Tourism Demand Forecasting in Normal and Crisis Times: Combining Bootstrap-Aggregating and Bayesian Approaches

Liu, Xinyang ORCID: https://orcid.org/0000-0001-6689-798X, Liu, Anyu, Chen, Jason Li, Li, Gang and Song, Haiyan (2026) Tourism Demand Forecasting in Normal and Crisis Times: Combining Bootstrap-Aggregating and Bayesian Approaches. Journal of Hospitality & Tourism Research, 50 (3). pp. 323-340.

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Abstract

Taking advantage of the merits of both the bootstrap-aggregating method and the Bayesian forecasting approach, this study introduces their combination—the BayesBag method—to the tourism forecasting literature for the first time. In this study, we examine whether the novel BayesBag method can improve the forecasting performance of the traditional Autoregressive-Distributed-Lag (ADL) model in both normal (i.e., pre-COVID-19) and crisis (i.e., during the pandemic) times. This is also the first study to incorporate the global travel sentiment index as a measure of visitors’ behavioral intentions for forecasting tourism demand in a crisis situation. We conduct both ex-post and ex-ante forecasting of European monthly tourism demand, and our empirical results show that the newly proposed BayesBag method outperforms other methods in both periods.

Item Type:	Article
Additional Information:	Publisher Copyright: © The Author(s) 2025. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
Uncontrolled Keywords:	tourism demand,autoregressive-distributed-lag,bootstrap aggregating,bayesian forecasting,crisis,3* ,/dk/atira/pure/researchoutput/REFrank/3_
Faculty \ School:	Faculty of Social Sciences > Norwich Business School
Related URLs:	https://www.scopus.com/pages/publication...
Depositing User:	LivePure Connector
Date Deposited:	26 May 2026 09:49
Last Modified:	18 Jun 2026 21:00
URI:	https://ueaeprints.uea.ac.uk/id/eprint/103143
DOI:	10.1177/10963480251313492

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