Bayesian bootstrap aggregation for tourism demand forecasting

Liu, Allen ORCID: https://orcid.org/0000-0001-6689-798X, Song, Haiyan, Liu, Anyu and Li, Gang (2021) Bayesian bootstrap aggregation for tourism demand forecasting. International Journal of Tourism Research, 23 (5). pp. 914-927. ISSN 1522-1970

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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.

Item Type:	Article
Uncontrolled Keywords:	bayesian,bagging,forecasting,general-to-specific,tourism demand,geography, planning and development,transportation,tourism, leisure and hospitality management,nature and landscape conservation ,/dk/atira/pure/subjectarea/asjc/3300/3305
Faculty \ School:	Faculty of Social Sciences > Norwich Business School
Related URLs:	https://www.scopus.com/pages/publication...
Depositing User:	LivePure Connector
Date Deposited:	28 May 2026 13:14
Last Modified:	18 Jun 2026 21:00
URI:	https://ueaeprints.uea.ac.uk/id/eprint/103192
DOI:	10.1002/jtr.2453

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