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https://hdl.handle.net/20.500.14279/2091
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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