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LIST OF CHAPTERS
- Notation, definitions and basic inference
- Traditional linear time series models
- The frequency domain
- Dynamic linear models
- State-space TVAR models
- General state-space models
- Mixture models in time series
- Topics and examples in multiple time series
- Vector AR and ARMA models
- Multivariate DLMs and covariance models
The book is supported by a web page with data and code from many examples in the book
that will be of use in teaching support as well as time series analysis applications.
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