An efficient branch-and-bound strategy for subset vector autoregressive model selection
AbstractA 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Economic Dynamics and Control.
Volume (Year): 32 (2008)
Issue (Month): 6 (June)
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- Dietmar Maringer & Peter Winker, 2004. "Optimal Lag Structure Selection in VEC-Models," Computing in Economics and Finance 2004 155, Society for Computational Economics.
- 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.
- 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.
- Hirotugu Akaike, 1969. "Fitting autoregressive models for prediction," Annals of the Institute of Statistical Mathematics, Springer, vol. 21(1), pages 243-247, December.
- Cristian Gatu & Erricos Kontoghiorghes, 2005. "Efficient strategies for deriving the subset VAR models," Computational Management Science, Springer, vol. 4(4), pages 253-278, November.
- Winker, Peter, 1995. "Identification of multivariate AR-models by threshold accepting," Computational Statistics & Data Analysis, Elsevier, vol. 20(3), pages 295-307, September.
- Paolo Foschi & Erricos J. Kontoghiorghes, 2003.
"Estimation of VAR Models: Computational Aspects,"
Society for Computational Economics, vol. 21(1_2), pages 3-22, 02.
- 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.
- Kontoghiorghes, E. J. & Clarke, M. R. B., 1995. "An alternative approach for the numerical solution of seemingly unrelated regression equations models," Computational Statistics & Data Analysis, Elsevier, vol. 19(4), pages 369-377, April.
- Sachs, Andreas & Schleer, Frauke, 2013.
"Labour market performance in OECD countries: A comprehensive empirical modelling approach of institutional interdependencies,"
ZEW Discussion Papers
13-040, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
- Andreas Sachs & Frauke Schleer, 2013. "Labour market performance in OECD countries: A comprehensive empirical modelling approach of institutional interdependencies," WWWforEurope Working Papers series 7, WWWforEurope.
- Shafik, Nivien & Tutz, Gerhard, 2009. "Boosting nonlinear additive autoregressive time series," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2453-2464, May.
- Fossati, Sebastian, 2011.
"Covariate Unit Root Tests with Good Size and Power,"
2011-4, University of Alberta, Department of Economics.
- Fossati, Sebastian, 2012. "Covariate unit root tests with good size and power," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3070-3079.
- Ivan Savin & Peter Winker, 2012.
"Heuristic Optimization Methods for Dynamic Panel Data Model Selection: Application on the Russian Innovative Performance,"
Society for Computational Economics, vol. 39(4), pages 337-363, April.
- Ivan Savin & Peter Winker, 2010. "Heuristic Optimization Methods for Dynamic Panel Data Model Selection. Application on the Russian Innovative Performance," Working Papers 027, COMISEF.
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