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Tourism forecast combination using the stochastic frontier analysis technique

Author

Listed:
  • Ji Wu

    (26469Sun Yat-sen University, China)

  • Xian Cheng

    (56711Southwest Jiaotong University, China)

  • Stephen Shaoyi Liao

    (53025City University of Hong Kong, China)

Abstract

Forecast combination has received a great deal of attention in the tourism domain. In this article, we propose a novel performance-based tourism forecast combination model by applying a multiple-criteria decision-making framework and the stochastic frontier analysis technique to determine combination weights for individual tourism forecast models. Thirteen time-series models are used to generate individual forecast tourism models, and five competing forecast combination models are selected to evaluate the forecast performance. Using the tourism forecast competition data set, we conclude that the proposed combination model significantly and statistically outperforms the five competing combination models in most cases based on multiple performance indicators. Our results show that the proposed model offers a good solution to identify optimal weights for individual tourism forecast models.

Suggested Citation

  • Ji Wu & Xian Cheng & Stephen Shaoyi Liao, 2020. "Tourism forecast combination using the stochastic frontier analysis technique," Tourism Economics, , vol. 26(7), pages 1086-1107, November.
    • Handle: RePEc:sae:toueco:v:26:y:2020:i:7:p:1086-1107
    DOI: 10.1177/1354816619868089
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    References listed on IDEAS

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    Cited by:

    1. Xi Wu & Adam Blake, 2023. "Does the combination of models with different explanatory variables improve tourism demand forecasting performance?," Tourism Economics, , vol. 29(8), pages 2032-2056, December.
    2. Xi Wu & Adam Blake, 2023. "The Impact of the COVID-19 Crisis on Air Travel Demand: Some Evidence From China," SAGE Open, , vol. 13(1), pages 21582440231, January.
    3. Yi-Chung Hu, 2023. "Tourism combination forecasting using a dynamic weighting strategy with change-point analysis," Current Issues in Tourism, Taylor & Francis Journals, vol. 26(14), pages 2357-2374, July.
    4. Mingwei Li & Bingxue Shao & Xiasheng Shi, 2022. "Impact of High-Speed Rail on the Development Efficiency of Low-Carbon Tourism: A Case Study of an Agglomeration in China," Sustainability, MDPI, vol. 14(16), pages 1-16, August.

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