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- W2053840033 abstract "Best subset AR models involve computationally intensive exhaustive search techniques. The aim of this paper is to avoid such computationally intensive exhaustive search procedures for obtaining best subset AR models. A methodology was proposed (Sarkar and Kanjilal, 1995) using singular value decomposition (SVD) and orthonormal with column pivoting factorisation (QRcp) for extracting a reduced subset from an exhaustive candidate set of regressors. Here the spurious regressor variables from this set are identified and eliminated by a test of significance based on the asymptotic normality of the maximum likelihood estimates (m.l.e) of themodel parameters and a model selection information criterion. This method obtains subset AR models directly and efficiently. Fitting procedure is demonstrated with a simulated series and some important real life time series. Some subset models have been found to generate better forecasts than their respective full models." @default.
- W2053840033 created "2016-06-24" @default.
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- W2053840033 date "1997-02-01" @default.
- W2053840033 modified "2023-09-24" @default.
- W2053840033 title "An approach to direct selection of best subset ar model" @default.
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- W2053840033 doi "https://doi.org/10.1080/00949659708811793" @default.
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