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What are some reference sources for understanding Markov switching models?

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Firstly, for understanding the Markov switching models, a nice knowledge of Markov models and the way they work. Most importantly, an idea of time series models and how they work, is very important.

I found this tutorial good enough for getting up to speed with the concept.

This is another tutorial on a similar application of the switching model, which is the regime switching model.

The statsmodels library has a nice support for building the Morkov switching models.

Here is one simple and quick Python tutorial which uses the statsmodels library.

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    $\begingroup$ The link to the Python tutorial is broken, the code can be found here: github.com/ChadFulton/pymar $\endgroup$
    – Zhubarb
    Commented Feb 22, 2017 at 8:34
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    $\begingroup$ How to forecast future values using markov regression, I am using statsmodel API? $\endgroup$
    – yogesh agrawal
    Commented Jan 9, 2020 at 6:08
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    $\begingroup$ @yogeshagrawal Have you found out how to? I was working with MarkovRegression and MarkovAutoregression today but encountered NotImplementedException... $\endgroup$
    – matt
    Commented Apr 26, 2020 at 12:59
  • $\begingroup$ sorry i couldn't found out @matt $\endgroup$
    – yogesh agrawal
    Commented Apr 27, 2020 at 4:27
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Markov switching models are a type of statistical model used to capture transitions between different states in a time series. They are a generalization of the classical Markov chain model and can be used to model complex state transition processes.

To learn more about Markov switching models, some reference sources that you might find helpful include:

  • Markov Switching Models by James D. Hamilton (1994): This is a classic book that provides a detailed introduction to Markov switching models and their applications in economics and finance.

  • Markov Switching Autoregressive Models by Andrew C. Harvey (1990): This book provides a comprehensive overview of Markov switching autoregressive models, including their estimation, inference, and applications.

  • Modeling Economic Time Series with Markov Switching Regime by Bo Honoré and Lars Peter Hansen (2020): This paper provides a modern overview of Markov switching models and their applications in economics and finance. It covers recent developments in the field and discusses current challenges and future directions.

  • Markov Switching Regimes in Macroeconomic Time Series by Peter C. B. Phillips and Jianjun Lu (1996): This paper presents an overview of Markov switching models and their application to macroeconomic time series data. It covers the basic theory and methodology of these models, as well as their empirical performance and limitations.

  • Markov switching autoregression models Python implementation: This notebook provides an example of the use of Markov switching models in statsmodels to replicate a number of results presented in Kim and Nelson (1999). It applies the Hamilton (1989) filter the Kim (1994) smoother.

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