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Robust Identification of Autoregressive Moving Average Models

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  • G. Masarotto

Abstract

We introduce a class of robust estimates for the partial autocorrelation function of a univariate stationary time series and show that it is possible to produce an estimate of the autocorrelation function from the estimated partial autocorrelation coefficients. These statistics seem suitable for the preliminary identification of the order, p and q of an ARMA (p, q) model when the observed series contains a few aberrant observations.

Suggested Citation

  • G. Masarotto, 1987. "Robust Identification of Autoregressive Moving Average Models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(2), pages 214-220, June.
    • Handle: RePEc:bla:jorssc:v:36:y:1987:i:2:p:214-220
    DOI: 10.2307/2347553
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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. H. Glendinning, Richard, 2001. "Selecting sub-set autoregressions from outlier contaminated data," Computational Statistics & Data Analysis, Elsevier, vol. 36(2), pages 179-207, April.
    3. Hella, Heikki, 2003. "On robust ESACF identification of mixed ARIMA models," Bank of Finland Scientific Monographs, Bank of Finland, volume 0, number sm2003_027, March.

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