Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/187420
Authors: 
Havranek, Tomas
Zeynalov, Ayaz
Year of Publication: 
2018
Abstract: 
In this paper, we examine the usefulness of Google Trends data in predicting monthly tourist arrivals and overnight stays in Prague during the period between January 2010 and December 2016. We offer two contributions. First, we analyze whether Google Trends provides significant forecasting 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 results stress the potential of Google Trends to offer more accurate prediction in the context of tourism: we find that Google Trends information, both two months and one week ahead of arrivals, is useful for predicting the actual number of tourist arrivals. The MIDAS forecasting model that employs weekly Google Trends data outperforms models using monthly Google Trends data and models without Google Trends data.
Subjects: 
Google trends
mixed-frequency data
forecasting
tourism
JEL: 
C53
L83
Z32
Document Type: 
Working Paper

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