Local Gaussian Cross-Spectrum Analysis

Lars Arne Jordanger () and Dag Tjøstheim
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Lars Arne Jordanger: Department of Computer Science, Electrical Engineering and Mathematical Sciences, Faculty of Engineering and Science, Western Norway University of Applied Sciences, 5020 Bergen, Norway
Dag Tjøstheim: Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Bergen, 5020 Bergen, Norway

Econometrics, 2023, vol. 11, issue 2, 1-27

Abstract: The ordinary spectrum is restricted in its applications, since it is based on the second-order moments (auto- and cross-covariances). Alternative approaches to spectrum analysis have been investigated based on other measures of dependence. One such approach was developed for univariate time series by the authors of this paper using the local Gaussian auto-spectrum based on the local Gaussian auto-correlations . This makes it possible to detect local structures in univariate time series that look similar to white noise when investigated by the ordinary auto-spectrum. In this paper, the local Gaussian approach is extended to a local Gaussian cross-spectrum for multivariate time series. The local Gaussian cross-spectrum has the desirable property that it coincides with the ordinary cross-spectrum for Gaussian time series, which implies that it can be used to detect non-Gaussian traits in the time series under investigation. In particular, if the ordinary spectrum is flat, then peaks and troughs of the local Gaussian spectrum can indicate nonlinear traits, which potentially might reveal local periodic phenomena that are undetected in an ordinary spectral analysis.

Keywords: local periodicities; local co-spectrum; local quadrature-spectrum; local amplitude-spectrum; local phase-spectrum; heatmap; distance plot; spectral plots (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2023
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