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A conditional density approach to the order determination of time series

By Qiwei Yao, Bärbel F. Finkenstädt and Howell Tong


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

Topics: HA Statistics
Publisher: Springer Netherlands
Year: 2001
DOI identifier: 10.1023/A:1016600304293
OAI identifier:
Provided by: LSE Research Online
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