A proof of consistency of the MLE for nonlinear Markov-switching AR processes

Autores: Fermín, Lisandro; Marcano, José; Rodríguez, Luis-Angel
URI: http://repositoriobibliotecas.uv.cl/handle/uvscl/7317
DOI: https://doi.org/10.1016/j.spl.2021.109347
Facultad: Facultad de Ingeniería
Departamento o escuela: CIMFAV
Grado o título: No disponible para descarga
Palabras clave: NONLINEAR AUTOREGRESSIVE PROCESS
MARKOV SWITCHING
ASYMPTOTIC NORMALITY
CONSISTENCY
HIDDEN MARKOV CHAIN
Editorial: Elsevier
Citación: Fermín, L., Marcano, J., y Rodríguez, L. A. (2022). A proof of consistency of the MLE for nonlinear Markov-switching AR processes. Elsevier. http://repositoriobibliotecas.uv.cl/handle/uvscl/7317
Resumen: We 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.

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