Trends and cycles during the COVID-19 pandemic period

Paulo Júlio and José Maria

Economic Modelling, 2024, vol. 139, issue C

Abstract: We perform several trend-cycle decompositions through the lens of two unobserved components models, herein estimated for Portugal and the euro area. Our procedure copes with the COVID-19’s consequences by explicitly considering potentially larger second moments during that period. This is achieved through a set of pandemic-specific shocks affecting only the 2020–21 period and embedded into estimation through a piecewise linear Kalman filter. Our methodology generates negligible historical revisions in key smoothed variables when the sample period is expanded until 2021:4, since pandemic shocks absorb a great deal of data volatility with minimal impacts on filtered data revisions or estimated parameters. Furthermore, non-pandemic shock volatility remains largely unaffected by the pandemic period. Innovations affecting the cycle in our preferred model are the key propellers of GDP developments during the COVID-19 pandemic period.

Keywords: COVID-19; Semi-structural models; Unobserved components; Potential output; Output gap; Bayesian estimation (search for similar items in EconPapers)
JEL-codes: C11 C30 E32 (search for similar items in EconPapers)
Date: 2024
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Working Paper: Trends and cycles during the COVID-19 pandemic period (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:139:y:2024:i:c:s0264999324001871

DOI: 10.1016/j.econmod.2024.106830

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