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Nowcasting Russia’s key macroeconomic variables using machine learning
https://doi.org/10.32609/0042-8736-2022-8-133-157
Abstract
The article developed a methodology for nowcasting and short-term forecasting key Russian macroeconomic aggregates: real GDP, consumption, investment, export, import, using machine learning methods: boosting, elastic net, and random forest. The set of predictors included indicators of the stock market, money market, surveys, world prices for resources, price indices, and other statistical indicators of different frequency, from daily to quarterly. Our approach makes available a detailed examination of the changes in forecasts with the flow of new information. For most of the considered variables, a monotonic non-deterioration of the forecast quality was obtained with an expansion of available information. Furthermore, machine learning methods have shown significant superiority in predictive performance over naive prediction. The considered methods within the framework of the pseudo-experiment quickly showed a strong drop in real GDP, household consumption, and other variables in the context of the spread of the COVID-19 pandemic in the 2nd and 3rd quarters of 2020.
Keywords
macroeconomics,
macroeconomic forecasting,
nowcasting,
machine learning,
boosting,
elastic net,
random forest,
big data,
Russia
JEL: C53, C55
About the Authors
M. Y. Gareev
Russian Presidential Academy of National Economy and Public Administration
Russian Federation
Russian Federation
Mikhail Y. Gareev
Moscow
A. V. Polbin
Russian Presidential Academy of National Economy and Public Administration; Gaidar Institute for Economic Policy
Russian Federation
Russian Federation
Andrey V. Polbin
Moscow
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For citations:
Gareev M.Y., Polbin A.V. Nowcasting Russia’s key macroeconomic variables using machine learning. Voprosy Ekonomiki. 2022;(8):133—157. (In Russ.) https://doi.org/10.32609/0042-8736-2022-8-133-157
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