Frequency domain tests of multivariate Gaussianity and nonlinearity

Wong, Woon K. ORCID: https://orcid.org/0000-0001-6892-9965 1997. Frequency domain tests of multivariate Gaussianity and nonlinearity. Journal of Time Series Analysis 18 (2) , pp. 181-194. 10.1111/1467-9892.00045

Full text not available from this repository.

Abstract

A stationary multivariate time series {Xt} is defined as linear if it can be written in the form Xt = ∑∞j=−∞Ajet−j where Aj are square matrices and et are independent and identically distributed random vectors. If the et} are normally distributed, then {Xtis a multivariate Gaussian linear process. This paper is concerned with the testing of departures of a vector stationary process from multivariate Gaussianity and linearity using the bispectral approach. First the definition and properties of cumulants of random matrices are used to obtain the expressions for the higher-order cumulant and spectral vectors of a linear vector process as defined above. Then it is shown that linearity of a vector process implies constancy of the modulus square of its normalized higher-order spectra whereas the component of such a vector process does not necessarily have a linear representation. Finally, statistics for the testing of multivariate Gaussianity and linearity are proposed.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics)
Subjects:	H Social Sciences > HF Commerce H Social Sciences > HG Finance
Uncontrolled Keywords:	Stationary multivariate linear and Gaussian time series; cumulants of random matrices; linear representation; higher-order cumulant and spectral vectors; testing of multivariate Gaussianity and linearity
Publisher:	John Wiley & Sons
ISSN:	1467-9892
Last Modified:	24 Oct 2022 11:44
URI:	https://orca.cardiff.ac.uk/id/eprint/49186

Citation Data

Cited 8 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item