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
Preprint
begutachtet (peer reviewed)
In: Annals of Tourism Research (2021) 89

Classification
Wirtschaft

Event
Veröffentlichung
(where)
Mannheim
(who)
SSOAR, GESIS – Leibniz-Institut für Sozialwissenschaften e.V.
(when)
2021
Creator
Zhang, Yishou
Li, Gang
Muskat, Birgit
Vu, Quan Huy
Law, Rob

DOI
10.1016/j.annals.2021.103234
URN
urn:nbn:de:0168-ssoar-75518-2
Rights
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Last update
25.03.2025, 1:52 PM CET

Data provider

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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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