Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/210109 
Year of Publication: 
2016
Series/Report no.: 
Working Paper No. 20/2016
Publisher: 
Norges Bank, Oslo
Abstract: 
The agents in the economy use a plethora of high frequency information, including news media, to guide their actions and thereby shape aggregate economic fluctuations. Traditional nowcasting approches have to a relatively little degree made use of such information. In this paper, I show how unstructured textual information in a business newspaper can be decomposed into daily news topics and used to nowcast quarterly GDP growth. Compared with a big bank of experts, here represented by official central bank nowcasts and a state-of-the-art forecast combination system, the proposed methodology performs at times up to 15 percent better, and is especially competitive around important business cycle turning points. Moreover, if the statistical agency producing the GDP statistics itself had used the news-based methodology, it would have resulted in a less noisy revision process. Thus, news reduces noise.
Subjects: 
LDA
DFM
nowcasting
dynamic factor model
latent dirichlet allocation
JEL: 
C11
C32
E37
Persistent Identifier of the first edition: 
ISBN: 
978-82-7553-952-4
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Working Paper
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