Constructing Daily Economic Sentiment Indices Based on Google Trends
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Date
2020-06
Publication Type
Working Paper
ETH Bibliography
yes
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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.
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published
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Journal / series
Volume
484
Pages / Article No.
Publisher
KOF Swiss Economic Institute, ETH Zurich
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Edition / version
Methods
Software
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Date collected
Date created
Subject
Google Trends; measurement; high frequency; forecasting; Covid-19
Organisational unit
02525 - KOF Konjunkturforschungsstelle / KOF Swiss Economic Institute
03716 - Sturm, Jan-Egbert / Sturm, Jan-Egbert