Abstract
In the last chapter attention was given to the determination of the state vector1 ΞΎ for given observations Y and known parameters A. In this chapter the maximum likelihood estimation of the parameters \(\lambda = (\theta \prime ,\rho \prime ,\xi {\prime _0})\prime \) of an MS-VAR model is considered. The aim of this chapter is (i.) to provide the reader with an introduction to the methodological issues of ML estimation of MS-VAR models in general, (ii.) to propose with the EM algorithm an estimation technique for all discussed types of the MS-VAR models, (iii.) to inform the reader about alternative techniques which can be used for special purposes or model extensions and (iv.) to give some basic asymptotic results.
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References
The reader is referred to LΓΌtkepohl [1991, sec. 5.4] or Hamilton [1994b, ch.12] for an introduction to the basic principles underlying Bayesian analysis with applications to time-invariant VAR models
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Β© 1997 Springer-Verlag Berlin Heidelberg
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Krolzig, HM. (1997). Maximum Likelihood Estimation. In: Markov-Switching Vector Autoregressions. Lecture Notes in Economics and Mathematical Systems, vol 454. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-51684-9_7
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DOI: https://doi.org/10.1007/978-3-642-51684-9_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63073-9
Online ISBN: 978-3-642-51684-9
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