Abstract
So far, we have concentrated on stationary VARMA processes for I(0) variables. In this chapter, the variables are allowed to be I(1) and may be cointegrated. As we have seen in Chapter 12, one of the problems in dealing with VARMA models is the nonuniqueness of their parameterization. For inference purposes, it is necessary to focus on a unique representation of a DGP. For stationary VARMA processes, we have considered the echelon form to tackle the identification problem. In the next section, this representation of a VARMA process will be combined with the error correction (EC) form. Thereby it is again possible to separate the long-run cointegration relations from the short-term dynamics. The resulting representation turns out to be a convenient framework for modelling cointegrated variables.
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© 2005 Springer-Verlag Berlin Heidelberg
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Lütkepohl, H. (2005). Cointegrated VARMA Processes. In: New Introduction to Multiple Time Series Analysis. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27752-1_14
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DOI: https://doi.org/10.1007/978-3-540-27752-1_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40172-8
Online ISBN: 978-3-540-27752-1
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