Statistics and Computing

, Volume 11, Issue 3, pp 229–240 | Cite as

A Conditional Density Approach to the Order Determination of Time Series

  • Bärbel F. Finkenstädt
  • Qiwei Yao
  • 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.

conditional density nonparametric regression entropy embedding dimension non-linear stochastic processes 


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Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • Bärbel F. Finkenstädt
    • 1
  • Qiwei Yao
    • 2
  • Howell Tong
    • 2
    • 3
  1. 1.Department of StatisticsUniversity of WarwickConventryUK
  2. 2.Department of StatisticsLondon School of EconomicsLondonUK
  3. 3.Department of Statistics and Actuarial ScienceThe University of Hong KongPokfulamHong Kong

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