Titlebook: Characterizing Interdependencies of Multiple Time Series; Theory and Applicati Yuzo Hosoya,Kosuke Oya,Ryo Kinoshita Book 2017 The Author(s)

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Yuzo Hosoya,Kosuke Oya,Ryo KinoshitaPresents an approach to characterizing the interdependencies of multivariate time series by means of the basic concept of the one-way effect.Shows how the third-series effect is eliminated with least

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Voice over IP, Internettelefonie,as frequency-wise) measures of one-way effect, reciprocity, and association. Section?. defines the Granger and Sims non-causality and establishes their equivalence for a general class of (not necessarily stationary) second-order processes. Sections?. and . define the overall and frequency-wise one-w

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https://doi.org/10.1007/978-3-8348-9205-8uated and applied to practical situations. Section?. discusses the statistical inference on those measures using the standard asymptotic theory of the Whittle likelihood inference for stationary multivariate ARMA processes. The point is the use of simulation-based estimations of the covariance matri

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Outsourcing von IT-Dienstleistungen,h as the vector ARMA model from previous chapters. Thus, the changes in the moments of the time series and the model parameters suggest the possibility of a change in causal relationships as we expected. However, the changes in the moments and the model parameters do not tell us much about the magni

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Introduction,e on empirical causal analysis and places the theme in a broader perspective, comparing a variety of conflicting views on how certain statistical associations can be viewed as causal. Among others, alluded to is the field experiment model of detecting causal effects by Neyman (.) and its reliance on