Show Less

Handbook of Research Methods in Tourism

Edited by Larry Dwyer, Alison Gill and Neelu Seetaram

This insightful book explores the most important established and emerging qualitative and quantitative research methods in tourism. The authors provide a detailed overview of the nature of the research method, its use in tourism, the advantages and limitations, and future directions for research.
Buy Book in Print
Show Summary Details
You do not have access to this content

Chapter 4: Demand Modeling and Forecasting

Grace Bo Peng, Haiyan Song and Stephen F. Witt


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 called extrapolative methods, and only...

You are not authenticated to view the full text of this chapter or article.

Elgaronline requires a subscription or purchase to access the full text of books or journals. Please login through your library system or with your personal username and password on the homepage.

Non-subscribers can freely search the site, view abstracts/ extracts and download selected front matter and introductory chapters for personal use.

Your library may not have purchased all subject areas. If you are authenticated and think you should have access to this title, please contact your librarian.

Further information

or login to access all content.