Words Are the New Numbers: A Newsy Coincident Index of Business Cycles

Arbeitspapier

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 re ect economic uctuations.

Object Details

• ISBN: 978-82-7553-953-1
• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 21/2016

Classification and Topics

• Classification: Wirtschaft ; Bayesian Analysis: General ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Business Fluctuations; Cycles
• Subject: LDA ; latent dirichlet allocation ; business cycles ; dynamic factor model

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Thorsrud, Leif Anders
• Event: Veröffentlichung
• (who): Norges Bank
• (where): Oslo
• (when): 2016

Further information

• Handle: http://hdl.handle.net/10419/210110
• Last update: 10.03.2025, 11:42 AM CET