The demand side of information provision: Using multivariate time series clustering to construct multinational uncertainty proxies

Abstract

[EN] Information demand in the modern world is met to a huge extent by information supply from the search engine Google. Humans use the search engine to gather information which shall help to reduce perceived personal uncertainty about a specific subject. Google Trends is providing insights into this information demand in a timely manner and for a variety of different countries. In this paper, multinational Google Trends data and unsupervised learning techniques are used to construct meaningful country clusters resembling the economic, geographic and political relationships of the considered countries. Additionally, these clusters are stable over time. Under the assumption that an increase in Google search requests reflect elevated uncertainty, the cluster information is used to construct economic and political uncertainty time series for 43 different countries. This uncertainty index Granger causes quarterly GDP growth in more countries compared to an existing multinational uncertainty index proofing its usefulness in the field of forecasting. Furthermore, the new index is available up to a daily frequency and can be applied to additional countries and regions.