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Efficient strategies for deriving the subset VAR models


  • Cristian Gatu


  • Erricos Kontoghiorghes


Algorithms for computing the subset Vector Autoregressive (VAR) models are proposed. These algorithms can be used to choose a subset of the most statistically-significant variables of a VAR model. In such cases, the selection criteria are based on the residual sum of squares or the estimated residual covariance matrix. The VAR model with zero coefficient restrictions is formulated as a Seemingly Unrelated Regressions (SUR) model. Furthermore, the SUR model is transformed into one of smaller size, where the exogenous matrices comprise columns of a triangular matrix. Efficient algorithms which exploit the common columns of the exogenous matrices, sparse structure of the variance-covariance of the disturbances and special properties of the SUR models are investigated. The main computational tool of the selection strategies is the generalized QR decomposition and its modification. Copyright Springer-Verlag Berlin/Heidelberg 2005

Suggested Citation

  • Cristian Gatu & Erricos Kontoghiorghes, 2005. "Efficient strategies for deriving the subset VAR models," Computational Management Science, Springer, vol. 4(4), pages 253-278, November.
  • Handle: RePEc:spr:comgts:v:4:y:2005:i:4:p:253-278
    DOI: 10.1007/s10287-004-0021-x

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    Cited by:

    1. Hofmann, Marc & Gatu, Cristian & Kontoghiorghes, Erricos John, 2007. "Efficient algorithms for computing the best subset regression models for large-scale problems," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 16-29, September.
    2. Gatu, Cristian & Kontoghiorghes, Erricos J., 2006. "Estimating all possible SUR models with permuted exogenous data matrices derived from a VAR process," Journal of Economic Dynamics and Control, Elsevier, vol. 30(5), pages 721-739, May.
    3. Gatu, Cristian & Yanev, Petko I. & Kontoghiorghes, Erricos J., 2007. "A graph approach to generate all possible regression submodels," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 799-815, October.
    4. Pacheco, Joaquin & Casado, Silvia & Nunez, Laura & Gomez, Olga, 2006. "Analysis of new variable selection methods for discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1463-1478, December.
    5. Pacheco, Joaquín & Casado, Silvia & Núñez, Laura, 2009. "A variable selection method based on Tabu search for logistic regression models," European Journal of Operational Research, Elsevier, vol. 199(2), pages 506-511, December.
    6. Gatu, Cristian & Kontoghiorghes, Erricos J. & Gilli, Manfred & Winker, Peter, 2008. "An efficient branch-and-bound strategy for subset vector autoregressive model selection," Journal of Economic Dynamics and Control, Elsevier, vol. 32(6), pages 1949-1963, June.


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