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Modelling tourist flow association for tourism demand forecasting

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  • Liang Zhu
  • Christine Lim
  • Wenjun Xie
  • Yuan Wu

Abstract

The purpose of this study is to examine tourism demand for Singapore from 1995 to 2013 by six major origin countries which belong to three different regions. Unlike prior tourism research, we take into account the dependence relations among the different tourist flows via copula. Copula is a statistical model of dependence and measurement of association. Specifically, we investigate the association between two tourist flows in each region. Based on empirical copula estimation, the Frank function has been identified as the most appropriate to capture the pairwise dependence structures of tourist flows. The copula-based approach combined with econometric models is proposed for tourism demand analysis that can be used to predict tourist arrivals. We apply the copula-ARDL and copula-ECM frameworks to generate joint forecasts of tourist arrivals from three regions. The findings show that the forecast performance of the Frank copula-based model outperforms the benchmark model which corresponds to the independence structure (no association) of tourist flows.

Suggested Citation

  • Liang Zhu & Christine Lim & Wenjun Xie & Yuan Wu, 2018. "Modelling tourist flow association for tourism demand forecasting," Current Issues in Tourism, Taylor & Francis Journals, vol. 21(8), pages 902-916, May.
    • Handle: RePEc:taf:rcitxx:v:21:y:2018:i:8:p:902-916
    DOI: 10.1080/13683500.2016.1218827
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    Cited by:

    1. Jianxin Zhang & Yuting Yan & Jinyue Zhang & Peixue Liu & Li Ma, 2023. "Investigating the Spatial-Temporal Variation of Pre-Trip Searching in an Urban Agglomeration," Sustainability, MDPI, vol. 15(14), pages 1-17, July.

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