Blaine (1996), Lim (1997) as well as Song and Witt (2000) the tourism demand model is assumed to take the following form: Arrit ¼ f Yit; PBt PSit ; PBt PCt ;Fit;EXit ; (1) where Arrit are total visitor arrivals from…”
• Tourism Destination Competitiveness: From Definition to Explanation? (2007, background): “This variable has been customary in econometric analyses of tourism demand where it tends to appear under a different name (viz. relative prices; see Song and Witt, 2000, p. 44).”
• Recent Developments in Econometric Modeling and Forecasting (2005, methodology): “Using the TVP approach, Li, Song, and Witt (2002); Song and Witt (2000); and Song and Wong (2003) confirmed the above findings in their empirical studies.”
• Statistical Testing in Forecasting Model Selection (2003, methodology): “The good performance of the TVP model in generating accurate 1-year-ahead forecasts but weaker performance over longer time horizons is consistent with previous tourism research findings (Song and Witt 2000).”
• Forecasting Tourism Using Univariate and Multivariate Structural Time Series Models (2001, background): “A further disadvantage is that current regression techniques are becoming increasingly complex.(2) This is of particular concern when a practitioner wishes to forecast many tourism series.”
• Combination forecasts of international tourism demand (2011, result): “Although previous empirical studies (e.g., Song & Witt, 2000; Song & Wong, 2003) suggest that the TVP model performs well in annual tourism demand forecasting, in this study, it was outperformed by most of the other models when quarterly data were involved.”
• Forecasting tourist arrivals using time-varying parameter structural time series models (2011, methodology): “The very good short-term forecasting performances of the TVP model, CSM and BSM are in line with the findings of past tourism forecasting studies, such as those of Song and Witt (2000), González and Moral (1995) and Turner and Witt (2001).”
• Tourism Demand Modeling: A Time-Varying Parameter Approach (2003, methodology): “The influencing factors and the measurement issues of the demand for international tourism were fully accounted in Song and Witt (2000).”
• Tourism Demand Modelling and Forecasting: How Should Demand Be Measured? (2010, background): “Additionally, marketing expenditure, consumer tastes, consumer expectations, habit persistence, origin population and one-off events are all potentially important factors that could be incorporated into the tourism demand model (Song and Witt, 2000).”
• Modelling and forecasting the demand for Thai tourism (2005, methodology): “It differs slightly from the substitute price index used in Song et al (2000), Song and Witt (2000) and Kulendran and Witt (2001), where it is measured by price in the destination relative to that in competing destinations.”
• Asymmetric impact of oil prices, exchange rate, and inflation on tourism demand in Pakistan: new evidence from nonlinear ARDL (2018, background): “Song and Witt (2000) argued that tourism demand can be expressed in terms of product mix which tourists desire to buy during a particular period which may further be influenced by some related factors used to determine tourism demand.”