Forecasting tourist arrivals: Google Trends meets mixed-frequency data
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Tomas Havranek [1] ; Ayaz Zeynalov [2]
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[1] Charles University in Prague
Charles University in Prague
Chequia
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[2] University of Economics Prague
University of Economics Prague
Chequia
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- Localización: Tourism economics: the business and finance of tourism and recreation, ISSN 1354-8166, Vol. 27, Nº. 1, 2021, págs. 129-148
- Idioma: inglés
- Texto completo no disponible (Saber más ...)
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Resumen
In this article, we examine the usefulness of Google Trends data in predicting monthly touristarrivals and overnight stays in Prague during the period between January 2010 and December2016. We offer two contributions. First, we analyze whether Google Trends provides significantforecasting improvements over models without search data. Second, we assess whether a high-frequency variable (weekly Google Trends) is more useful for accurate forecasting than a low-frequency variable (monthly tourist arrivals) using mixed-data sampling (MIDAS). Our resultssuggest the potential of Google Trends to offer more accurate predictions in the context oftourism: we find that Google Trends information, both 2 months and 1 week ahead of arrivals, isuseful for predicting the actual number of tourist arrivals. The MIDAS forecasting model employingweekly Google Trends data outperforms models using monthly Google Trends data and modelswithout Google Trends data.
