ExpectationM_HMmdl function

Hidden Markov model log-likelihood function

This function computes the log-likelihood for a Hidden Markov 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_HMmdl(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 value and smoothed probabilities of each regime.