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On Nonparametric and Semiparametric Testing for Multivariate Time Series

Author

Listed:
  • Yoshihiro Yajima

    (Faculty of Economics, The University of Tokyo)

  • Yasumasa Matsuda

    (Faculty of Economics, Niigata University)

Abstract

We formulate nonparametric and semiparametric hypothesis testing of multivariate stationary time series in a unified fashion and propose new test statistics based on estimators of the spectral density matrix. The limiting distributions of these test statistics under null hypotheses are always normal distributions and they are implemented easily for practical use. While if null hypotheses are false, as n, the sample size, goes to infinity, they diverge to infinity faster than the parametric rate n1/2. They can be applied to various null hypotheses such as the independence between the component series, the equality of the autocovariance functions or the autocorrelation functions of the component series, and the separability of the covariance matrix function.

Suggested Citation

  • Yoshihiro Yajima & Yasumasa Matsuda, 2003. "On Nonparametric and Semiparametric Testing for Multivariate Time Series," CIRJE F-Series CIRJE-F-253, CIRJE, Faculty of Economics, University of Tokyo.
    • Handle: RePEc:tky:fseres:2003cf253
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    Cited by:

    1. Dette, Holger & Paparoditis, Efstathios, 2008. "Bootstrapping frequency domain tests in multivariate time series with an application to comparing spectral densities," Technical Reports 2008,28, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

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