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Visitor arrivals forecasts amid COVID-19: A perspective from the Africa team

Author

Listed:
  • Nikolaos Kourentzes

    (LRCF - Lancaster Research Centre for Forecasting (Lancaster University Management School, Department of Management Science))

  • Andrea Saayman

  • Philippe Jean-Pierre

    (IAE La Réunion - Institut d'Administration des Entreprises - La Réunion - UR - Université de La Réunion)

  • Davide Provenzano
  • Mondher Sahli

    (Victoria University of Wellington)

  • Neelu Seetaram

    (University of Huddersfield)

  • Serena Volo

Abstract

COVID-19 disrupted international tourism worldwide, subsequently presenting forecasters with a challenging conundrum. In this competition, we predict international arrivals for 20 destinations in two phases: (i) Ex post forecasts pre-COVID; (ii) Ex ante forecasts during and after the pandemic up to end 2021. Our results show that univariate combined with cross-sectional hierarchical forecasting techniques (THieF-ETS) outperform multivariate models pre-COVID. Scenarios were developed based on judgemental adjustment of the THieF-ETS baseline forecasts. Analysts provided a regional view on the most likely path to normal, based on country-specific regulations, macroeconomic conditions, seasonal factors and vaccine development. Results show an average recovery of 58% compared to 2019 tourist arrivals in the 20 destinations under the medium scenario; severe, it is 34% and mild, 80%.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Nikolaos Kourentzes & Andrea Saayman & Philippe Jean-Pierre & Davide Provenzano & Mondher Sahli & Neelu Seetaram & Serena Volo, 2021. "Visitor arrivals forecasts amid COVID-19: A perspective from the Africa team," Post-Print hal-03286786, HAL.
    • Handle: RePEc:hal:journl:hal-03286786
    DOI: 10.1016/j.annals.2021.103197
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    Cited by:

    1. Xi Wu & Adam Blake, 2023. "Does the combination of models with different explanatory variables improve tourism demand forecasting performance?," Tourism Economics, , vol. 29(8), pages 2032-2056, December.
    2. Pritularga, Kandrika F. & Svetunkov, Ivan & Kourentzes, Nikolaos, 2021. "Stochastic coherency in forecast reconciliation," International Journal of Production Economics, Elsevier, vol. 240(C).
    3. Trinh, Vu Quang & Seetaram, Neelu, 2022. "Top-management compensation and survival likelihood: the case of tourism and leisure firms in the US," Annals of Tourism Research, Elsevier, vol. 92(C).
    4. Sagaert, Yves R. & Kourentzes, Nikolaos, 2025. "Inventory management with leading indicator augmented hierarchical forecasts," Omega, Elsevier, vol. 136(C).
    5. 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).
    6. Li, Hengyun & Guo, Honggang & Wang, Jianzhou & Wang, Yong & Wu, Chunying, 2025. "Tourism combination forecasting with swarm intelligence," Annals of Tourism Research, Elsevier, vol. 111(C).
    7. Marcus Roller, 2022. "Pre-Crisis Determinants of Tourism Resilience," Diskussionsschriften credresearchpaper39, Universitaet Bern, Departement Volkswirtschaft - CRED.
    8. Athanasopoulos, George & Hyndman, Rob J. & Kourentzes, Nikolaos & Panagiotelis, Anastasios, 2024. "Forecast reconciliation: A review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 430-456.
    9. 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.
    10. Hanyuan Zhang & Jiangping Lu, 2022. "Forecasting hotel room demand amid COVID-19," Tourism Economics, , vol. 28(1), pages 200-221, February.
    11. Li, Cheng & Zheng, Weimin & Ge, Peng, 2022. "Tourism demand forecasting with spatiotemporal features," Annals of Tourism Research, Elsevier, vol. 94(C).
    12. Cindy Yoonjoung Heo & Luciano Viverit & Luís Nobre Pereira, 2024. "Does historical data still matter for demand forecasting in uncertain and turbulent times? An extension of the additive pickup time series method for SME hotels," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(1), pages 39-43, February.
    13. Athanasopoulos, George & Kourentzes, Nikolaos, 2023. "On the evaluation of hierarchical forecasts," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1502-1511.
    14. Manuel González-Gómez, 2022. "European outbound tourism expansion on the islands of Cape Verde," Tourism Economics, , vol. 28(4), pages 1129-1150, June.
    15. 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.
    16. 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).
    17. Davide Provenzano & Serena Volo, 2022. "Tourism recovery amid COVID-19: The case of Lombardy, Italy," Tourism Economics, , vol. 28(1), pages 110-130, February.
    18. Martin Henseler & Hélène Maisonnave & Asiya Maskaeva, 2021. "Economic impacts of COVID-19 on the tourism sector in Tanzania," Working Papers hal-03501722, HAL.
    19. George Athanasopoulos & Nikolaos Kourentzes, 2021. "On the Evaluation of Hierarchical Forecasts," Monash Econometrics and Business Statistics Working Papers 10/21, Monash University, Department of Econometrics and Business Statistics.
    20. 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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