Methods for Multivariate Time Series

Tomas Cipra ()
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Tomas Cipra: Charles University, Faculty of Mathematics and Physics

Chapter Chapter 12 in Time Series in Economics and Finance, 2020, pp 305-349 from Springer

Abstract: Abstract Most procedures for univariate time series from previous chapters can be generalized for multivariate time series, where instead of scalar values yt we observe m-variate vector values yt = (y1t, …, ymt)′ in time as realizations of a vector random process (see Sect. 2.1 ). The transfer from univariate to multivariate dimension mostly means only higher formal and numerical complexity of methods described in previous parts of this text (decomposition methods, methods for linear and nonlinear processes, and the like), which will be demonstrated briefly in this section by means of examples of stationary multivariate time series. Later we shall see that such a parallel description of several scalar processes brings to the analysis further elements that have exclusively the multivariate character (examples are the routine methodology VAR for multivariate time series, the cointegration among particular univariate components, and others).

Date: 2020
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DOI: 10.1007/978-3-030-46347-2_12

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