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Data of Sectoral Financial Flows as a High-Frequency Indicator of Economic Activity

Author

Listed:
  • Natalia Turdyeva

    (Bank of Russia)

  • Anna Tsvetkova

    (Bank of Russia)

  • Levon Movsesyan

    (Bank of Russia)

  • Alexey Porshakov

    (Bank of Russia)

  • Dmitriy Chernyadyev

    (Bank of Russia)

Abstract

In times of crisis, events are moving fast and standard macroeconomic statistics published with a lag cannot quite keep pace with the changing situation. During such periods, there is an increasing need to use high-frequency indicators that allow virtually real-time monitoring of economic activity. In many countries, this is achieved by using financial transaction data. In this paper, we present a methodology for the current analysis of sectoral financial flows in the Russian economy based on data from the Bank of Russia payment system. We use the information on the dynamics of average daily payments for each class of OKVED 2 (the Russian National Classifier of Economic Activities) to develop high-frequency indicators of economic activity, which have been published on the Bank of Russia website since April 2020. We also tentatively discuss the potential of financial transaction data in terms of improving the tools for short-term forecasting of business activity dynamics and solutions to other research problems.

Suggested Citation

  • Natalia Turdyeva & Anna Tsvetkova & Levon Movsesyan & Alexey Porshakov & Dmitriy Chernyadyev, 2021. "Data of Sectoral Financial Flows as a High-Frequency Indicator of Economic Activity," Russian Journal of Money and Finance, Bank of Russia, vol. 80(2), pages 28-49, June.
    • Handle: RePEc:bkr:journl:v:80:y:2021:i:2:p:28-49
    DOI: 10.31477/rjmf.202102.28
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    References listed on IDEAS

    as
    1. Sergey Seleznev & Natalia Turdyeva & Ramis Khabibullin & Anna Tsvetkova, 2020. "Seasonal adjustment of the Bank of Russia Payment System financial flows data," Bank of Russia Working Paper Series wps65, Bank of Russia.
    2. Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
    3. Valentina Aprigliano & Guerino Ardizzi & Libero Monteforte, 2017. "Using the payment system data to forecast the Italian GDP," Temi di discussione (Economic working papers) 1098, Bank of Italy, Economic Research and International Relations Area.
    4. Ramis Khbaibullin & Sergei Seleznev, 2020. "Stochastic Gradient Variational Bayes and Normalizing Flows for Estimating Macroeconomic Models," Bank of Russia Working Paper Series wps61, Bank of Russia.
    5. Ollech, Daniel, 2018. "Seasonal adjustment of daily time series," Discussion Papers 41/2018, Deutsche Bundesbank.
    6. Aleksey Ponomarenko & Svetlana Popova & Andrey Sinyakov & Natalia Turdyeva & Dmitry Chernyadyev, 2020. "Assessing the Consequences of the Pandemic for the Russian Economy Through an Input-Output Model," Russian Journal of Money and Finance, Bank of Russia, vol. 79(4), pages 3-17, December.
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    Cited by:

    1. 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.

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    More about this item

    Keywords

    Bank of Russia payment system; high-frequency data; sectoral financial flows; nowcasting; coronacrisis;
    All these keywords.

    JEL classification:

    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • E42 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Monetary Sytsems; Standards; Regimes; Government and the Monetary System

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