Economics at your fingertips  

Using machine learning and big data to analyse the business cycle

Dominik Hirschbühl, Luca Onorante () and Lorena Saiz

Economic Bulletin Articles, 2021, vol. 5

Abstract: This article reviews how policy institutions – international organisations and central banks – use big data and machine learning methods to analyse the business cycle. It provides different examples to show how big data and machine learning methods are particularly suitable for capturing large shocks and non-linearities in real time. The coronavirus crisis is a case in point, where big data have provided invaluable timely signals on the state of the economy, thus helping to track and assess economic activity, domestic demand and labour market developments in real time. Finally, the article discusses the main challenges faced by central banks when using non-standard data and methods and areas of further application to the work of central banks. JEL Classification: C53, C55, E32

Keywords: big data; machine learning; Short-term forecasting (search for similar items in EconPapers)
Date: 2021-08
Note: 412615
References: Add references at CitEc
Citations: Track citations by RSS feed

Downloads: (external link) ... 2~c429c01d24.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:

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 ().

Page updated 2023-03-27
Handle: RePEc:ecb:ecbart:2021:0005:2