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The Performance of AICC as an Order Selection Criterion in ARMA Time Series Models


  • Liew Khim Sen

    (Universiti Putra Malaysia)

  • Mahendran Shitan

    (Universiti Putra Malaysia)


This study is undertaken with the objective of investigating the performance of Akaike’s Information Corrected Criterion (AICC) as an order determination criterion for the selection of Autoregressive Moving-average or ARMA (p, q) time series models. A simulation investigation was carried out to determine the probability of the AICC statistic picking up the true model. Results obtained showed that the probability of the AICC criterion picking up the correct model was moderately good. The problem of over parameterization existed but under parameterization was found to be minimal. Hence, for any two comparable models, it is always safe to choose the one with lower order of p and q.

Suggested Citation

  • Liew Khim Sen & Mahendran Shitan, 2003. "The Performance of AICC as an Order Selection Criterion in ARMA Time Series Models," GE, Growth, Math methods 0307003, University Library of Munich, Germany.
  • Handle: RePEc:wpa:wuwpge:0307003
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    References listed on IDEAS

    1. Geweke, John & Meese, Richard, 1981. "Estimating regression models of finite but unknown order," Journal of Econometrics, Elsevier, vol. 16(1), pages 162-162, May.
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    More about this item


    AICC; ARMA; under/over parameterization;

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • D5 - Microeconomics - - General Equilibrium and Disequilibrium
    • D9 - Microeconomics - - Micro-Based Behavioral Economics

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