IDEAS home Printed from https://ideas.repec.org/a/ris/apltrx/022712.html

Monthly indicator and nowcast of nominal GDP with flow of funds

Author

Listed:
  • Matvey Finagin

    (HSE University; Bank of Russia, Moscow, Russian Federation)

  • Dmitriy Miroshnichenko

    (Bank of Russia, Moscow, Russian Federation)

Abstract

We construct a monthly indicator and perform nowcast for nominal GDP in Russia based on operational data. The data used are bank statements and the ruble price of Russian oil, which allows for the nowcast of nominal GDP ahead of official statistics by 3–5 months. Since nominal GDP data is published by “Rosstat” only on a quarterly basis and with a significant delay after the end of the reporting quarter, high-frequency and operational assessment of the indicator is of considerable relevance for the overall assessment of macroeconomic dynamics, as well as for the analysis of such sectors of the economy as fiscal, financial and external. The statistics of the 0409101 form “Revolving statement of accounting accounts of a credit institution” is used as bank reporting data. To assess the quality of the models, indicators of the variability of the indicator over time and the accuracy of the GDP science are used. At the same time, accuracy is assessed based on both actual and vintage data. The paper uses models of temporal disaggregation, MIDAS and the DFM model.

Suggested Citation

  • Matvey Finagin & Dmitriy Miroshnichenko, 2026. "Monthly indicator and nowcast of nominal GDP with flow of funds," Applied Econometrics, Russian Academy of National Economy and Public Administration (RANEPA), vol. 82, pages 105-123.
    • Handle: RePEc:ris:apltrx:022712
    as

    Download full text from publisher

    File URL: http://pe.cemi.rssi.ru/pe_2026_82_105-123.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Heiner Mikosch & Laura Solanko, 2019. "Forecasting Quarterly Russian GDP Growth with Mixed-Frequency Data," Russian Journal of Money and Finance, Bank of Russia, vol. 78(1), pages 19-35, March.
    2. Dafermos, Yannis & Nikolaidi, Maria & Galanis, Giorgos, 2017. "A stock-flow-fund ecological macroeconomic model," Ecological Economics, Elsevier, vol. 131(C), pages 191-207.
    3. Ali B. Barlas & Seda Guler Mert & Berk Orkun Isa & Alvaro Ortiz & Tomasa Rodrigo & Baris Soybilgen & Ege Yazgan, 2024. "Big data financial transactions and GDP nowcasting: The case of Turkey," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 227-248, March.
    4. Sax, Christoph & Steiner, Peter, 2013. "Temporal Disaggregation of Time Series," MPRA Paper 53389, University Library of Munich, Germany.
    5. Michael Zhemkov, 2021. "Nowcasting Russian GDP using forecast combination approach," International Economics, CEPII research center, issue 168, pages 10-24.
    6. Michael Zhemkov, 2022. "Assessment of Monthly GDP Growth Using Temporal Disaggregation Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 81(2), pages 79-104, June.
    7. Laura Felber & Simon Beyeler, 2023. "Nowcasting economic activity using transaction payments data," Working Papers 2023-01, Swiss National Bank.
    8. M. Y. Gareev & A. V. Polbin, 2022. "Nowcasting Russia’s key macroeconomic variables using machine learning," Voprosy Ekonomiki, NP Voprosy Ekonomiki, issue 8.
    9. Bezemer, Dirk J., 2010. "Understanding financial crisis through accounting models," Accounting, Organizations and Society, Elsevier, vol. 35(7), pages 676-688, October.
    10. Porshakov, A. & Ponomarenko, A. & Sinyakov, A., 2016. "Nowcasting and Short-Term Forecasting of Russian GDP with a Dynamic Factor Model," Journal of the New Economic Association, New Economic Association, vol. 30(2), pages 60-76.
    11. Galbraith, John W. & Tkacz, Greg, 2015. "Nowcasting GDP with electronic payments data," Statistics Paper Series 10, European Central Bank.
    12. Konchitchki, Yaniv & Patatoukas, Panos N., 2014. "Accounting earnings and gross domestic product," Journal of Accounting and Economics, Elsevier, vol. 57(1), pages 76-88.
    13. Litterman, Robert B, 1983. "A Random Walk, Markov Model for the Distribution of Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 169-173, April.
    14. Ivan Stankevich, 2020. "Comparison of macroeconomic indicators nowcasting methods: Russian GDP case," Applied Econometrics, Russian Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 113-127.
    15. Dmitry Gornostaev & Alexey Ponomarenko & Sergei Seleznev & Alexandra Sterkhova, 2022. "A Real-Time Historical Database of Macroeconomic Indicators for Russia," Russian Journal of Money and Finance, Bank of Russia, vol. 81(1), pages 88-103, March.
    16. Andrey Polbin & Andrei Shumilov, 2025. "Nowcasting and forecasting Russian GDP and its components using quantile models," Applied Econometrics, Russian Academy of National Economy and Public Administration (RANEPA), vol. 79, pages 5-26.
    17. Vladimir Boyko & Nadezhda Kislyak & Mikhail Nikitin & Oleg Oborin, 2020. "Methods for Estimating the Gross Regional Product Leading Indicator," Russian Journal of Money and Finance, Bank of Russia, vol. 79(3), pages 3-29, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alexander Eliseev, 2025. "Nowcasting Russian GDP in a Mixed-Frequency DSGE Model with a Panel of Non-Modelled Variables," Russian Journal of Money and Finance, Bank of Russia, vol. 84(3), pages 63-93, September.
    2. Anastasia Pankratova, 2025. "Application of MF-PVAR Model for Nowcasting Gross Regional Products in Russia," Russian Journal of Money and Finance, Bank of Russia, vol. 84(4), pages 47-62, December.
    3. Ivan Stankevich, 2023. "Application of Markov-Switching MIDAS models to nowcasting of GDP and its components," Applied Econometrics, Russian Academy of National Economy and Public Administration (RANEPA), vol. 70, pages 122-143.
    4. 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.
    5. 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.
    6. Vladimir Boyko & Nadezhda Kislyak & Mikhail Nikitin & Oleg Oborin, 2020. "Methods for Estimating the Gross Regional Product Leading Indicator," Russian Journal of Money and Finance, Bank of Russia, vol. 79(3), pages 3-29, September.
    7. Michael Zhemkov, 2022. "Assessment of Monthly GDP Growth Using Temporal Disaggregation Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 81(2), pages 79-104, June.
    8. 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.
    9. 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.
    10. Andrey Polbin & Andrei Shumilov, 2025. "Nowcasting and forecasting Russian GDP and its components using quantile models," Applied Econometrics, Russian Academy of National Economy and Public Administration (RANEPA), vol. 79, pages 5-26.
    11. Nikita Fokin, 2021. "The importance of modeling structural breaks in forecasting Russian GDP," Applied Econometrics, Russian Academy of National Economy and Public Administration (RANEPA), vol. 63, pages 5-29.
    12. Canelli, Rosa & Fontana, Giuseppe & Realfonzo, Riccardo & Passarella, Marco Veronese, 2024. "Energy crisis, economic growth and public finance in Italy," Energy Economics, Elsevier, vol. 132(C).
    13. 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.
    14. El-Shagi, Makram & Tochkov, Kiril, 2022. "Divisia monetary aggregates for Russia: Money demand, GDP nowcasting and the price puzzle," Economic Systems, Elsevier, vol. 46(4).
    15. Luke Mosley & Idris A. Eckley & Alex Gibberd, 2022. "Sparse temporal disaggregation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2203-2233, October.
    16. Roberto Veneziani & Luca Zamparelli & Michalis Nikiforos & Gennaro Zezza, 2017. "Stock-Flow Consistent Macroeconomic Models: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 31(5), pages 1204-1239, December.
    17. Bosetti, Isabella & Incardona, Rocco & Caloca, Antonio Rodríguez, 2025. "Filling the gap: the geographical allocation of euro area portfolio investment liabilities and related income," Statistics Paper Series 50, European Central Bank.
    18. Klaus Abberger & Michael Graff & Oliver Müller & Boriss Siliverstovs, 2023. "Imputing Monthly Values for Quarterly Time Series: An Application Performed with Swiss Business Cycle Data," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(3), pages 241-273, November.
    19. Kyosuke Chikamatsu, Naohisa Hirakata, Yosuke Kido, Kazuki Otaka, 2018. "Nowcasting Japanese GDPs," Bank of Japan Working Paper Series 18-E-18, Bank of Japan.
    20. Michael Zhemkov, 2021. "Nowcasting Russian GDP using forecast combination approach," International Economics, CEPII research center, issue 168, pages 10-24.

    More about this item

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ris:apltrx:022712. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Anatoly Peresetsky (email available below). General contact details of provider: http://appliedeconometrics.cemi.rssi.ru/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.