Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/249889 
Year of Publication: 
2021
Series/Report no.: 
ECB Working Paper No. 2616
Publisher: 
European Central Bank (ECB), Frankfurt a. M.
Abstract: 
This paper shows that newspaper articles contain timely economic signals that can materially improve nowcasts of real GDP growth for the euro area. Our text data is drawn from fifteen popular European newspapers, that collectively represent the four largest Euro area economies, and are machine translated into English. Daily sentiment metrics are created from these news articles and we assess their value for nowcasting. By comparing to competitive and rigorous benchmarks, we find that newspaper text is helpful in nowcasting GDP growth especially in the first half of the quarter when other lower-frequency soft indicators are not available. The choice of the sentiment measure matters when tracking economic shocks such as the Great Recession and the Great Lockdown. Non-linear machine learning models can help capture extreme movements in growth, but require sufficient training data in order to be effective so become more useful later in our sample.
Subjects: 
Text analysis
Forecasting
Machine learning
Business cycles
COVID-19
JEL: 
C43
C45
C55
C82
E37
Persistent Identifier of the first edition: 
ISBN: 
978-92-899-4869-2
Document Type: 
Working Paper

Files in This Item:
File
Size
864.37 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.