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    Efficient strategies for deriving the subset VAR models

    Gatu, C. and Kontoghiorghes, Erricos (2005) Efficient strategies for deriving the subset VAR models. Computational Management Science 2 (4), pp. 253-278. ISSN 1619-697X.

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    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.

    Metadata

    Item Type: Article
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 11 May 2021 19:18
    Last Modified: 11 May 2021 19:18
    URI: https://eprints.bbk.ac.uk/id/eprint/44232

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