Autoregressive Markov Switching Model Function

The Autoregressive Markov Switching Model function serves as a comprehensive tool for evaluating, simulating, and forecasting autoregressive Markov switching models. This model supports flexible selection of distribution functions for estimation purposes, including normal distribution and t-distribution options. Autoregressive Markov switching models find extensive applications in detecting structural breaks in time series data and analyzing financial markets, stock behavior, and inflation patterns. The model operates on Markov process principles, enabling time series to transition between different states based on probabilistic mechanisms. The state transitions depend on current state probabilities, meaning each state influences subsequent state transitions through predefined transition matrices. Implementation typically involves maximum likelihood estimation with EM algorithm optimization for parameter recovery. Beyond providing accurate time series forecasts, this model enables detailed analysis and comparison of different regimes, along with their characteristic time series properties. The computational framework includes regime classification, parameter estimation for each state, and probability-weighted forecasting. This makes it a powerful analytical tool for time series analysis across multiple disciplines, with code implementations often featuring state path simulation and smoothed probability calculations.