Please use this identifier to cite or link to this item:
|Title:||An efficient branch-and-bound strategy for subset vector autoregressive model selection||Authors:||Gatu, Cristian
Gilli, Manfred H.
Kontoghiorghes, Erricos John
|Keywords:||Branch and bound algorithms;Algorithms||Issue Date:||2008||Publisher:||Elsevier||Source:||Journal of Economic Dynamics and Control, 2008, Volume 32, Issue 6, Pages 1949-1963||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.||URI:||http://ktisis.cut.ac.cy/handle/10488/6728||ISSN:||01651889||DOI:||http://dx.doi.org/10.1016/j.jedc.2007.08.001||Rights:||© 2007 Elsevier B.V. All rights reserved.||Type:||Article|
|Appears in Collections:||Άρθρα/Articles|
Show full item record
checked on Dec 17, 2017
checked on Dec 18, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.