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An efficient branch-and-bound strategy for subset vector autoregressive model selection

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  • Gatu, Cristian
  • Kontoghiorghes, Erricos J.
  • Gilli, Manfred
  • Winker, Peter

Abstract

A computationally efficient branch-and-bound strategy for finding the subsets of the most statistically significant variables of a vector autoregressive (VAR) model from a given search subspace is proposed. Specifically, the candidate submodels are obtained by deleting columns from the coefficient matrices of the full-specified VAR process. The strategy is based on a regression tree and derives the best-subset VAR models without computing the whole tree. The branch-and-bound cutting test is based on monotone statistical selection criteria which are functions of the determinant of the estimated residual covariance matrix. Experimental results confirm the computational efficiency of the proposed algorithm.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:dyncon:v:32:y:2008:i:6:p:1949-1963
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    1. Kontoghiorghes, Erricos J & Dinenis, Elias, 1997. "Computing 3SLS Solutions of Simultaneous Equation Models with a Possible Singular Variance-Covariance Matrix," Computational Economics, Springer;Society for Computational Economics, vol. 10(3), pages 231-250, August.
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    3. Cristian Gatu & Erricos Kontoghiorghes, 2005. "Efficient strategies for deriving the subset VAR models," Computational Management Science, Springer, vol. 4(4), pages 253-278, November.
    4. Cristian Gatu & Erricos Kontoghiorghes, 2002. "A branch and bound algorithm for computing the best subset regression models," Computing in Economics and Finance 2002 294, Society for Computational Economics.
    5. 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.
    6. Paolo Foschi & Erricos J. Kontoghiorghes, 2003. "Estimation of VAR Models: Computational Aspects," Computational Economics, Springer;Society for Computational Economics, vol. 21(1_2), pages 3-22, February.
    7. 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.
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    11. Peter Winker & Dietmar Maringer, 2004. "Optimal Lag Structure Selection in VEC-Models," Contributions to Economic Analysis, in: New Directions in Macromodelling, pages 213-234, Emerald Group Publishing Limited.
    12. 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.
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    3. Wagner Martin & Hlouskova Jaroslava, 2015. "Growth Regressions, Principal Components Augmented Regressions and Frequentist Model Averaging," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 235(6), pages 642-662, December.
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    6. Ivan Savin & Peter Winker, 2012. "Heuristic Optimization Methods for Dynamic Panel Data Model Selection: Application on the Russian Innovative Performance," Computational Economics, Springer;Society for Computational Economics, vol. 39(4), pages 337-363, April.
    7. Andreas Sachs & Frauke Schleer, 2013. "Labour Market Performance in OECD Countries: A Comprehensive Empirical Modelling Approach of Institutional Interdependencies. WWWforEurope Working Paper No. 7," WIFO Studies, WIFO, number 46851, April.

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