Google Trends and reality: Do the proportions match?
Boriss Siliverstovs and
Daniel S. Wochner
Journal of Economic Behavior & Organization, 2018, vol. 145, issue C, 1-23
This study examines the extent to which real-world economic activity is reflected in aggregate online search behavior on Google Search. As opposed to previous studies, being subject to potential mismeasurement problems when examining search queries along their longitudinal dimension, we apply an alternative investigative approach that exploits the cross-sectional instead of the longitudinal informational content embodied in Googles data. Moreover, while previous studies most often examine a single Google Trends series, our analyses are based on over 60 distinct series, which allow us to assess how informative the data are, not only within each series, but also between series. Finally, our Google Trends indices are based on the recently launched Google Knowledge Graph technology, allowing for a remarkably accurate measurement of relevant search query volumes. We assess the informational value of the data as strong, semi-strong, or weak based on unbiasedness and efficiency considerations in a Mincer–Zarnowitz-type regression model. Here, the context of (Swiss) tourism demand proves particularly useful, and we find that search-based tourism demand predictions are, on average, highly accurate approximations of reality. This indicates that search-based indicators may serve as valuable real-time complements for the guidance of economic policy.
Keywords: Online search data; Google Trends; Predictive accuracy; Nowcasting; Tourism demand; Switzerland (search for similar items in EconPapers)
JEL-codes: C80 E29 Z30 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jeborg:v:145:y:2018:i:c:p:1-23
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