Journal article

Publication year: 2008

Pages: 1949-1963

Journal: Journal of Economic Dynamics and Control

Volume number: 32

Issue number: 6

ISSN: 0165-1889

eISSN: 1879-1743

DOI Link: https://doi.org/10.1016/j.jedc.2007.08.001

Publisher: Elsevier

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.

Harvard Citation style: Gatu, C., Kontoghiorghes, E., Gilli, M. and Winker, P. (2008) An efficient branch-and-bound strategy for subset vector autoregressive model selection, Journal of Economic Dynamics and Control, 32(6), pp. 1949-1963. https://doi.org/10.1016/j.jedc.2007.08.001

APA Citation style: Gatu, C., Kontoghiorghes, E., Gilli, M., & Winker, P. (2008). An efficient branch-and-bound strategy for subset vector autoregressive model selection. Journal of Economic Dynamics and Control. 32(6), 1949-1963. https://doi.org/10.1016/j.jedc.2007.08.001