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Nowcasting using news topics Big Data versus big bank

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  • Leif Anders Thorsrud

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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.

Suggested Citation

  • Leif Anders Thorsrud, 2016. "Nowcasting using news topics Big Data versus big bank," Working Papers No 6/2016, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
  • Handle: RePEc:bny:wpaper:0046
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    Keywords

    Nowcasting; Dynamic Factor Model (DFM); Latent Dirichlet Allocation (LDA);

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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