  This paper investigates the role of high-dimensional information sets in the
context of Markov switching models with time varying transition probabilities.
Markov switching models are commonly employed in empirical macroeconomic
research and policy work. However, the information used to model the switching
process is usually limited drastically to ensure stability of the model.
Increasing the number of included variables to enlarge the information set
might even result in decreasing precision of the model. Moreover, it is often
not clear a priori which variables are actually relevant when it comes to
informing the switching behavior. Building strongly on recent contributions in
the field of factor analysis, we introduce a general type of Markov switching
autoregressive models for non-linear time series analysis. Large numbers of
time series are allowed to inform the switching process through a factor
structure. This factor-augmented Markov switching (FAMS) model overcomes
estimation issues that are likely to arise in previous assessments of the
modeling framework. More accurate estimates of the switching behavior as well
as improved model fit result. The performance of the FAMS model is illustrated
in a simulated data example as well as in an US business cycle application.
