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TOURIST ARRIVAL FORECAST AMID COVID-19: A perspective from the Asia and Pacific team

Author

Listed:
  • Richard T.R. Qiu
  • Doris Chenguang Wu
  • Vincent Dropsy
  • Sylvain Petit

    (UPF - Université de la Polynésie Française, LARSH - Laboratoire de Recherche Sociétés & Humanités - UPHF - Université Polytechnique Hauts-de-France - INSA Hauts-De-France - INSA Institut National des Sciences Appliquées Hauts-de-France - INSA - Institut National des Sciences Appliquées)

  • Stephen Pratt
  • Yasuo Ohe

Abstract

It is important to provide scientific assessments concerning the future of tourism under the uncertainty surrounding COVID-19. To this purpose, this paper presents a two-stage three-scenario forecast framework for inbound-tourism demand across 20 countries. The main findings are as follows: in the first-stage ex-post forecasts, the stacking-based algorithms are more accurate and robust, especially when combining five single models. The second-stage ex-ante forecasts are based on three recovery scenarios: a mild case assuming a V-shape recovery, a medium one with a V/U-shape, and a severe one with an L-shape. The forecast results show a wide range of recovery (10%-70%) in 2021 compared to 2019. This two-stage three-scenario framework contributes to improvement in the accuracy and robustness of tourism demand forecasting.

Suggested Citation

  • Richard T.R. Qiu & Doris Chenguang Wu & Vincent Dropsy & Sylvain Petit & Stephen Pratt & Yasuo Ohe, 2021. "TOURIST ARRIVAL FORECAST AMID COVID-19: A perspective from the Asia and Pacific team," Post-Print hal-03138092, HAL.
    • Handle: RePEc:hal:journl:hal-03138092
    DOI: 10.1016/j.annals.2021.103155
    Note: View the original document on HAL open archive server: https://hal.science/hal-03138092
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    References listed on IDEAS

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    Cited by:

    1. Fangming Qin & Gezhi Chen, 2022. "Vulnerability of Tourist Cities’ Economic Systems Amid the COVID-19 Pandemic: System Characteristics and Formation Mechanisms—A Case Study of 46 Major Tourist Cities in China," Sustainability, MDPI, vol. 14(5), pages 1-18, February.
    2. Li, Cheng & Zheng, Weimin & Ge, Peng, 2022. "Tourism demand forecasting with spatiotemporal features," Annals of Tourism Research, Elsevier, vol. 94(C).

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