A Mixed Frequency BVAR for the Euro Area Labour Market

Agostino Consolo, Claudia Foroni and Catalina Martínez Hernández

Oxford Bulletin of Economics and Statistics, 2023, vol. 85, issue 5, 1048-1082

Abstract: We introduce a Bayesian mixed frequency VAR model for the aggregate euro area labour market that features a structural identification via sign restrictions. The purpose of this paper is twofold: we aim at (i) providing reliable and timely forecasts of key labour market variables and (ii) enhancing the economic interpretation of the main movements in the labour market. We find satisfactory results in terms of nowcasting and forecasting, especially for employment growth. Furthermore, we look into the shocks that drove the labour market and macroeconomic dynamics from 2002 to 2022, with an insight also on the COVID‐19 recession. While demand shocks were the main drivers during the Global Financial Crisis, technology and wage bargaining factors, reflecting the degree of lockdown‐related restrictions and job retention schemes, have been important drivers of key labour market variables during the pandemic.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.1111/obes.12555

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:obuest:v:85:y:2023:i:5:p:1048-1082

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0305-9049

Access Statistics for this article

Oxford Bulletin of Economics and Statistics is currently edited by Christopher Adam, Anindya Banerjee, Christopher Bowdler, David Hendry, Adriaan Kalwij, John Knight and Jonathan Temple

More articles in Oxford Bulletin of Economics and Statistics from Department of Economics, University of Oxford Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().