On white noises driven by hidden Markov chains

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Abstract

We consider a time series model where the variance of the underlying process depends on the state of a non-observed Markov chain. Maximum likelihood estimates are shown to be consistent. Estimators with asymptotic Gaussian distribution are proposed. Prediction and identification are also mentioned. This is illustrated by means of real and simulated data sets.

Original languageEnglish
Pages (from-to)553-578
Number of pages26
JournalJournal of Time Series Analysis
Volume18
Issue number6
DOIs
Publication statusPublished - 1 Jan 1997

Keywords

  • Asymptotic normality
  • Consistency
  • Hidden Markov chain
  • Maximum likelihood
  • Non-linear time series models
  • Switching models

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