Abstract
The state space methods or models provide a unified and flexible methodology and technology for handling a wide range of problems in time series analysis and are also applied in other fields including artificial intelligence. This chapter introduces the basic principle of state space methods and its application to SARIMAX modeling with Python, presents relationship between state space models and ARIMAX models using the local-level model, and lastly discusses the Markov switching model which is useful in econometrics and other disciplines.
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Huang, C., Petukhina, A. (2022). State Space Models and Markov Switching Models. In: Applied Time Series Analysis and Forecasting with Python. Statistics and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-13584-2_8
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DOI: https://doi.org/10.1007/978-3-031-13584-2_8
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