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The COVID-19 shock and challenges for time series models

Author

Listed:
  • Bobeica, Elena
  • Hartwig, Benny
Abstract
We document the impact of COVID-19 on frequently employed time series models, with a focus on euro area inflation. We show that for both single equation models (Phillips curves) and Vector Autoregressions (VARs) estimated parameters change notably with the pandemic. In a VAR, allowing the errors to have a distribution with fatter tails than the Gaussian one equips the model to better deal with the COVID-19 shock. A standard Gaussian VAR can still be used for producing conditional forecasts when relevant off-model information is used. We illustrate this by conditioning on official projections for a set of variables, but also by tilting to expectations from the Survey of Professional Forecasters. For Phillips curves, averaging across many conditional forecasts in a thick modelling framework offers some hedge against parameter instability. JEL Classification: C53, E31, E37

Suggested Citation

  • Bobeica, Elena & Hartwig, Benny, 2021. "The COVID-19 shock and challenges for time series models," Working Paper Series 2558, European Central Bank.
    • Handle: RePEc:ecb:ecbwps:20212558
    Note: 2382002
    as

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    File URL: https://www.ecb.europa.eu//pub/pdf/scpwps/ecb.wp2558~22b223a7c6.en.pdf
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    References listed on IDEAS

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    2. Tamás Kiss & Hoang Nguyen & Pär Österholm, 2023. "Modelling Okun’s law: Does non-Gaussianity matter?," Empirical Economics, Springer, vol. 64(5), pages 2183-2213, May.
    3. Morley, James & Rodríguez-Palenzuela, Diego & Sun, Yiqiao & Wong, Benjamin, 2023. "Estimating the euro area output gap using multivariate information and addressing the COVID-19 pandemic," European Economic Review, Elsevier, vol. 153(C).
    4. Costin Radu Boldea & Bogdan Ion Boldea & Tiberiu Iancu, 2023. "The Pandemic Waves’ Impact on the Crude Oil Price and the Rise of Consumer Price Index: Case Study for Six European Countries," Sustainability, MDPI, vol. 15(8), pages 1-15, April.
    5. Haderer, Michaela, 2022. "An Estimated DSGE Model of the Euro Area with Expectations about the Timing and Nature of Liftoff from the Lower Bound," Working Papers 2022-05, University of Sydney, School of Economics.
    6. Omotosho, Babatunde S. & Yang, Bo, 2024. "Oil price shocks and macroeconomic dynamics in resource-rich emerging economies under regime shifts," Journal of International Money and Finance, Elsevier, vol. 144(C).
    7. Zeynep Kantur & Gülserim Özcan, 2022. "Dissecting Turkish inflation: theory, fact, and illusion," Economic Change and Restructuring, Springer, vol. 55(3), pages 1543-1553, August.
    8. Doojav, Gan-Ochir, 2021. "Socio-economic recovery from the Covid-19 pandemic: Macroeconomic impacts and policy issues in Mongolia," MPRA Paper 111197, University Library of Munich, Germany.
    9. Gan-Ochir Doojav, 2023. "Macroeconomic Effects of Covid-19 in a Commodity-Exporting Economy: Evidence from Mongolia," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 59(5), pages 1323-1348, April.

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    More about this item

    Keywords

    COVID-19; forecasting; inflation; student's t errors; tilting; VAR;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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