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

Abstract

In this paper I construct a daily business cycle index based on quarterly GDP and textual information contained in a daily business newspaper. The newspaper data is 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. I show that both contributions, the usage of newspaper data and the latent threshold mechanism, contribute towards the qualities of the derived index: It is more timely and accurate than commonly used alternative business cycle indicators and indexes, and, it provides the index user with broad based high frequent information about the type of news that drive or reflect economic fluctuations.