Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/6773
Title: Efficient strategies for deriving the subset var models
Authors: Gatu, Cristian 
Kontoghiorghes, Erricos John 
Keywords: Least squares
Algorithms
Strategy
Issue Date: 2005
Publisher: Springer Link
Source: Computational Management Science, 2005, Volume 2, Issue 4, Pages 253-278
Abstract: Algorithms for computing the subset Vector Autoregressive (VAR) models are proposed. These algorithms can be used to choose a subset of the most statistically-significant variables of a VAR model. In such cases, the selection criteria are based on the residual sum of squares or the estimated residual covariance matrix. The VAR model with zero coefficient restrictions is formulated as a Seemingly Unrelated Regressions (SUR) model. Furthermore, the SUR model is transformed into one of smaller size, where the exogenous matrices comprise columns of a triangular matrix. Efficient algorithms which exploit the common columns of the exogenous matrices, sparse structure of the variance-covariance of the disturbances and special properties of the SUR models are investigated. The main computational tool of the selection strategies is the generalized QR decomposition and its modification
URI: http://ktisis.cut.ac.cy/handle/10488/6773
ISSN: 1619-697X (print)
1619-6988 (online)
DOI: 10.1007/s10287-004-0021-x
Rights: © Springer-Verlag Berlin/Heidelberg 2005.
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