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Density forecasting for tourism demand

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  • Wan, Shui Ki
  • Song, Haiyan
  • Ko, David

Abstract

•Introduce usefulness of density forecasts for visitor arrival in risk management.•Test the appropriateness of model assumption with histogram-based method.•Apply the framework to ARDL model for Hong Kong inbound tourism demand.•Error in ARDL model for the demand growth rates of China exhibit fat tail.•Normality assumption for the growth rates model from Macau is appropriate.

Suggested Citation

  • Wan, Shui Ki & Song, Haiyan & Ko, David, 2016. "Density forecasting for tourism demand," Annals of Tourism Research, Elsevier, vol. 60(C), pages 27-30.
    • Handle: RePEc:eee:anture:v:60:y:2016:i:c:p:27-30
    DOI: 10.1016/j.annals.2016.05.012
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    References listed on IDEAS

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    1. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-883, November.
    2. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society.
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    Cited by:

    1. Song, Haiyan & Wen, Long & Liu, Chang, 2019. "Density tourism demand forecasting revisited," Annals of Tourism Research, Elsevier, vol. 75(C), pages 379-392.
    2. Assaf, A. George & Tsionas, Mike G., 2019. "Forecasting occupancy rate with Bayesian compression methods," Annals of Tourism Research, Elsevier, vol. 75(C), pages 439-449.
    3. 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.

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