Forecasting Chinese outbound tourism recovery: A Triple-layer forecast combination framework

Zhang, Hanyuan, Liu, Ying, Liu, Xinyang, Liu, Anyu and Lin, Vera Shanshan (2026) Forecasting Chinese outbound tourism recovery: A Triple-layer forecast combination framework. Annals of Tourism Research, 116. ISSN 0160-7383

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Abstract

Forecast combinations became particularly significant in the post-pandemic era due to heightened uncertainty. This study introduces a Triple-layer Forecast Combination Framework to predict Chinese outbound tourism recovery from August 2023 to July 2024 across 20 destinations. The framework integrates baseline quantitative models, expert-based model selection, and real-time judgmental adjustments to enhance forecast accuracy in post-crisis contexts. Results show Chinese visitor arrivals rebounding, on average, to 80% of July 2019 levels by mid-2024, with East and Southeast Asia—particularly Hong Kong SAR, Macao SAR, and Thailand—recovering faster than long-haul markets such as Hawaii, Canada, and the Czech Republic. By combining statistical rigor with contextual insight, the framework supports replicable, adaptive forecasting under uncertainty for tourism recovery planning.

Item Type:	Article
Additional Information:	Data availability: The authors do not have permission to share data.
Uncontrolled Keywords:	forecast combination,delphi method,judgmental adjustments,chinese outbound tourism,recovery pattern,4* ,/dk/atira/pure/researchoutput/REFrank/4_
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 12:31
Last Modified:	02 Jun 2026 07:51
URI:	https://ueaeprints.uea.ac.uk/id/eprint/103148
DOI:	10.1016/j.annals.2025.104079

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