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Effect of cross correlations in error terms on the model selection criteria for the stationary VAR process

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  • Nezir Kose
  • Nuri Ucar

Abstract

In this paper we investigate the finite sample properties of several model selection criteria in case of bivariate Vector AutoRegressions (VARs) of order one and two by a simulation study, particularly focusing on the effects of the degree of cross correlation in the error terms in combination with the values of more or less extreme values of the characteristic roots of the VAR-process. The Monte Carlo experiments show that the degree of cross correlation has an influence on the model selection criteria.

Suggested Citation

  • Nezir Kose & Nuri Ucar, 2006. "Effect of cross correlations in error terms on the model selection criteria for the stationary VAR process," Applied Economics Letters, Taylor & Francis Journals, vol. 13(4), pages 223-228.
    • Handle: RePEc:taf:apeclt:v:13:y:2006:i:4:p:223-228
    DOI: 10.1080/13504850500392974
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    References listed on IDEAS

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    1. Clive Granger & Yongil Jeon, 2004. "Forecasting Performance of Information Criteria with Many Macro Series," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(10), pages 1227-1240.
    2. Hafer, R. W. & Sheehan, Richard G., 1989. "The sensitivity of VAR forecasts to alternative lag structures," International Journal of Forecasting, Elsevier, vol. 5(3), pages 399-408.
    3. Braun, Phillip A. & Mittnik, Stefan, 1993. "Misspecifications in vector autoregressions and their effects on impulse responses and variance decompositions," Journal of Econometrics, Elsevier, vol. 59(3), pages 319-341, October.
    4. Baci, Sidika & Zaman, Asad, 1998. "Effects of skewness and kurtosis on model selection criteria," Economics Letters, Elsevier, vol. 59(1), pages 17-22, April.
    5. Kul Luintel, 1999. "Non-causality due to irrelevant lag polynomials," Applied Economics Letters, Taylor & Francis Journals, vol. 6(1), pages 17-20.
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