Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/89707
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
Bayesian
Forecasting
General-to-specific
Tourism demand
Publisher: John Wiley & Sons
Journal: International journal of tourism research 
ISSN: 1099-2340
EISSN: 1522-1970
DOI: 10.1002/jtr.2453
Appears in Collections:Journal/Magazine Article

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Embargo End Date 2023-10-31
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