Constructing daily economic sentiment indices based on Google Trends - Vera Eichenauer; Ronald Indergand; Isabel Martínez; Christoph Sax (ETH Zurich, KOF Swiss Economic Institute, 2020)
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.
hdl:20.500.11850/423817
Keywords: E01; E32; E37; 330; Google Trends; measurement; high frequency; forecasting; Covid-19