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Structural seasonality

Author

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  • 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
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    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

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