 Nowcasting using news topics Big Data versus big bankNo No 6/2016, Working Papers from Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School 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. Keywords: Nowcasting; Dynamic Factor Model (DFM); Latent Dirichlet Allocation (LDA) (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bny:wpaper:0046 Access Statistics for this paper More papers in Working Papers from Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School Contact information at EDIRC. |
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