Volume 19, 2015
|Page(s)||268 - 292|
|Published online||06 October 2015|
Consistency of the maximum likelihood estimate for non-homogeneous Markov–switching models
Revised: 12 September 2014
We prove the consistency of the maximum likelihood estimator for a large family of models generalizing the well known Markov-switching AutoRegressive (MS-AR) models by letting the transition probabilities vary in time and depend on covariates. We illustrate our theoretical result on the famous MacKenzie River lynx dataset and on a multi-site model for downscaling rainfall.
Mathematics Subject Classification: 62F12 / 62M05
Key words: Markov-switching autoregressive process / non-homogeneous hidden Markov process / maximum likelihood / consistency / stability / lynx data
© EDP Sciences, SMAI 2015
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.