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• W2066734404 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." @default.
• W2066734404 created "2016-06-24" @default.
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• W2066734404 date "2005-11-01" @default.
• W2066734404 modified "2023-09-25" @default.
• W2066734404 title "Efficient strategies for deriving the subset VAR models" @default.
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• W2066734404 doi "https://doi.org/10.1007/s10287-004-0021-x" @default.
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