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Forecast by mixed-frequency dynamic panel model
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DOI：10.1016/j.annals.2024.103887.png)
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摘要
En 中文
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.
Keyword:
Tourism demand
Forecasting
Mixed-frequency
Dynamic panel
Machine learning
Heterogeneity
期刊
IF:
7.8
论文数:
4.5K
被引数:
1.8W
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