 Estimating the output gap after COVID: How to address unprecedented macroeconomic variationsCamilo Granados and Daniel Parra-Amado Economic Modelling, 2024, vol. 135, issue C Abstract: We examine the importance of adjusting output gap frameworks during large-scale disruptions, with a focus on the COVID-19 pandemic. Such adaptation can be crucial given the impact of such episodes on the reliability of time-series models and the inherent need for stability in output gap methods. We employ a Bayesian Structural Vector Autoregression model, identified through a permanent-transitory decomposition, and enhance it by scaling residuals around the pandemic period. Our analysis, conducted for seven developed economies, suggests that adjusting the model around the pandemic’s onset leads to improved estimates and reduced uncertainty. This approach surpasses traditional filters and other complex models lacking pandemic-timed adjustments. Notably, omitting such adjustments can result in biased and unstable gap estimates, potentially causing rapid gap recoveries post-downturns or increased volatility. Our findings underscore the importance of prompt reassessments of output gap frameworks during unprecedented global events, focusing on their stability and uncertainty. Keywords: Bayesian methods; Business cycles; Potential output; Output gaps; Structural estimation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:135:y:2024:i:c:s0264999324000671 DOI: 10.1016/j.econmod.2024.106711 Access Statistics for this article Economic Modelling is currently edited by S. Hall and P. Pauly More articles in Economic Modelling from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.