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Title: Bayesian bootstrap aggregation for tourism demand forecasting
Authors: Song, H 
Liu, A
Li, G
Liu, X
Issue Date: Sep-2021
Source: International journal of tourism research, Sept-Oct. 2021, v. 23, no. 5, p. 914-927
Abstract: Limited 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.
Keywords: Bagging
Tourism demand
Publisher: John Wiley & Sons
Journal: International journal of tourism research 
ISSN: 1099-2340
EISSN: 1522-1970
DOI: 10.1002/jtr.2453
Rights: © 2021 John Wiley & Sons Ltd.
This 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 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.
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