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Time Varying Coefficient AR and VAR Models

  • Xing-Qi Jiang
Chapter
Part of the Statistics for Engineering and Physical Science book series (ISS)

Abstract

Autoregressive (AR) models are very useful for time series analysis. As shown in Figure 11.1, there is correspondence between the AR model and the autocovariance function and the power spectrum of an univariate stationary time series. Therefore, if an AR model is estimated from a time series, then the estimates of the autocovariance function and the power spectrum are obtained immediately. For the analysis of multivariate time series, Figure 11.2 shows the relation between the vector autoregressive (VAR) model and the cross-covariance function, the cross-spectrum, and the relative power contribution. Akaike and Nakagawa (1988), and Kitagawa (1993) developed procedures and programs for the analysis of stationary time series by using the AR and VAR models.

Keywords

Kalman Filter State Space Representation Autocovariance Function Nonstationary Time Series Power Contribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag New York, Inc. 1999

Authors and Affiliations

  • Xing-Qi Jiang
    • 1
  1. 1.Department of EconomicsAsahikawa UniversityAsahikawa, HokkaidoJapan

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