Markov-switching autoregressive log-likelihood function
Markov-switching autoregressive log-likelihood function
This function computes the log-likelihood for a markov-switching autoregressive model and uses the Hamilton smoother to obtain smoothed probabilities of each state. This is also the expectation step in the Expectation Maximization algorithm for a Markov-switching autoregressive model.
ExpectationM_MSARmdl(theta, mdl, k)
Arguments
theta: Vector of model parameters.
mdl: List with model attributes.
k: Integer determining the number of regimes.
Returns
List which includes log-likelihood and smoothed probabilities of each regime.