Synchronization of cyclical behavior of economic and financial time series variables has generated considerable research interest in recent years. Typical examples include the synchronization of business cycles in different countries or regions (see Artis et al., 1997, 2004, among others) and the synchronization of bull and bear phases in financial markets (see Edwards et al., 2003; Bekaert et al., 2005, among others). Much attention in this research area has been focused on the extreme cases of independence and perfect synchronization of cycles. In the first case, the cycles in two variables are purely idiosyncratic, while in the second case the two variables are driven by a single common cycle such that regime shifts occur contemporaneously. A wide range of econometric techniques has been developed to examine these polar cases, ranging from nonparametric test statistics (see Harding and Pagan, 2006), to unobserved components models (see Koopman and Harvey, 1997), to Markov Switching Vector AutoRegressive (MS-VAR) models (see Krolzig, 1997).

Perhaps not surprisingly, it is often found that neither independence nor perfect synchronization are adequate representations of the cyclical dynamics in economic and financial variables. A variety of mechanisms may lead to the intermediate case of ’imperfect synchronization’. For example, it may be that cycles in individual variables are partly due to common components and partly due to idiosyncratic factors. This seems relevant when considering, for example, business cycles in different countries or regions, Kose et al. (see 2003); Del Negro and Otrok (see 2008). A second possibility is that the different variables in fact share a single common cyclical component, but are subject to different phase shifts. This approach is appropriate in the context of business cycles for jointly modeling the cyclical behavior of leading and coincident indicator variables. Similarly, in financial markets shifts from bull to bear phases and vice versa may occur earlier in some assets than in others due to differences in liquidity or gradual information diffusion among other reasons.