Accelerating Peak Dating in a Dynamic Factor Markov-Switching Model

Abstract

The dynamic factor Markov-switching (DFMS) model introduced by Diebold and Rudebusch (1996) has proven to be a powerful framework to measure the business cycle. We extend the DFMS model by allowing for time-varying transition probabilities, with the aim of accelerating the real-time dating of turning points between expansion and recession regimes. Time-variation of the transition probabilities is brought about endogenously using the accelerated 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 the period 1959-2020, we find that signaling power for recessions is significantly improved and are able to date the 2001 and 2008 recession peaks four and ten months before the NBER.