周波数領域における時系列間の因果性の変化の検証  [in Japanese] Measurement of Causality Change between Multiple Time Series  [in Japanese]

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Author(s)

Abstract

時系列間の因果性の検証にはGrangerによる因果性検定など代表的な方法があるが,本稿では構造変化が因果性の大きさに,どのような変化をもたらしたかを検証する方法を提案する.Hosoya (1991)において定義された周波数領域上での因果性測度を利用し,構造変化の時点を既知とした上で,因果性の変化の程度を測り,有意な変化が生じているかどうかを検証するためのWald検定統計量を提案した.この検定統計量では各周波数における因果性の変化を検出することが可能であり,誤差修正モデルにおいても応用が可能である.検定統計量の有限標本における特性をモンテカルロ実験によって確認し,応用例として日米の株価指数を用いた実証分析を行った.

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.

Journal

  • Journal of the Japan Statistical Society, Japanese Issue

    Journal of the Japan Statistical Society, Japanese Issue 44(1), 19-40, 2014

    Japan Statistical Society

Codes

  • NII Article ID (NAID)
    110009864634
  • NII NACSIS-CAT ID (NCID)
    AA11989749
  • Text Lang
    JPN
  • ISSN
    0389-5602
  • NDL Article ID
    025868045
  • NDL Call No.
    Z3-1003
  • Data Source
    NDL  NII-ELS  J-STAGE  NDL-Digital 
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