IDEAS home Printed from https://ideas.repec.org/p/bkr/wpaper/wps160.html

Structural seasonality

Author

Listed:
  • Sergey Ivashchenko

    (Bank of Russia, Russian Federation)

Abstract

The conventional practice in estimating DSGE models is to rely on seasonally adjusted data. While convenient, this approach distorts the microeconomic foundations of the model. An alternative is to model seasonality explicitly, but this often introduces severe misspecification. This paper proposes a middle ground: using year-over-year growth rates instead of quarter-over-quarter growth rates, which allows the model to endogenously determine the seasonal adjustment. This approach greatly improves forecast accuracy by more than 20% while keeping the internal consistency of the model. Moreover, we show that model misspecification and seasonal adjustment can offset each other, implying that seasonality should be treated as model-specific rather than imposed exogenously. Empirical results for U.S. and Russian data confirm that structural seasonality improves forecasting performance, and model fit relative to conventional seasonal adjustment methods.

Suggested Citation

  • Sergey Ivashchenko, 2026. "Structural seasonality," Bank of Russia Working Paper Series wps160, Bank of Russia.
    • Handle: RePEc:bkr:wpaper:wps160
    as

    Download full text from publisher

    File URL: https://www.cbr.ru/StaticHtml/File/186051/wp_160.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Antonio Matas-Mir & Denise R. Osborn & Marco J. Lombardi, 2008. "The effect of seasonal adjustment on the properties of business cycle regimes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(2), pages 257-278.
    2. Stephanie Schmitt-Grohe & Martin Uribe, 2011. "Business Cycles With A Common Trend in Neutral and Investment-Specific Productivity," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 14(1), pages 122-135, January.
    3. Christiano, Lawrence J. & Todd, Richard M., 2002. "The conventional treatment of seasonality in business cycle analysis: does it create distortions?," Journal of Monetary Economics, Elsevier, vol. 49(2), pages 335-364, March.
    4. Canova, Fabio, 2014. "Bridging DSGE models and the raw data," Journal of Monetary Economics, Elsevier, vol. 67(C), pages 1-15.
    5. Hayat, Aziz & Bhatti, M. Ishaq, 2013. "Masking of volatility by seasonal adjustment methods," Economic Modelling, Elsevier, vol. 33(C), pages 676-688.
    6. Sims, Christopher A., 1993. "Rational expectations modeling with seasonally adjusted data," Journal of Econometrics, Elsevier, vol. 55(1-2), pages 9-19.
    7. Russell W. Cooper & Immo Schott, 2023. "Capital reallocation and the cyclicality of aggregate productivity," Quantitative Economics, Econometric Society, vol. 14(4), pages 1337-1365, November.
    8. Saijo, Hikaru, 2013. "Estimating DSGE models using seasonally adjusted and unadjusted data," Journal of Econometrics, Elsevier, vol. 173(1), pages 22-35.
    9. Victor Gómez & Agustín Maravall, 1996. "Programs TRAMO and SEATS, Instruction for User (Beta Version: september 1996)," Working Papers 9628, Banco de España.
    10. Hansen, Lars Peter & Sargent, Thomas J., 1993. "Seasonality and approximation errors in rational expectations models," Journal of Econometrics, Elsevier, vol. 55(1-2), pages 21-55.
    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. Lilia Maliar & Serguei Maliar & John B. Taylor & Inna Tsener, 2020. "A tractable framework for analyzing a class of nonstationary Markov models," Quantitative Economics, Econometric Society, vol. 11(4), pages 1289-1323, November.
    2. Serguei Maliar & John Taylor & Lilia Maliar, 2016. "The Impact of Alternative Transitions to Normalized Monetary Policy," 2016 Meeting Papers 794, Society for Economic Dynamics.
    3. Saijo, Hikaru, 2013. "Estimating DSGE models using seasonally adjusted and unadjusted data," Journal of Econometrics, Elsevier, vol. 173(1), pages 22-35.
    4. Christiano, Lawrence J. & Vigfusson, Robert J., 2003. "Maximum likelihood in the frequency domain: the importance of time-to-plan," Journal of Monetary Economics, Elsevier, vol. 50(4), pages 789-815, May.
    5. Ghysels, E., 1992. "Charistmas, Spring and the Dawning of Economic Recovery," Cahiers de recherche 9215, Universite de Montreal, Departement de sciences economiques.
    6. Christiano, Lawrence J. & Todd, Richard M., 2002. "The conventional treatment of seasonality in business cycle analysis: does it create distortions?," Journal of Monetary Economics, Elsevier, vol. 49(2), pages 335-364, March.
    7. Lars Peter Hansen & James J. Heckman, 1996. "The Empirical Foundations of Calibration," Journal of Economic Perspectives, American Economic Association, vol. 10(1), pages 87-104, Winter.
    8. Zhongjun Qu, 2018. "A Composite Likelihood Framework for Analyzing Singular DSGE Models," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 916-932, December.
    9. Olalla, Myriam García & Gómez, Alejandro Ruiz, 2011. "Robust control and central banking behaviour," Economic Modelling, Elsevier, vol. 28(3), pages 1265-1278, May.
    10. Sergey M. Ivashchenko, 2019. "DSGE Models: Problem of Trends," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 2, pages 81-95, April.
    11. Ivashchenko, S., 2020. "Long-term growth sources for sectors of Russian economy," Journal of the New Economic Association, New Economic Association, vol. 48(4), pages 86-112.
    12. A. Shorrocks & T. Hens & H. Gottinger & S. Reichelstein & B. Kuon & M. Frenkel & R. Braun & R. Noll & Y. Xu, 1997. "Book Reviews," Journal of Economics, Springer, vol. 66(3), pages 308-328, October.
    13. Luca Sala, 2015. "Dsge Models in the Frequency Domains," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(2), pages 219-240, March.
    14. Otrok, Christopher & Ravikumar, B. & Whiteman, Charles H., 2007. "A generalized volatility bound for dynamic economies," Journal of Monetary Economics, Elsevier, vol. 54(8), pages 2269-2290, November.
    15. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    16. Éric Ghysels, 1994. "L’analyse économétrique et la saisonnalité," L'Actualité Economique, Société Canadienne de Science Economique, vol. 70(1), pages 43-62.
    17. Jinho Choi & Juan Carlos Escanciano & Junjie Guo, 2022. "Generalized band spectrum estimation with an application to the New Keynesian Phillips curve," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 1055-1078, August.
    18. Abeysinghe, Tilak, 1998. "Forecasting Singapore's quarterly GDP with monthly external trade," International Journal of Forecasting, Elsevier, vol. 14(4), pages 505-513, December.
    19. Braun, R Anton & Evans, Charles L, 1998. "Seasonal Solow Residuals and Christmas: A Case for Labor Hoarding and Increasing Returns," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 30(3), pages 306-330, August.
    20. Denis Tkachenko & Zhongjun Qu, 2012. "Frequency Domain Analysis of Medium Scale DSGE Models with Application to Smets and Wouters (2007)," Advances in Econometrics, in: DSGE Models in Macroeconomics: Estimation, Evaluation, and New Developments, pages 319-385, Emerald Group Publishing Limited.

    More about this item

    Keywords

    ;
    ;
    ;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:bkr:wpaper:wps160. 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: BoR Research The email address of this maintainer does not seem to be valid anymore. Please ask BoR Research to update the entry or send us the correct address (email available below). General contact details of provider: https://edirc.repec.org/data/cbrgvru.html .

    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.