Forecasting tourism demand with an improved mixed data sampling model

Wen, Long, Liu, Chang, Song, Haiyan and Liu, Han (2020) Forecasting tourism demand with an improved mixed data sampling model. Journal of Travel Research . 004728752090622. ISSN 0047-2875 (In Press)

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Search query data reflect users’ intentions, preferences and interests. The interest in using such data to forecast tourism demand has increased in recent years. The mixed data sampling (MIDAS) method is often used in such forecasting, but is not effective when moving average (MA) dynamics are involved. To investigate the relevance of the MA components in MIDAS models to tourism demand forecasting, an improved MIDAS model that integrates MIDAS and the seasonal autoregressive integrated moving average process is proposed. Its performance is tested by forecasting monthly tourist arrivals in Hong Kong from mainland China with daily composite indices constructed from a large number of search queries using the generalised dynamic factor model. The forecasting results suggest that this new model significantly outperforms the benchmark model. In addition, comparing the forecasts and nowcasts shows that the latter generally outperform the former.

Item Type: Article
Keywords: Tourism demand forecasting; MIDAS; Search query data; Generalised dynamic factor model; Nowcasts
Schools/Departments: University of Nottingham Ningbo China > Faculty of Humanities and Social Sciences > School of Economics
Identification Number:
Depositing User: Zhou, Elsie
Date Deposited: 13 Apr 2020 01:15
Last Modified: 13 Apr 2020 01:15

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