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Visitor arrivals forecasts amid COVID-19: A perspective from the Asia and Pacific team

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  • Qiu, Richard T.R.
  • Wu, Doris Chenguang
  • Dropsy, Vincent
  • Petit, Sylvain
  • Pratt, Stephen
  • Ohe, Yasuo

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 models 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-shaped recovery, a medium one with a V/U-shaped, and a severe one with an L-shaped. The forecast results show a wide range of recovery (10%–70%) in 2021 compared to 2019. This two-stage three-scenario framework contributes to the improvement in the accuracy and robustness of tourism demand forecasting.

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  • Qiu, Richard T.R. & Wu, Doris Chenguang & Dropsy, Vincent & Petit, Sylvain & Pratt, Stephen & Ohe, Yasuo, 2021. "Visitor arrivals forecasts amid COVID-19: A perspective from the Asia and Pacific team," Annals of Tourism Research, Elsevier, vol. 88(C).
    • Handle: RePEc:eee:anture:v:88:y:2021:i:c:s0160738321000177
    DOI: 10.1016/j.annals.2021.103155
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    1. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    2. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737, September.
    3. Athanasopoulos, George & Hyndman, Rob J. & Song, Haiyan & Wu, Doris C., 2011. "The tourism forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 822-844, July.
    4. Witt, Stephen F. & Witt, Christine A., 1995. "Forecasting tourism demand: A review of empirical research," International Journal of Forecasting, Elsevier, vol. 11(3), pages 447-475, September.
    5. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    6. Jaganathan, Srihari & Prakash, P.K.S., 2020. "A combination-based forecasting method for the M4-competition," International Journal of Forecasting, Elsevier, vol. 36(1), pages 98-104.
    7. Li, Gang & Wu, Doris Chenguang & Zhou, Menglin & Liu, Anyu, 2019. "The combination of interval forecasts in tourism," Annals of Tourism Research, Elsevier, vol. 75(C), pages 363-378.
    8. Song, Haiyan & Qiu, Richard T.R. & Park, Jinah, 2019. "A review of research on tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 75(C), pages 338-362.
    9. Christine Lim, 1997. "An Econometric Classification and Review of International Tourism Demand Models," Tourism Economics, , vol. 3(1), pages 69-81, March.
    10. Yang, Yang & Zhang, Hongru & Chen, Xiang, 2020. "Coronavirus pandemic and tourism: Dynamic stochastic general equilibrium modeling of infectious disease outbreak," Annals of Tourism Research, Elsevier, vol. 83(C).
    Full references (including those not matched with items on IDEAS)

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    Cited by:

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    7. Michał Roman & Kumar Bhatta & Monika Roman & Prakash Gautam, 2021. "Socio-Economic Factors Influencing Travel Decision-Making of Poles and Nepalis during the COVID-19 Pandemic," Sustainability, MDPI, vol. 13(20), pages 1-16, October.
    8. Emrah Kocak & Fevzi Okumus & Mehmet Altin, 2023. "Global pandemic uncertainty, pandemic discussion and visitor behaviour: A comparative tourism demand estimation for the US," Tourism Economics, , vol. 29(5), pages 1225-1250, August.
    9. Ulrich Gunter, 2021. "Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests," Forecasting, MDPI, vol. 3(4), pages 1-36, November.
    10. Yi-Chung Hu, 2023. "Tourism combination forecasting using a dynamic weighting strategy with change-point analysis," Current Issues in Tourism, Taylor & Francis Journals, vol. 26(14), pages 2357-2374, July.
    11. Yasuo Ohe, 2023. "Exploring New Opportunities for Agritourism in the Post-COVID-19 Era," Agriculture, MDPI, vol. 13(6), pages 1-4, June.
    12. Liu, Ying & Wen, Long & Liu, Han & Song, Haiyan, 2024. "Predicting tourism recovery from COVID-19: A time-varying perspective," Economic Modelling, Elsevier, vol. 135(C).
    13. Kleanthis Natsiopoulos & Nickolaos G. Tzeremes, 2024. "ARDL: An R Package for ARDL Models and Cointegration," Computational Economics, Springer;Society for Computational Economics, vol. 64(3), pages 1757-1773, September.
    14. Davide Provenzano & Serena Volo, 2022. "Tourism recovery amid COVID-19: The case of Lombardy, Italy," Tourism Economics, , vol. 28(1), pages 110-130, February.
    15. Park, Eunhye & Park, Jinah & Hu, Mingming, 2021. "Tourism demand forecasting with online news data mining," Annals of Tourism Research, Elsevier, vol. 90(C).
    16. Yang, Yang & Fan, Yawen & Jiang, Lan & Liu, Xiaohui, 2022. "Search query and tourism forecasting during the pandemic: When and where can digital footprints be helpful as predictors?," Annals of Tourism Research, Elsevier, vol. 93(C).
    17. Doris Chenguang Wu & Shiteng Zhong & Richard T R Qiu & Ji Wu, 2022. "Are customer reviews just reviews? Hotel forecasting using sentiment analysis," Tourism Economics, , vol. 28(3), pages 795-816, May.
    18. Marcus Roller, 2022. "Pre-Crisis Determinants of Tourism Resilience," Diskussionsschriften credresearchpaper39, Universitaet Bern, Departement Volkswirtschaft - CRED.
    19. He, Ling-Yang & Li, Hui & Bi, Jian-Wu & Yang, Jing-Jing & Zhou, Qing, 2022. "The impact of public health emergencies on hotel demand - Estimation from a new foresight perspective on the COVID-19," Annals of Tourism Research, Elsevier, vol. 94(C).
    20. Zhang, Hanyuan & Qiu, Richard T.R. & Wen, Long & Song, Haiyan & Liu, Chang, 2023. "Has COVID-19 changed tourist destination choice?," Annals of Tourism Research, Elsevier, vol. 103(C).
    21. Xi Wu & Adam Blake, 2023. "The Impact of the COVID-19 Crisis on Air Travel Demand: Some Evidence From China," SAGE Open, , vol. 13(1), pages 21582440231, January.
    22. 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.
    23. Hu, Yi-Chung, 2023. "Air passenger flow forecasting using nonadditive forecast combination with grey prediction," Journal of Air Transport Management, Elsevier, vol. 112(C).
    24. Li, Hengyun & Gao, Huicai & Song, Haiyan, 2023. "Tourism forecasting with granular sentiment analysis," Annals of Tourism Research, Elsevier, vol. 103(C).

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