Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/1660
| Title: | Effect of seasonality treatment on the forecasting performance of tourism demand models | Authors: | Shen, S Li, G Song, H |
Issue Date: | Dec-2009 | Source: | Tourism economics, Dec. 2009, v. 15, no. 4, p. 693-708 | Abstract: | This study provides a comprehensive comparison of the performance of the commonly used econometric and time-series models in forecasting seasonal tourism demand. The empirical study is carried out based on the demand for outbound leisure tourism by UK residents to seven destination countries: Australia, Canada, France, Greece, Italy, Spain and the USA. In the modelling exercise, the seasonality of the data is treated using the deterministic seasonal dummies, seasonal unit root test techniques and the unobservable component method. The empirical results suggest that no single forecasting technique is superior to the others in all situations. As far as overall forecast accuracy is concerned, the Johansen maximum likelihood error correction model outperforms the other models. The time-series models also show superior performance in dealing with seasonality. However, the time-varying parameter model performs relatively poorly in forecasting seasonal tourism demand. This empirical evidence suggests that the methods of seasonality treatment affect the forecasting performance of the models and that the pre-test for seasonal unit roots is necessary and can improve forecast accuracy. | Keywords: | Seasonality Tourism demand Forecasting Seasonal unit roots Econometric model Time-series model |
Publisher: | IP Publishing Ltd | Journal: | Tourism economics | ISSN: | 1354-8166 | EISSN: | 2044-0375 | Rights: | Copyright © 2009 IP Publishing Ltd. Reproduced by permission. The journal web site is located at www.ippublishing.com. |
| Appears in Collections: | Journal/Magazine Article |
Access
View full-text via PolyU eLinks 
Page views
110
Last Week
1
1
Last month
Citations as of May 28, 2023
Downloads
203
Citations as of May 28, 2023
WEB OF SCIENCETM
Citations
29
Last Week
0
0
Last month
0
0
Citations as of May 25, 2023
Google ScholarTM
Check
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.