Predictivity of tourism demand data
Abstract: As tourism researchers continue to search for solutions to determine the best possible forecasting performance, it is important to understand the maximum predictivity achieved by models, as well as how various data characteristics influence the maximum predictivity. Drawing on information theory, the predictivity of tourism demand data is quantitatively evaluated and beneficial for improving the performance of tourism demand forecasting. Empirical results from Hong Kong tourism demand data show that 1) the predictivity could largely help the researchers estimate the best possible forecasting performance and understand the influence of various data characteristics on the forecasting performance.; 2) the predictivity can be used to assess the short effect of external shock - such as SARS over tourism demand forecasting
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
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Preprint
begutachtet (peer reviewed)
In: Annals of Tourism Research (2021) 89
- Classification
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Wirtschaft
- Event
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Veröffentlichung
- (where)
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Mannheim
- (who)
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SSOAR, GESIS – Leibniz-Institut für Sozialwissenschaften e.V.
- (when)
-
2021
- Creator
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Zhang, Yishou
Li, Gang
Muskat, Birgit
Vu, Quan Huy
Law, Rob
- DOI
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10.1016/j.annals.2021.103234
- URN
-
urn:nbn:de:0168-ssoar-75518-2
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:30 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Zhang, Yishou
- Li, Gang
- Muskat, Birgit
- Vu, Quan Huy
- Law, Rob
- SSOAR, GESIS – Leibniz-Institut für Sozialwissenschaften e.V.
Time of origin
- 2021
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