MarkovSwitchingModelle
Markov Switching Models are a class of statistical models used to analyze time series data that exhibit changes in behavior over time. The core idea is that the underlying data-generating process can switch between several distinct states or regimes. These switches are assumed to follow a Markov process, meaning the probability of transitioning to a particular state depends only on the current state and not on the sequence of past states.
In a typical Markov Switching Model, each state is characterized by its own set of parameters, such
The key components of a Markov Switching Model are the observable time series, the unobservable states, and
These models are widely applied in econometrics, finance, and other fields where time-varying dynamics are prevalent.