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A Methodology For Selecting Subset Autoregressive Time Series Models

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  • Gwo‐Hsing Yu
  • Yow‐Chang Lin

Abstract

. In time series modelling, subset models are often desirable, especially when the data exhibit some form of periodic behaviour with a range of different natural periods in terms of days, weeks, months and years. Recently, Hokstad proposed a method based on personal judgement for selecting the first tentative model to obtain the best subset autoregressive model. The subjective approach adopted in the Hokstad method is a disadvantage in building up a computer program which could automatically select the appropriate model of a given time series. In this paper, we propose overcoming this disadvantage by employing the inverse autocorrelation function to select the first tentative model. In addition to sets of synthetic data, some well‐known real series such as the D, E and F series of Box and Jenkins and the Canadian lynx data are analysed to validate the proposed method. The results indicate that the method can successfully detect the true model for a given time series.

Suggested Citation

  • Gwo‐Hsing Yu & Yow‐Chang Lin, 1991. "A Methodology For Selecting Subset Autoregressive Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 12(4), pages 363-373, July.
    • Handle: RePEc:bla:jtsera:v:12:y:1991:i:4:p:363-373
    DOI: 10.1111/j.1467-9892.1991.tb00090.x
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    Cited by:

    1. R. H. Glendinning, 2000. "Estimating the Inverse Autocorrelation Function from Outlier Contaminated Data," Computational Statistics, Springer, vol. 15(4), pages 541-565, December.
    2. N. K. Unnikrishnan, 2004. "Bayesian Subset Model Selection for Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(5), pages 671-690, September.
    3. Roberto Baragona & Francesco Battaglia & Domenico Cucina, 2004. "Estimating threshold subset autoregressive moving-average models by genetic algorithms," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 39-61.
    4. H. Glendinning, Richard, 2001. "Selecting sub-set autoregressions from outlier contaminated data," Computational Statistics & Data Analysis, Elsevier, vol. 36(2), pages 179-207, April.

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