Edited by Larry Dwyer, Alison Gill and Neelu Seetaram
4 Demand modeling and forecasting Grace Bo Peng, Haiyan Song and Stephen F. Witt NATURE OF TOURISM DEMAND MODELING AND FORECASTING Tourism demand forecasting methods can be divided into two categories: qualitative and quantitative methods. Quantitative forecasting methods organize past tourism demand information by mathematical rules and there are three main subcategories: time series models, econometric approaches, and artificial intelligence methods. According to the complexities of the models and estimation techniques, the time series forecasting methods can be subdivided into basic and advanced time series models. Based on their temporal structure, econometric models can be grouped into two categories: static and dynamic models. Tourism demand is normally measured by either tourist arrivals in a destination, tourists’ expenditure when they visit a destination, or tourist nights stayed in the destination. The variables that have been generally accepted as the main determinants of international tourism demand comprise potential tourists’ income levels, the relative price of tourism products in the origin and destination countries, substitute prices of tourism products in alternative foreign destinations, transportation cost, population of origin country, exchange rates, marketing expenditure by the destination in the origin country, and one-off events (which can have a positive or negative effect). For a detailed review of tourism demand measures and their determinants, see, for example, Martin and Witt (1987, 1988); Song and Li (2008); Song et al. (2009); Witt and Martin (1987b); and Witt and Witt (1992, 1995). BASIC TIME SERIES METHODS Time series forecasting methods (see Tables 4.1 and 4.2) are also...
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