Short-term forecasting of euro area economic activity in an uncertain world

Sercan Eraslan, Andrea Fabbri and Lorena Saiz

Economic Bulletin Articles, 2026, vol. 8

Abstract: This article examines the key challenges for short-term forecasting of euro area economic activity since the COVID-19 pandemic, highlighting the persistently elevated levels of uncertainty. It details the significant enhancements made to the short-term forecasting models of the ECB as part of a general review aimed at improving their accuracy. It also highlights exploratory work on alternative approaches using advanced machine learning methods, which offer promising avenues to address the complexities of economic forecasting in times of high uncertainty. JEL Classification: C53, E32, E37, E52

Keywords: bridge equations; density; forecasting; machine learning; uncertainty (search for similar items in EconPapers)
Date: 2026-01
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.ecb.europa.eu//press/economic-bulletin ... 2~93ecd7cbc0.en.html (text/html)

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:ecb:ecbart:2026:0008:2

Access Statistics for this article

More articles in Economic Bulletin Articles from European Central Bank 60640 Frankfurt am Main, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Official Publications ().