Forecasting Tourist Arrivals with Google Trends and Mixed Frequency Data
Tomas Havranek () and
EconStor Preprints from ZBW - Leibniz Information Centre for Economics
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.
Keywords: Google trends; mixed-frequency data; forecasting; tourism (search for similar items in EconPapers)
JEL-codes: C53 L83 Z32 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-for, nep-ict and nep-tur
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:esprep:187420
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