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Forecast by mixed-frequency dynamic panel model

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  • Liu, Han
  • Chen, Yuxiu
  • Hu, Mingming
  • Chen, Jason Li

Abstract

This study presents a novel forecasting framework for panel tourism demand, utilizing a machine learning approach with mixed-frequency panel data—the first in tourism forecasting. The empirical results indicate that (a) our proposed approach, which leverages mixed-frequency panel data, significantly outperforms benchmark models in forecasting tourism demand by effectively capturing high-frequency consumer behavior information; (b) the successful capture of common information in panel data can offset the deviations brought about by individual countries' heterogeneity and improve the average accuracy of tourism demand forecasting; and (c) the machine learning approach through sparse-group least absolute shrinkage and selection operator addresses the collinearity issue in dynamic panel tourism demand forecasting and facilitates the identification of the time lag structure of influential variables.

Suggested Citation

  • Liu, Han & Chen, Yuxiu & Hu, Mingming & Chen, Jason Li, 2025. "Forecast by mixed-frequency dynamic panel model," Annals of Tourism Research, Elsevier, vol. 110(C).
    • Handle: RePEc:eee:anture:v:110:y:2025:i:c:s0160738324001646
    DOI: 10.1016/j.annals.2024.103887
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    References listed on IDEAS

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