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Nowcasting using news topics Big Data versus big bank

Listed author(s):
  • Leif Anders Thorsrud

    ()

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.

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Paper provided by Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School in its series Working Papers with number No 6/2016.

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Length: 60 pages
Date of creation: Nov 2016
Handle: RePEc:bny:wpaper:0046
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