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- W2049969435 abstract "Nous étudions dans cette Note les modèles ARMA multivariés à changement de régime markovien. Dans ces modèles, les paramètres sont fonction des états d'une chaı̂ne de Markov non observable. Les méthodes développées dans la littérature statistique pour estimer ce type de modèles reposent généralement sur des hypothèses de stationnarité locale (i.e. dans chaque régime). Nous montrons que celle-ci n'est ni nécessaire ni suffisante pour assurer la stationnarité globale. Nous obtenons des conditions de stationnarité et calculons la fonction d'autocovariance de ces processus. Nous proposons des exemples destinés à illustrer les conditions de stationnarité. In this Note we consider multivariate ARMA models subject to Markov-switching. In these models, the parameters are allowed to depend on the state of an unobserved Markov chain. The methods developed in the statistical literature for estimating these models, typically impose local stationarity conditions, i.e., stationarity within each regime. We show that the local stationarity of the observed process is neither sufficient nor necessary to obtain the global stationarity. We derive stationarity conditions and we compute the autocovariance function of this nonlinear process. Some examples are proposed to illustrate the stationarity conditions." @default.
- W2049969435 created "2016-06-24" @default.
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- W2049969435 date "2000-06-01" @default.
- W2049969435 modified "2023-10-16" @default.
- W2049969435 title "Stationnarité des modèles ARMA à changement de régime markovien" @default.
- W2049969435 doi "https://doi.org/10.1016/s0764-4442(00)00302-5" @default.
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