Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/1978
Title: An efficient branch-and-bound strategy for subset vector autoregressive model selection
Authors: Gatu, Cristian 
Gilli, Manfred H. 
Kontoghiorghes, Erricos John 
Major Field of Science: Social Sciences
Field Category: Economics and Business
Keywords: Branch and bound algorithms;Algorithms
Issue Date: 2008
Source: Journal of Economic Dynamics and Control, 2008, vol. 32, iss. 6, pp. 1949-1963
Volume: 32
Issue: 6
Start page: 1949
End page: 1963
Journal: Journal of Economic Dynamics and Control 
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: https://hdl.handle.net/20.500.14279/1978
ISSN: 0165-1889
DOI: 10.1016/j.jedc.2007.08.001
Rights: © Elsevier
Type: Article
Affiliation: Cyprus University of Technology 
Affiliation : VTT Technical Research Centre of Finland 
University of Cyprus 
University of Geneva 
Alexandru Ioan Cuza University of Iaşi 
Birkbeck University of London 
Swiss Finance Institute 
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