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

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Author Info
Liew Khim Sen (Universiti Putra Malaysia)
Mahendran Shitan (Universiti Putra Malaysia)

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Abstract

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.

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Paper provided by EconWPA in its series GE, Growth, Math methods with number 0307003.

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Date of creation: 23 Jul 2003
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Handle: RePEc:wpa:wuwpge:0307003

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Related research
Keywords: AICC; ARMA; under/over parameterization;

Find related papers by JEL classification:
C6 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming
D5 - Microeconomics - - General Equilibrium and Disequilibrium
D9 - Microeconomics - - Intertemporal Choice and Growth

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  1. 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. [Downloadable!] (restricted)
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This page was last updated on 2009-11-25.

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