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Determining The Order Of A Vector Autoregression When The Number Of Component Series Is Large

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  • Sergio G. Koreisha
  • Tarmo Pukkila

Abstract

. We contrast the performance of several methods used for identifying the order of vector autoregressive (VAR) processes when the number K of component series is large. Through simulation experiments we show that their performance is dependent on K, the number of nonzero elements in the polynomial matrices of the VAR parameters and the permitted upper limit of the order used in testing the autoregressive structure. In addition we introduce a new quite powerful multivariate order determination criterion.

Suggested Citation

  • Sergio G. Koreisha & Tarmo Pukkila, 1993. "Determining The Order Of A Vector Autoregression When The Number Of Component Series Is Large," Journal of Time Series Analysis, Wiley Blackwell, vol. 14(1), pages 47-69, January.
    • Handle: RePEc:bla:jtsera:v:14:y:1993:i:1:p:47-69
    DOI: 10.1111/j.1467-9892.1993.tb00129.x
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    Cited by:

    1. Matkovskyy, Roman, 2012. "The Index of the Financial Safety (IFS) of South Africa and Bayesian Estimates for IFS Vector-Autoregressive Model," MPRA Paper 42173, University Library of Munich, Germany.
    2. Baci, Sidika & Zaman, Asad, 1998. "Effects of skewness and kurtosis on model selection criteria," Economics Letters, Elsevier, vol. 59(1), pages 17-22, April.
    3. Jesús Gonzalo & Jean‐Yves Pitarakis, 2002. "Lag length estimation in large dimensional systems," Journal of Time Series Analysis, Wiley Blackwell, vol. 23(4), pages 401-423, July.
    4. Holger Bartel & Helmut Lutkepohl, 1998. "Estimating the Kronecker indices of cointegrated echelon-form VARMA models," Econometrics Journal, Royal Economic Society, vol. 1(Conferenc), pages 76-99.
    5. Matkovskyy, Roman, 2012. "Прогнозування розвитку економіки України на основі баєсівських авторегресійних (BVAR) моделей з різними priors [Forecasting Economic Development of Ukraine based on BVAR models with different prior," MPRA Paper 44725, University Library of Munich, Germany, revised Nov 2012.
    6. Judith A. Giles & Sadaf Mirza, 1999. "Some Pretesting Issues on Testing for Granger Noncausality," Econometrics Working Papers 9914, Department of Economics, University of Victoria.
    7. Alexander W. Hoffmaister & Carlos A. Végh, 1996. "Disinflation and The Recession-Now-versus-Recession-Later Hypothesis: Evidence from Uruguay," IMF Staff Papers, Palgrave Macmillan, vol. 43(2), pages 355-394, June.
    8. Koreisha, Sergio G. & Pukkila, Tarmo, 1998. "A two-step approach for identifying seasonal autoregressive time series forecasting models," International Journal of Forecasting, Elsevier, vol. 14(4), pages 483-496, December.
    9. Arie Preminger & Shinichi Sakata, 2007. "A model selection method for S-estimation," Econometrics Journal, Royal Economic Society, vol. 10(2), pages 294-319, July.

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