Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/104879
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Title: Can bagging improve the forecasting performance of tourism demand models?
Authors: Song, H 
Witt, SF
Qiu, RT 
Issue Date: 2017
Source: In Kreinovich, V., Sriboonchitta, S., Huynh, V. N. (Eds. ), Robustness in Econometrics, p. 419–433. Cham: Springer, 2017
Abstract: This study examines the forecasting performance of the general-to-specific (GETS) models developed for Hong Kong through the bootstrap aggregating method (known as bagging). Although the literature in other research areas shows that bagging can improve the forecasting performance of GETS models, the empirical analysis in this study does not confirm this conclusion. This study is the first attempt to apply bagging to tourism forecasting, but additional effort is needed to examine the effectiveness of bagging in tourism forecasting by extending the models to cover more destination-source markets related to destinations other than Hong Kong.
Keywords: Bagging
General-to-specific modeling
Hong Kong
Tourism demand
Publisher: Springer
Journal: Studies in computational intelligence 
ISBN: 978-3-319-50741-5 (Hardcover ISBN)
978-3-319-84480-0 (Softcover ISBN)
978-3-319-50742-2 (eBook ISBN)
ISSN: 1860-949X
DOI: 10.1007/978-3-319-50742-2_25
Rights: © Springer International Publishing AG 2017
This version of the book chapter has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-319-50742-2_25.
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