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Predicting Quarterly Hong Kong Tourism Demand Growth Rates, Directional Changes and Turning Points with Composite Leading Indicators

Author

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  • Nada Kulendran

    (School of Economics and Finance and Centre for Hospitality and Tourism, Faculty of Business and Law, Victoria University, PO Box 14428, MCMC Melbourne, Victoria 8001, Australia)

  • Kevin K.F. Wong

    (School of Hotel and Tourism Management, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong)

Abstract

This study predicts numerical demand growth rates, directional changes and turning points in the growth rate using the single input leading indicator model and assesses its forecasting performance with the ARIMA model and the no-change model. To assess the forecasting performance from the March quarter of 2004 to the December quarter of 2006, models are fitted to the growth rates of Hong Kong inbound tourism demand from selected tourism markets (Australia, Japan, the UK and the USA). Composite leading indicators for the single input leading indicator model are constructed from selected national leading and lagged indicators. To avoid false signals in turning points, a method is specified to identify the correct turning points in tourism demand growth rates. The prediction performance of these models is then examined, based on the mean absolute percentage error, directional change error and turning point error. A statistical procedure is considered to determine whether the actual and predicted directional changes and turning points are independent or associated.

Suggested Citation

  • Nada Kulendran & Kevin K.F. Wong, 2009. "Predicting Quarterly Hong Kong Tourism Demand Growth Rates, Directional Changes and Turning Points with Composite Leading Indicators," Tourism Economics, , vol. 15(2), pages 307-322, June.
    • Handle: RePEc:sae:toueco:v:15:y:2009:i:2:p:307-322
    DOI: 10.5367/000000009788254340
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    References listed on IDEAS

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    Cited by:

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    2. Candy Mei Fung Tang & Nada Kulendran, 2011. "A Composite Leading Indicator for the Hotel Industry," Tourism Economics, , vol. 17(3), pages 549-563, June.
    3. Mei Candy Fung & Mei Tang Fung & Nada Kulendran & Brian King & Matthew H.T. Yap, 2016. "Research Note: Using Demand Determinants to Anticipate Fluctuations in Hotel Occupancy," Tourism Economics, , vol. 22(1), pages 179-187, February.
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    5. Silva, Emmanuel Sirimal & Hassani, Hossein, 2022. "‘Modelling’ UK tourism demand using fashion retail sales," Annals of Tourism Research, Elsevier, vol. 95(C).
    6. Candy Mei Fung Tang & Brian King & Stephen Pratt, 2017. "Predicting hotel occupancies with public data," Tourism Economics, , vol. 23(5), pages 1096-1113, August.

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