Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/89707
PIRA download icon_1.1View/Download Full Text
DC FieldValueLanguage
dc.contributorSchool of Hotel and Tourism Managementen_US
dc.creatorSong, Hen_US
dc.creatorLiu, Aen_US
dc.creatorLi, Gen_US
dc.creatorLiu, Xen_US
dc.date.accessioned2021-05-05T04:56:55Z-
dc.date.available2021-05-05T04:56:55Z-
dc.identifier.issn1099-2340en_US
dc.identifier.urihttp://hdl.handle.net/10397/89707-
dc.language.isoenen_US
dc.publisherJohn Wiley & Sonsen_US
dc.rights© 2021 John Wiley & Sons Ltd.en_US
dc.rightsThis is the peer reviewed version of the following article: Song, H, Liu, A, Li, G, Liu, X. Bayesian bootstrap aggregation for tourism demand forecasting. Int J Tourism Res. 2021; 23: 914–927, which has been published in final form at https://doi.org/10.1002/jtr.2453. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.en_US
dc.subjectBaggingen_US
dc.subjectBayesianen_US
dc.subjectForecastingen_US
dc.subjectGeneral-to-specificen_US
dc.subjectTourism demanden_US
dc.titleBayesian bootstrap aggregation for tourism demand forecastingen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage914en_US
dc.identifier.epage927en_US
dc.identifier.volume23en_US
dc.identifier.issue5en_US
dc.identifier.doi10.1002/jtr.2453en_US
dcterms.abstractLimited historical data are the primary cause of the failure of tourism forecasts. Bayesian bootstrap aggregation (BBagging) may offer a solution to this problem. This study is the first to apply BBagging to tourism demand forecasting. An analysis of annual and quarterly tourism demand for Hong Kong shows that BBagging can, in general, improve the forecasting accuracy of the econometric models obtained using the general-to-specific (GETS) approach by reducing, relative to the ordinary bagging method, the variability in the posterior distributions of the forecasts it generates.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationInternational journal of tourism research, Sept-Oct. 2021, v. 23, no. 5, p. 914-927en_US
dcterms.isPartOfInternational journal of tourism researchen_US
dcterms.issued2021-09-
dc.identifier.scopus2-s2.0-85104543023-
dc.identifier.eissn1522-1970en_US
dc.description.validate202105 bcwhen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera0876-n01-
dc.identifier.SubFormID2091-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextP0013967en_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryGreen (AAM)en_US
Appears in Collections:Journal/Magazine Article
Files in This Item:
File Description SizeFormat 
Song_Bayesian_Bootstrap_Aggregation.pdfPre-Published version1.04 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Final Accepted Manuscript
Access
View full-text via PolyU eLinks SFX Query
Show simple item record

Page views

137
Last Week
1
Last month
Citations as of Apr 14, 2025

Downloads

76
Citations as of Apr 14, 2025

SCOPUSTM   
Citations

19
Citations as of May 8, 2025

WEB OF SCIENCETM
Citations

14
Citations as of Oct 10, 2024

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.