Estimating Optimal Transformations for Correlation and Coherence

Martin R. Young
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Martin R. Young: University of Michigan

A chapter in Computing Science and Statistics, 1992, pp 571-575 from Springer

Abstract: Abstract Coherency is a frequency domain measure of the linear association between two time series X(t) and Y(t). Since association between time series can in general be nonlinear, it may be useful to nonlinearly transform the time series prior to the coherency analysis. A broad class of nonlinear transformations of time series is proposed, and a procedure is described for estimating the transformations in this class which maximize the coherency between the transformed series. An efficient and numerically robust procedure for computing these optimal transformations is described, and the connection between this technique and Breiman and Friedman’s (1985) ACE technique for estimating optimal transformations of random variables is explored.

Keywords: Discrete Fourier Transform; Canonical Correlation; Canonical Correlation Analysis; Canonical Variate; American Statistical Association (search for similar items in EconPapers)
Date: 1992
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4612-2856-1_103

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DOI: 10.1007/978-1-4612-2856-1_103

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