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 language | English |
|---|---|
| Pages (from-to) | 553-578 |
| Number of pages | 26 |
| Journal | Journal of Time Series Analysis |
| Volume | 18 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 1 Jan 1997 |
Keywords
- Asymptotic normality
- Consistency
- Hidden Markov chain
- Maximum likelihood
- Non-linear time series models
- Switching models
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