In the extant literature of business cycle predictions, the signals for business cycle turning points are generally issued with a lag of at least 5 months. In this paper, we make use of a novel and timely indicator-the Google search volume data-to help to improve the timeliness of business cycle turning point identification. We identify multiple query terms to capture the real-time public concern on the aggregate economy, the credit market, and the labor market condition. We incorporate the query indices in a Markov-switching framework and successfully "nowcast" the peak date within a month that the turning occurred.
Scopus Subject Areas
- Business, Management and Accounting(all)
- Economics and Econometrics
- Public Administration