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Measurement of causality change between multiple time series

Author

Listed:
  • Ryo Kinoshita

    (Graduate School of Economics, Osaka University)

  • Kosuke Oya

    (Graduate School of Economics & Center for the Study of Finance and Insurance, Osaka University)

Abstract

Structural change is gauged with the change of parameters in the model. In the case of multiple time series model, the causality between the time series also changes when there is a structural change. However the magnitude of change in causality is not clear in the case of structural change. We explore the measure of causality change between the time series and propose the test statistic whether there is any significance change in the causal relationship using frequency domain causality measure given by Geweke (1982) and Hosoya (1991). These procedures can be applied to error correction model which is non-stationary time series. The properties of the measure and test statistic are examined through the Monte Carlo simulation. As an example of application, the change in causality between United states and Japanese stock indexes is tested.

Suggested Citation

  • Ryo Kinoshita & Kosuke Oya, 2014. "Measurement of causality change between multiple time series," Discussion Papers in Economics and Business 14-09, Osaka University, Graduate School of Economics.
    • Handle: RePEc:osk:wpaper:1409
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    More about this item

    Keywords

    Causality; Frequency domain; Error correction model; Structural breaks;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other

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