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

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dc.contributor.authorFermín, Lisandro
dc.contributor.authorMarcano, José
dc.contributor.authorRodríguez, Luis-Angel
dc.date.accessioned2022-11-30T02:46:19Z
dc.date.available2022-11-30T02:46:19Z
dc.date.issued2022
dc.description.abstractWe 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.en_ES
dc.facultadFacultad de Ingenieríaen_ES
dc.file.nameFermin_Pro2022.pdf
dc.identifier.doihttps://doi.org/10.1016/j.spl.2021.109347
dc.identifier.urihttp://repositoriobibliotecas.uv.cl/handle/uvscl/7317
dc.languageen
dc.publisherElsevier
dc.sourceStatistics & Probability Letters
dc.subjectNONLINEAR AUTOREGRESSIVE PROCESSen_ES
dc.subjectMARKOV SWITCHINGen_ES
dc.subjectASYMPTOTIC NORMALITYen_ES
dc.subjectCONSISTENCYen_ES
dc.subjectHIDDEN MARKOV CHAINen_ES
dc.titleA proof of consistency of the MLE for nonlinear Markov-switching AR processes
dc.typeArticulo
uv.departamentoCIMFAV
uv.notageneralNo disponible para descarga

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