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.

Modelling of Leishmaniasis Infection Dynamics: A Comparative Time Series Analysis with VAR, VECM, Generalized Linear and Markov Switching Models

“…By permitting switching between these structures, this model is able to capture more complex dynamic patterns. This model and its variants have been widely applied [12,13]. The following network mapping [14] summarized the co-occurrence of author's keywords when using the Markov Switching model (Figure 3).…”