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Nowcasting Russia’s key macroeconomic variables using machine learning

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  • M. Y. Gareev
  • A. V. Polbin

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.

Suggested Citation

  • M. Y. Gareev & A. V. Polbin, 2022. "Nowcasting Russia’s key macroeconomic variables using machine learning," Voprosy Ekonomiki, NP Voprosy Ekonomiki, issue 8.
    • Handle: RePEc:nos:voprec:y:2022:id:4093
    DOI: 10.32609/0042-8736-2022-8-133-157
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    Cited by:

    1. Alexandra Bozhechkova & Urmat Dzhunkeev, 2024. "CLARA and CARLSON: Combination of Ensemble and Neural Network Machine Learning Methods for GDP Forecasting," Russian Journal of Money and Finance, Bank of Russia, vol. 83(3), pages 45-69, September.
    2. Anastasia Mogilat & Oleg Kryzhanovskiy & Zhanna Shuvalova & Yaroslav Murashov, 2024. "DYFARUS: Dynamic Factor Model to Forecast GDP by Output Using Input-Output Tables," Russian Journal of Money and Finance, Bank of Russia, vol. 83(2), pages 3-25, June.
    3. Stankevich, Ivan, 2023. "Application of Markov-Switching MIDAS models to nowcasting of GDP and its components," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 70, pages 122-143.
    4. Anastasiia Pankratova, 2024. "Forecasting Key Macroeconomic Indicators Using DMA and DMS Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 83(1), pages 32-52, March.

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