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A comparison of some of pattern identification methods for order determination of mixed ARMA models

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  • Chan, Wai-Sum

Abstract

Model identification is a crucial step in time-series modelling. The orthodox Box-Jenkins (BJ) identification examines the patterns of the sample autocorrelation function (SACF) and the sample partial autocorrelation function (SPACF). However, for mixed ARMA processes, the SACF and SPACF often exhibit similar behaviour, which makes the identification much more difficult. Recently, identification methods using the patterns of some functions of the autocorrelations have been proposed to supplement the BJ methods. This paper studies some of these proposed procedures. Their performances for order selection of a mixed ARMA process are compared with an expert system in a simulation study. Comments on each individual identification method are also given.

Suggested Citation

  • Chan, Wai-Sum, 1999. "A comparison of some of pattern identification methods for order determination of mixed ARMA models," Statistics & Probability Letters, Elsevier, vol. 42(1), pages 69-79, March.
  • Handle: RePEc:eee:stapro:v:42:y:1999:i:1:p:69-79
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    References listed on IDEAS

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    1. Bertrand Mareschal & Guy Melard, 1988. "Algorithm AS-237: The corner method for identifying autoregressive-moving average models," ULB Institutional Repository 2013/13704, ULB -- Universite Libre de Bruxelles.
    2. Keh‐Shin Lii, 1985. "Transfer Function Model Order And Parameter Estimation," Journal of Time Series Analysis, Wiley Blackwell, vol. 6(3), pages 153-169, May.
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    Cited by:

    1. Wen-chuan Wang & Kwok-wing Chau & Dong-mei Xu & Xiao-Yun Chen, 2015. "Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2655-2675, June.
    2. Befekadu Chemere & Jiyung Kim & Baehun Lee & Moonju Kim & Byongwan Kim & Kyungil Sung, 2018. "Detecting Long-Term Dry Matter Yield Trend of Sorghum-Sudangrass Hybrid and Climatic Factors Using Time Series Analysis in the Republic of Korea," Agriculture, MDPI, vol. 8(12), pages 1-10, December.

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