  Markovian-regime-switching (MRS) models are commonly used for modelling
economic time series, including electricity prices where independent regime
models are used, since they can more accurately and succinctly capture
electricity price dynamics than dependent regime MRS models can. We can think
of these independent regime MRS models for electricity prices as a collection
of independent AR(1) processes, of which only one process is observed at each
time; which is observed is determined by a (hidden) Markov chain. Here we
develop novel, computationally feasible methods for MRS models with independent
regimes including forward, backward and EM algorithms. The key idea is to
augment the hidden process with a counter which records the time since the
hidden Markov chain last visited each state that corresponding to an AR(1)
process.
