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A small-sample criterion based on Kullback's symmetric divergence for vector autoregressive modeling

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  • Hafidi, Bezza

Abstract

In this note, we propose a small-sample criterion KICc for selecting vector autoregressive models. KICc is an approximately unbiased estimator of the expected Kullback's symmetric divergence. A simulation study shows that KICc provides better model order choices than the KIC criterion in small samples.

Suggested Citation

  • Hafidi, Bezza, 2006. "A small-sample criterion based on Kullback's symmetric divergence for vector autoregressive modeling," Statistics & Probability Letters, Elsevier, vol. 76(15), pages 1647-1654, September.
  • Handle: RePEc:eee:stapro:v:76:y:2006:i:15:p:1647-1654
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    References listed on IDEAS

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    1. Joseph E. Cavanaugh, 2004. "Criteria for Linear Model Selection Based on Kullback's Symmetric Divergence," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 46(2), pages 257-274, June.
    2. Cavanaugh, Joseph E., 1999. "A large-sample model selection criterion based on Kullback's symmetric divergence," Statistics & Probability Letters, Elsevier, vol. 42(4), pages 333-343, May.
    3. Clifford M. Hurvich & Chih‐Ling Tsai, 1993. "A Corrected Akaike Information Criterion For Vector Autoregressive Model Selection," Journal of Time Series Analysis, Wiley Blackwell, vol. 14(3), pages 271-279, May.
    4. Hirotugu Akaike, 1969. "Fitting autoregressive models for prediction," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 21(1), pages 243-247, December.
    5. Hafidi, B. & Mkhadri, A., 2006. "A corrected Akaike criterion based on Kullback's symmetric divergence: applications in time series, multiple and multivariate regression," Computational Statistics & Data Analysis, Elsevier, vol. 50(6), pages 1524-1550, March.
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