Forecast by mixed-frequency dynamic panel model

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

Annals of Tourism Research, 2025, vol. 110, issue C

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.

Keywords: Tourism demand; Forecasting; Mixed-frequency; Dynamic panel; Machine learning; Heterogeneity (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:anture:v:110:y:2025:i:c:s0160738324001646

DOI: 10.1016/j.annals.2024.103887

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