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dc.contributor.authorThorsrud, Leif Anders
dc.date.accessioned2017-02-02T11:49:14Z
dc.date.available2017-02-02T11:49:14Z
dc.date.issued2016
dc.identifier.issn1892-2198
dc.identifier.urihttp://hdl.handle.net/11250/2429268
dc.description.abstractThe 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 o cial 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.nb_NO
dc.language.isoengnb_NO
dc.publisherBI Norwegian Business Schoolnb_NO
dc.relation.ispartofseriesCAMP Working Paper Series;6/2016
dc.subjectNowcasting, Dynamic Factor Model (DFM), Latent Dirichlet Allocation (LDA)nb_NO
dc.titleNowcasting using news topics Big Data versus big banknb_NO
dc.typeWorking papernb_NO
dc.source.pagenumber62nb_NO


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