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Τίτλος: Efficient strategies for deriving the subset var models
Συγγραφείς: Gatu, Cristian 
Kontoghiorghes, Erricos John 
Major Field of Science: Natural Sciences
Λέξεις-κλειδιά: Least squares;Algorithms;Strategy
Ημερομηνία Έκδοσης: Νοε-2005
Πηγή: Computational Management Science, 2005, vol. 2, no. 4, pp. 253-278
Volume: 2
Issue: 4
Start page: 253
End page: 278
Περιοδικό: Computational Management Science 
Περίληψη: 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: https://hdl.handle.net/20.500.14279/2091
ISSN: 16196988
DOI: 10.1007/s10287-004-0021-x
Rights: © Springer Nature
Type: Article
Affiliation: Université de Neuchâtel 
Publication Type: Peer Reviewed
Εμφανίζεται στις συλλογές:Άρθρα/Articles

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