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ON THE SELECTION OF SUBSET AUTOREGRESSIVE TIME SERIES MODELS

ON THE SELECTION OF SUBSET AUTOREGRESSIVE TIME SERIES MODELS Abstract. The estimation of subset autoregressive time series models has been a difficult problem because of the large number of possible alternative models involved. However, with the advent of model selection criteria based on the maximum likelihood, subset model fitting has become feasible. Using an efficient technique for evaluating the residual variance of all possible subset models, a method is proposed for the fitting of subset autoregressive models. The application of the method is illustrated by means of real and simulated data. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Time Series Analysis Wiley

ON THE SELECTION OF SUBSET AUTOREGRESSIVE TIME SERIES MODELS

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References (16)

Publisher
Wiley
Copyright
Copyright © 1984 Wiley Subscription Services, Inc., A Wiley Company
ISSN
0143-9782
eISSN
1467-9892
DOI
10.1111/j.1467-9892.1984.tb00380.x
Publisher site
See Article on Publisher Site

Abstract

Abstract. The estimation of subset autoregressive time series models has been a difficult problem because of the large number of possible alternative models involved. However, with the advent of model selection criteria based on the maximum likelihood, subset model fitting has become feasible. Using an efficient technique for evaluating the residual variance of all possible subset models, a method is proposed for the fitting of subset autoregressive models. The application of the method is illustrated by means of real and simulated data.

Journal

Journal of Time Series AnalysisWiley

Published: Mar 1, 1984

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