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Seasonality in COVID-19 times.

Juan Bógalo1, Martín Llada2, Pilar Poncela1

  • 1Universidad Autónoma de Madrid, Spain.

Economics Letters
|December 21, 2021
PubMed
Summary

The COVID-19 pandemic disrupted economic data, altering time series. This study evaluates seasonal adjustment methods using simulations with outliers to reflect pandemic-era data changes.

Area of Science:

  • Econometrics
  • Time Series Analysis
  • Statistical Modeling

Background:

  • The COVID-19 pandemic significantly impacted economic data generation.
  • Unprecedented changes in economic series necessitate robust statistical methods.
  • Understanding data heterogeneity during crisis periods is crucial for accurate analysis.

Purpose of the Study:

  • To compare the performance of various seasonal adjustment methods.
  • To assess the impact of outliers and trend/seasonality changes on time series data.
  • To identify reliable methods for economic data analysis post-COVID-19.

Main Methods:

  • Simulations were conducted to model data generating processes.
  • Outliers were introduced into trend and seasonal components.
Keywords:
COVID-19CiSSADeseasonalizingOutlierX-13ARIMA-SEATS

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  • Performance metrics were used to evaluate different seasonal adjustment techniques.

    • Main Results:

    • Certain seasonal adjustment methods demonstrated greater resilience to outliers.
    • The heterogeneity introduced by the pandemic significantly affected standard adjustment procedures.
    • Method performance varied depending on the type and magnitude of introduced anomalies.

    Conclusions:

    • Standard seasonal adjustment methods may require re-evaluation in light of pandemic-induced data disruptions.
    • Simulations with realistic outlier scenarios are vital for method selection.
    • Further research is needed to develop adaptive seasonal adjustment techniques for crisis periods.