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A Unified Approach To Arma Model Identification And Preliminary Estimation

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  • D. Piccolo
  • G. Tunnicliffe Wilson

Abstract

. This paper reviews several different methods for identifying the orders of autoregressive‐moving average models for time series data. The case is made that these have a common basis, and that a unified approach may be found in the analysis of a matrix G, defined to be the covariance matrix of forecast values. The estimation of this matrix is considered, emphasis being placed on the use of high order autoregression to approximate the predictor coefficients. Statistical procedures are proposed for analysing G, and identifying the model orders. A simulation example and three sets of real data are used to illustrate the procedure, which appears to be a very useful tool for order identification and preliminary model estimation.

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  • D. Piccolo & G. Tunnicliffe Wilson, 1984. "A Unified Approach To Arma Model Identification And Preliminary Estimation," Journal of Time Series Analysis, Wiley Blackwell, vol. 5(3), pages 183-204, May.
    • Handle: RePEc:bla:jtsera:v:5:y:1984:i:3:p:183-204
    DOI: 10.1111/j.1467-9892.1984.tb00386.x
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    Cited by:

    1. Bhansali, Rajendra J., 2020. "Model specification and selection for multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 175(C).
    2. Hella, Heikki, 2003. "On robust ESACF identification of mixed ARIMA models," Bank of Finland Scientific Monographs, Bank of Finland, volume 0, number sm2003_027.

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