Forecasting turning points in tourism growth

Shui Ki WAN, Haiyan Song*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 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 - Sep 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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