UNSPECIFIED (2001) A conditional density approach to the order determination of time series. STATISTICS AND COMPUTING, 11 (3). pp. 229-240. ISSN 0960-3174

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Abstract

The study focuses on the selection of the order of a general time series process via the conditional density of the latter, a characteristic of which is that it remains constant for every order beyond the true one. Using simulated time series from various nonlinear models we illustrate how this feature can be traced from conditional density estimation. We study whether two statistics derived from the likelihood function can serve as univariate statistics to determine the order of the process. It is found that a weighted version of the log likelihood function has desirable robust properties in detecting the order of the process.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QA Mathematics
Journal or Publication Title:	STATISTICS AND COMPUTING
Publisher:	KLUWER ACADEMIC PUBL
ISSN:	0960-3174
Date:	July 2001
Volume:	11
Number:	3
Number of Pages:	12
Page Range:	pp. 229-240
Publication Status:	Published
URI:	http://wrap.warwick.ac.uk/id/eprint/12056

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