Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/235098 
Year of Publication: 
2020
Series/Report no.: 
KOF Working Papers No. 484
Publisher: 
ETH Zurich, KOF Swiss Economic Institute, Zurich
Abstract: 
Google Trends have become a popular data source for social science research. We show that for small countries or sub-national regions like U.S. states, underlying sampling noise in Google Trends can be substantial. The data may therefore be unreliable for time series analysis and is furthermore frequency-inconsistent: daily data differs from weekly or monthly data. We provide a novel sampling technique along with the R-package trendecon in order to generate stable daily Google search results that are consistent with weekly and monthly queries of Google Trends. We use this new approach to construct long and consistent daily economic indices for the (mainly) German-speaking countries Germany, Austria, and Switzerland. The resulting indices are significantly correlated with traditional leading indicators, with the advantage that they are available much earlier.
Subjects: 
Google Trends
measurement
high frequency
forecasting
Covid-19
JEL: 
E01
E32
E37
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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