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Words are the new numbers: A newsy coincident index of business cycles

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Abstract

I construct a daily business cycle index based on quarterly GDP and textual information contained in a daily business newspaper. The newspaper data are decomposed into time series representing newspaper topics using a Latent Dirichlet Allocation model. The business cycle index is estimated using the newspaper topics and a time-varying Dynamic Factor Model where dynamic sparsity is enforced upon the factor loadings using a latent threshold mechanism. The resulting index is shown to be not only more timely but also more accurate than commonly used alternative business cycle indicators. Moreover, the derived index provides the index user with broad based high frequent information about the type of news that drive or reflect economic fluctuations.

Suggested Citation

  • Leif Anders Thorsrud, 2016. "Words are the new numbers: A newsy coincident index of business cycles," Working Paper 2016/21, Norges Bank.
    • Handle: RePEc:bno:worpap:2016_21
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    File URL: http://www.norges-bank.no/en/Published/Papers/Working-Papers/2016/212016/
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    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
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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