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Modelling time series when mean and variability both change

Author

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  • Withers, C.S.
  • Krouse, D.P.
  • Pearson, C.P.
  • Nadarajah, S.

Abstract

An extended least-squares method is described which allows us to model the location and scale of a process parametrically without specifying any parametric form for the distribution of the errors. The degree of the associated polynomials is chosen using a step-down method on a table of p-values. A pseudo-likelihood ratio test is given. The concepts of upper and lower return levels are extended to non-stationary series. The method is applied to annual means and extremes of Auckland temperatures. Evidence is found that the mean is changing non-linearly and the variance is also changing for all three series.

Suggested Citation

  • Withers, C.S. & Krouse, D.P. & Pearson, C.P. & Nadarajah, S., 2008. "Modelling time series when mean and variability both change," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 77(1), pages 57-63.
    • Handle: RePEc:eee:matcom:v:77:y:2008:i:1:p:57-63
    DOI: 10.1016/j.matcom.2007.01.039
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    References listed on IDEAS

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    1. S. Nadarajah & J. Shiau, 2005. "Analysis of Extreme Flood Events for the Pachang River, Taiwan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 19(4), pages 363-374, August.
    2. McAleer, Michael, 2005. "Automated Inference And Learning In Modeling Financial Volatility," Econometric Theory, Cambridge University Press, vol. 21(1), pages 232-261, February.
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    Cited by:

    1. Jin, Hao & Tian, Zheng & Qin, Ruibing, 2009. "Subsampling tests for the mean change point with heavy-tailed innovations," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(7), pages 2157-2166.

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