  In this paper we derive the asymptotic properties of the least squares
estimator (LSE) of autoregressive moving-average (ARMA) models with regime
changes under the assumption that the errors are uncorrelated but not
necessarily independent. Relaxing the independence assumption considerably
extends the range of application of the class of ARMA models with regime
changes. Conditions are given for the consistency and asymptotic normality of
the LSE. A particular attention is given to the estimation of the asymptotic
covariance matrix, which may be very different from that obtained in the
standard framework. The theoretical results are illustrated by means of Monte
Carlo experiments.
