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Analysis of tourism demand serial dependence structure for forecasting

Author

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  • Liang Zhu

    (Nanyang Technological University, Singapore)

  • Christine Lim

    (Nanyang Technological University, Singapore)

  • Wenjun Xie

    (Nanyang Technological University, Singapore)

  • Yuan Wu

    (Nanyang Technological University, Singapore)

Abstract

This study aims to extend knowledge of serial dependence structure in tourism demand modelling and make a contribution to tourism forecasting with the use of copula method. Analysis of serial dependence can reveal the impact of current tourism demand on the future. This is important for tourism demand forecasting, as the prediction of future tourism demand relies highly on the historical demand information. However, serial dependence, especially its structure, has received very little attention in previous tourism research. The copula method is flexible as it provides various functions to specify different serial dependence structures and allows arbitrary distributions of tourism demand. We used five types of copulas to analyse two-dimensional serial dependence structure for 10 arrivals series to Singapore. The empirical findings show that serial dependence structures of arrivals can be non-linear. Additionally, the Student- t copula generates forecasts of tourism demand with higher accuracy than the autoregressive integrated moving average (ARIMA) and seasonal ARIMA models.

Suggested Citation

  • Liang Zhu & Christine Lim & Wenjun Xie & Yuan Wu, 2017. "Analysis of tourism demand serial dependence structure for forecasting," Tourism Economics, , vol. 23(7), pages 1419-1436, November.
    • Handle: RePEc:sae:toueco:v:23:y:2017:i:7:p:1419-1436
    DOI: 10.1177/1354816617693964
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    References listed on IDEAS

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    1. Chih-Yuan Lin & Mateus Lee, 2020. "Taiwan’s opening policy to Chinese tourists and cross-strait relations: The impacts on inbound tourism into Taiwan," Tourism Economics, , vol. 26(1), pages 27-44, February.

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