Moving dynamic principal component analysis for non-stationary multivariate time series

Fayed Alshammri () and Jiazhu Pan ()
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Fayed Alshammri: University of Strathclyde
Jiazhu Pan: University of Strathclyde

Computational Statistics, 2021, vol. 36, issue 3, No 32, 2247-2287

Abstract: Abstract This paper proposes an extension of principal component analysis to non-stationary multivariate time series data. A criterion for determining the number of final retained components is proposed. An advance correlation matrix is developed to evaluate dynamic relationships among the chosen components. The theoretical properties of the proposed method are given. Many simulation experiments show our approach performs well on both stationary and non-stationary data. Real data examples are also presented as illustrations. We develop four packages using the statistical software R that contain the needed functions to obtain and assess the results of the proposed method.

Keywords: Dimension reduction; Eigenanalysis; Moving cross-covariance; Moving cross-correlation; Multivariate time series; Non-stationary data (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s00180-021-01081-8

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