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

Klaus Neusser ()

Chapter 12 in Time Series Econometrics, 2016, pp 215-224 from Springer

Abstract: 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

Keywords: Covariance Function; Univariate Case; Companion Matrix; Causal Representation; Companion Form (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sptchp:978-3-319-32862-1_12

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DOI: 10.1007/978-3-319-32862-1_12

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