Forecasting turning points in tourism growth

Shui Ki WAN, Haiyan Song*

*Corresponding author for this work

    Research output: Contribution to journalJournal articlepeer-review

    39 Citations (Scopus)

    Abstract

    Tourism demand exhibits growth cycles, and it is important to forecast turning points in these growth cycles to minimise risks to destination management. This study estimates logistic models of Hong Kong tourism demand, which are then used to generate both short- and long-term forecasts of tourism growth. The performance of the models is evaluated using the quadratic probability score and hit rates. The results show that the ways in which this information is used are crucial to the models’ predictive power. Further, we investigate whether combining probability forecasts can improve predictive accuracy, and find that combination approaches, especially nonlinear combination approaches, are sensitive to the quality of forecasts in the pool. In addition, model screening can improve forecasting performance.

    Original languageEnglish
    Pages (from-to)156-167
    Number of pages12
    JournalAnnals of Tourism Research
    Volume72
    DOIs
    Publication statusPublished - Sept 2018

    Scopus Subject Areas

    • Development
    • Tourism, Leisure and Hospitality Management

    User-Defined Keywords

    • Combined probability forecast
    • Hong Kong
    • Quadratic probability score
    • Tourism demand

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