Inference on Changes in Interdependence Measures

  • Yuzo HosoyaEmail author
  • Kosuke Oya
  • Taro Takimoto
  • Ryo Kinoshita
Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST)


The causal relationship between the time series can be characterized with the moments of distributions for the series and the parameters of models such 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 magnitude of the change in causal relationships. In this chapter, we provide a measure of the change in causal relationships between a time series and the test statistic to determine whether such a change is associated with a structural change and is statistically significant. The properties of the measure and the test statistic are examined through a Monte Carlo simulation, and empirical examples are provided.


Change in measure High-frequency data Subsampling Variance estimation 


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Copyright information

© The Author(s) 2017

Authors and Affiliations

  • Yuzo Hosoya
    • 1
    Email author
  • Kosuke Oya
    • 2
  • Taro Takimoto
    • 3
  • Ryo Kinoshita
    • 4
  1. 1.Tohoku UniversitySendaiJapan
  2. 2.Osaka UniversityToyonakaJapan
  3. 3.Kyushu UniversityFukuokaJapan
  4. 4.Tokyo Keizai UniversityKokubunjiJapan

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