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Seasonality in COVID-19 times

Author

Listed:
  • Bógalo, Juan
  • Llada, Martín
  • Poncela, Pilar
  • Senra, Eva

Abstract

COVID-19 hit the economy in an unprecedented way, changing the data generating process of many series. We compare different seasonal adjustment methods through simulations, introducing outliers in the trend and seasonality to reproduce the heterogeneity in the series during COVID-19.

Suggested Citation

  • Bógalo, Juan & Llada, Martín & Poncela, Pilar & Senra, Eva, 2022. "Seasonality in COVID-19 times," Economics Letters, Elsevier, vol. 211(C).
    • Handle: RePEc:eee:ecolet:v:211:y:2022:i:c:s0165176521004547
    DOI: 10.1016/j.econlet.2021.110206
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    References listed on IDEAS

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    1. Maravall, Agustin, 1993. "Stochastic linear trends : Models and estimators," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 5-37, March.
    2. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    3. Victor Gómez & Agustín Maravall, 1996. "Programs TRAMO and SEATS, Instruction for User (Beta Version: september 1996)," Working Papers 9628, Banco de España.
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    Cited by:

    1. William M. Doerner & Wenzhen Lin, 2022. "Applying Seasonal Adjustments to Housing Markets," FHFA Staff Working Papers 22-03, Federal Housing Finance Agency.

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    More about this item

    Keywords

    CiSSA; COVID-19; Deseasonalizing; Outlier; X-13ARIMA-SEATS;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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