Accelerating peak dating in a dynamic factor Markov-switching model

The dynamic factor Markov-switching (DFMS) model introduced by Diebold and Rudebusch (1996) has proven to be a powerful framework for measuring the business cycle. We extend the DFMS model by allowing for time-varying transition probabilities, intending to accelerate the real-time dating of business cycle peaks. Time-variation of the transition probabilities is brought about endogenously using the score-driven approach and exogenously using the term spread. In a real-time application using the four components of The Conference Board's Coincident Economic Index for 1959–2020, we find that signaling power for recessions is significantly improved. We are able to date the 2001 and 2008 recession peaks four and two months after the peak date, which is four and ten months before the NBER.