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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Representation of the Partial Measures,ervention is known to sometimes incur phenomena such as spurious or indirect causality attributable to possible feedback from the series. To address the problem, this chapter introduces an operational way to define the partial causality and allied concepts between a pair of processes. The third-effe

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Inference Based on the Vector Autoregressive and Moving Average Model,uated 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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Inference on Changes in Interdependence Measures,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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Characterizing Interdependencies of Multiple Time SeriesTheory and Applicati

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Book 2017frequency domain method includes the Granger noncausality test as a special case..Chapters 2 and 3 of the book introduce an improved version of the basic concepts for measuring the one-way effect, reciprocity, and association of multiple time series, which were originally proposed by Hosoya. Then th

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