Forecasting Russian Macroeconomic Indicators Based on Information from News and Search Queries

Filipp Ulyankin ()
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Filipp Ulyankin: Russian Presidential Academy of National Economy and Public Administration

Russian Journal of Money and Finance, 2020, vol. 79, issue 4, 75-97

Abstract: Modern economic literature features quite a number of various indices of economic activity. Some of them are based on consumer and business surveys ('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 release of macroeconomic statistics makes them suitable for nowcasting.

Keywords: text analysis; sentiment analysis; economic uncertainty index; data analysis; machine learning (search for similar items in EconPapers)
JEL-codes: C43 C55 C81 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:bkr:journl:v:79:y:2020:i:4:p:75-97

DOI: 10.31477/rjmf.202004.75

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