The Performance of AICC as an Order Selection Criterion in ARMA Time Series Models
AbstractThis 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.
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Date of creation: 23 Jul 2003
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AICC; ARMA; under/over parameterization;
Find related papers by JEL classification:
- C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
- D5 - Microeconomics - - General Equilibrium and Disequilibrium
- D9 - Microeconomics - - Intertemporal Choice
This paper has been announced in the following NEP Reports:
- NEP-ALL-2003-07-29 (All new papers)
- NEP-ECM-2003-08-01 (Econometrics)
- NEP-ETS-2003-07-29 (Econometric Time Series)
- NEP-SEA-2003-07-29 (South East Asia)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Geweke, John F & Meese, Richard, 1981.
"Estimating Regression Models of Finite but Unknown Order,"
International Economic Review,
Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 22(1), pages 55-70, February.
- 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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