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Performance of lag length selection criteria in three different situations

  • Asghar, Zahid
  • Abid, Irum

Determination of the lag length of an autoregressive process is one of the most difficult parts of ARIMA modeling. Various lag length selection criteria (Akaike Information Criterion, Schwarz Information Criterion, Hannan-Quinn Criterion, Final Prediction Error, Corrected version of AIC) have been proposed in the literature to overcome this difficulty. We have compared these criteria for lag length selection for three different cases that is under normal errors, under non-normal errors and under structural break by using Monte Carlo simulation. It has been found that SIC is the best for large samples and no criteria is useful for selecting true lag length in presence of regime shifts or shocks to the system.

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File URL: https://mpra.ub.uni-muenchen.de/40042/1/MPRA_paper_40042.pdf
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 40042.

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Date of creation: Apr 2007
Date of revision:
Handle: RePEc:pra:mprapa:40042
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  1. repec:ebl:ecbull:v:3:y:2004:i:33:p:1-9 is not listed on IDEAS
  2. Akaike, Hirotugu, 1981. "Likelihood of a model and information criteria," Journal of Econometrics, Elsevier, vol. 16(1), pages 3-14, May.
  3. Venus Khim-Sen Liew, 2004. "Which Lag Length Selection Criteria Should We Employ?," Economics Bulletin, AccessEcon, vol. 3(33), pages 1-9.
  4. Chow, Gregory C., 1981. "A comparison of the information and posterior probability criteria for model selection," Journal of Econometrics, Elsevier, vol. 16(1), pages 21-33, May.
  5. Hirotugu Akaike, 1969. "Fitting autoregressive models for prediction," Annals of the Institute of Statistical Mathematics, Springer, vol. 21(1), pages 243-247, December.
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