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Parameter estimation and hypothesis testing in stationary vector time series

✍ Scribed by Yoshihide Kakizawa

Publisher: Elsevier Science
Year: 1997
Tongue: English
Weight: 474 KB
Volume: 33
Category: Article
ISSN: 0167-7152
DOI: 10.1016/s0167-7152(96)00131-9

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✦ Synopsis

Statistical inference for stationary time series is often based on the maximum likelihood principle, i.e., the maximization of the (quasi) likelihood of observations derived on Gaussian assumptions, although no such distributional assumptions are made. In this paper, we define the disparity measure between spectral density matrices and introduce the minimum distance principle for parameter estimation and hypothesis testing in spectral analysis of stationary vector time series.

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