Abstract
Modern economic literature features quite a number of various indices of economic activity. Some of them are based on public opinion polls (‘manual’ indices), while others are based on unstructured data from the Internet (‘automatic’ indices). However, the question as to which of these approaches is the most effective remains open. In this paper, we compare several different indices of economic activity in terms of their explanatory and predictive power. We build ‘automatic’ indices using machine learning methods. Search queries, news articles and user comments under news posts from social media are used as source data. The analysis of the resulting indices of economic activity shows that the search and news indices Granger-cause ‘manual’ indices and also better explain and predict the set of macroeconomic variables selected for research. The good explanatory power of the current values of macroeconomic indicators by means of current indices of economic activity with a lag in the output of macroeconomic statistics makes them suitable for nowcasting.
Publisher
The Central Bank of the Russian Federation
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