Forecasting tourist arrivals: Google Trends meets mixed-frequency data

Tomas Havranek and Ayaz Zeynalov
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Ayaz Zeynalov: 197946University of Economics, Prague

Tourism Economics, 2021, vol. 27, issue 1, 129-148

Abstract: In this article, 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 suggest the potential of Google Trends to offer more accurate predictions in the context of tourism: we find that Google Trends information, both 2 months and 1 week ahead of arrivals, is useful for predicting the actual number of tourist arrivals. The MIDAS forecasting model employing weekly Google Trends data outperforms models using monthly Google Trends data and models without Google Trends data.

Keywords: forecasting; Google Trends; mixed-frequency data; tourism (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (15)

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Related works:
Working Paper: Forecasting Tourist Arrivals: Google Trends Meets Mixed Frequency Data (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:sae:toueco:v:27:y:2021:i:1:p:129-148

DOI: 10.1177/1354816619879584

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