JSM 2005 - Toronto

Abstract #303393

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Activity Number: 141
Type: Contributed
Date/Time: Monday, August 8, 2005 : 10:30 AM to 12:20 PM
Sponsor: General Methodology
Abstract - #303393
Title: On the Unique Representation of NonGaussian Multivariate Linear Processes
Author(s): Lop-hing Ho*+ and Kung-Sik Chan
Companies: Wichita State University and The University of Iowa
Address: Department of Math and Stat, Wichita, KS, 67260-0033, United States
Keywords: Fourier transform ; Stationarity ; Time Series
Abstract:

NonGaussian univariate linear processes have been shown to admit essentially unique moving-average representation under various regularity conditions despite generally having nonunique moving-average representations. In this paper, we extend the one-dimensional result to multivariate processes. We prove that if the components of the error process are independent, the nonGaussian multivariate moving-average representation is unique up to shifts in the indices and invertible multiplicative factors under the condition the transfer function is of full-rank, plus other mild conditions. When mild intercomponent-dependent conditions are assumed, we prove the representation is unique, allowing only a single shift and an invertible matrix multiplier.

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