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Density tourism demand forecasting revisited

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  • Song, Haiyan
  • Wen, Long
  • Liu, Chang

Abstract

This study used scoring rules to evaluate density forecasts generated by different time-series models. Based on quarterly tourist arrivals to Hong Kong from ten source markets, the empirical results suggest that density forecasts perform better than point forecasts. The seasonal autoregressive integrated moving average (SARIMA) model was found to perform best among the competing models. The innovation state space models for exponential smoothing and the structural time-series models were significantly outperformed by the SARIMA model. Bootstrapping improved the density forecasts, but only over short time horizons.

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  • Song, Haiyan & Wen, Long & Liu, Chang, 2019. "Density tourism demand forecasting revisited," Annals of Tourism Research, Elsevier, vol. 75(C), pages 379-392.
    • Handle: RePEc:eee:anture:v:75:y:2019:i:c:p:379-392
    DOI: 10.1016/j.annals.2018.12.019
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    5. Nyoni, Thabani, 2019. "Sri Lanka – the wonder of Asia: analyzing monthly tourist arrivals in the post-war era," MPRA Paper 96790, University Library of Munich, Germany.

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