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Seasonal adjustment of the Bank of Russia Payment System financial flows data

Author

Listed:
  • Sergey Seleznev

    (Bank of Russia, Russian Federation)

  • Natalia Turdyeva

    (Bank of Russia, Russian Federation)

  • Ramis Khabibullin

    (Bank of Russia, Russian Federation)

  • Anna Tsvetkova

    (Bank of Russia, Russian Federation)

Abstract

This paper describes the seasonal adjustment algorithm used by the Bank of Russia to clean up data for ‘Monitoring of Sectoral Financial Flows’ weekly publication. We have developed a simple and fast procedure based on a set of trigonometric functions and dummy variables that demonstrates good results in terms of various quality metrics and can be easily modified for working with more flexible model specifications.

Suggested Citation

  • 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.
    • Handle: RePEc:bkr:wpaper:wps65
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    References listed on IDEAS

    as
    1. 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.
    2. Hirotugu Akaike, 1980. "Seasonal Adjustment By A Bayesian Modeling," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 1-13, January.
    3. Carvalho, V & Garcia, Juan R. & Hansen, S. & Ortiz, A. & Rodrigo, T. & More, J. V. R., 2020. "Tracking the COVID-19 Crisis with High-Resolution Transaction Data," Cambridge Working Papers in Economics 2030, Faculty of Economics, University of Cambridge.
    4. Raj Chetty & John N Friedman & Michael Stepner & Opportunity Insights Team & Camille Baker & Harvey Barnhard & Matt Bell & Gregory Bruich & Tina Chelidze & Lucas Chu & Westley Cineus & Sebi Devlin-Fol, 2024. "The Economic Impacts of COVID-19: Evidence from a New Public Database Built Using Private Sector Data," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 139(2), pages 829-889.
    5. Siem Jan Koopman & Marius Ooms & Irma Hindrayanto, 2009. "Periodic Unobserved Cycles in Seasonal Time Series with an Application to US Unemployment," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(5), pages 683-713, October.
    6. C. James Hueng, 2020. "Alternative Economic Indicators," Books from Upjohn Press, W.E. Upjohn Institute for Employment Research, number altecind.
    7. Ghysels,Eric & Osborn,Denise R., 2001. "The Econometric Analysis of Seasonal Time Series," Cambridge Books, Cambridge University Press, number 9780521565882, January.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. 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.

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    More about this item

    Keywords

    daily seasonal adjustment; time series; sectoral financial flows; Bayesian estimator.;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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