Fermín, LisandroMarcano, JoséRodríguez, Luis-Angel2022-11-302022-11-302022http://repositoriobibliotecas.uv.cl/handle/uvscl/7317We propose a new approach to demonstrate the consistency of the maximum likelihood estimator for nonlinear Markov-switching AR processes (abbreviated MS-NAR). We obtain a uniform exponential memory loss property for the prediction filter by approximating it by a filter with finite memory. From the -mixing property for the MS-NAR process we obtain an ergodic theorem. Finally, we show that in the linear and Gaussian case our assumptions are fully satisfied.NONLINEAR AUTOREGRESSIVE PROCESSMARKOV SWITCHINGASYMPTOTIC NORMALITYCONSISTENCYHIDDEN MARKOV CHAINArticulohttps://doi.org/10.1016/j.spl.2021.109347