Estimating the Euro Area output gap using multivariate information and addressing the COVID-19 pandemic

James Morley, Diego Rodriguez Palenzuela, Yiqiao Sun and Benjamin Wong

No 2716, Working Paper Series from European Central Bank

Abstract: We estimate the euro area output gap by applying the Beveridge-Nelson decomposition based on a large Bayesian vector autoregression. Our approach incorporates multivariate information through the inclusion of a wide range of variables in the analysis and addresses data issues associated with the COVID-19 pandemic. The estimated output gap lines up well with the CEPR chronology of the business cycle for the euro area and we find that hours worked, more than the unemployment rate, provides the key source of information about labor utilization in the economy, especially in pinning down the depth of the output gap during the COVID-19 recession when the unemployment rate rose only moderately. Our findings suggest that labor market adjustments to the business cycle in the euro area occur more through the intensive, rather than extensive, margin. JEL Classification: C18, E17, E32

Keywords: Bayesian estimation; Beveridge-Nelson decomposition; multivariate information; output gap (search for similar items in EconPapers)
Date: 2022-08
New Economics Papers: this item is included in nep-eec and nep-mon
Note: 2759141
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Citations: View citations in EconPapers (1)

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Journal Article: Estimating the euro area output gap using multivariate information and addressing the COVID-19 pandemic (2023)
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