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Stationary Time Series Models: Vector Autoregressive Moving-Average Processes (VARMA Processes)

In: Time Series Econometrics

Author

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  • Klaus Neusser

Abstract

The most important class of models is obtained by requiring {X t } to be the solution of a linear stochastic difference equation with constant coefficients. In analogy to the univariate case, this leads to the theory of vector autoregressive moving-average processes (VARMA processes or just ARMA processes). Vector autoregressive moving-average process see alsoVARMA process VARMA process

Suggested Citation

  • Klaus Neusser, 2016. "Stationary Time Series Models: Vector Autoregressive Moving-Average Processes (VARMA Processes)," Springer Texts in Business and Economics, in: Time Series Econometrics, chapter 12, pages 215-224, Springer.
    • Handle: RePEc:spr:sptchp:978-3-319-32862-1_12
    DOI: 10.1007/978-3-319-32862-1_12
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