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Nowcasting Russian GDP using forecast combination approach

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  • Michael Zhemkov

Abstract

This paper presents a forecast combination approach for short-term assessment of economic growth in Russia. Our method significantly expands the existing domestic academic literature and embraces most advanced nowcasting techniques. The key feature of our approach is forecasting the growth rates of components GDP by expenditure using scenario indicators and a huge set of high-frequency predictors. The total number of combined models is nearly 500. Comparing our approach with popular benchmark models over the period from January 2003 to December 2020, we conclude that our method has the highest accuracy of short-term GDP forecasting. The findings of this research may be useful for the development of monetary and communication policy in Russia.

Suggested Citation

  • Michael Zhemkov, 2021. "Nowcasting Russian GDP using forecast combination approach," International Economics, CEPII research center, issue 168, pages 10-24.
  • Handle: RePEc:cii:cepiie:2021-q4-168-2
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    Cited by:

    1. Marina Diakonova & Luis Molina & Hannes Mueller & Javier J. Pérez & Cristopher Rauh, 2022. "The information content of conflict, social unrest and policy uncertainty measures for macroeconomic forecasting," Working Papers 2232, Banco de España.
    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. Diakonova, Marina & Ghirelli, Corinna & Molina, Luis & Pérez, Javier J., 2023. "The economic impact of conflict-related and policy uncertainty shocks: The case of Russia," International Economics, Elsevier, vol. 174(C), pages 69-90.
    5. Sergey V. Arzhenovskiy, 2024. "Forecasting GDP Dynamics Based on the Bank of Russia’s Enterprise Monitoring Data," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 1, pages 31-44, February.
    6. Andrey Zubarev & Daniil Lomonosov & Konstantin Rybak, 2022. "Estimation of the Impact of Global Shocks on the Russian Economy and GDP Nowcasting Using a Factor Model," Russian Journal of Money and Finance, Bank of Russia, vol. 81(2), pages 49-78, June.
    7. 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.
    8. Maria Ghani & Feng Ma & Dengshi Huang, 2024. "Forecasting the Asian stock market volatility: Evidence from WTI and INE oil futures," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 1496-1512, April.

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    More about this item

    Keywords

    Nowcast; Forecast combination; GDP; DFM; Bank of Russia;
    All these keywords.

    JEL classification:

    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

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