Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/6728
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

SCOPUSTM   
Citations 20

9
checked on Dec 17, 2017

Page view(s)

23
Last Week
1
Last month
1
checked on Dec 18, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.