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Bootstrapping prediction intervals for autoregressive models
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UNSPECIFIED (2001) Bootstrapping prediction intervals for autoregressive models. INTERNATIONAL JOURNAL OF FORECASTING, 17 (2). pp. 247-267.
Full text not available from this repository, contact author.Abstract
Methods of improving the coverage of Box-Jenkins prediction intervals for linear autoregressive models are explored. These methods use bootstrap techniques to allow for parameter estimation uncertainty and to reduce the small-sample bias in the estimator of the models' parameters. In addition, we also consider a method of bias-correcting the non-linear functions of the parameter estimates that are used to generate conditional multi-step predictions. (C) 2001 International Institute of Forecasters. Published by Elsevier Science B.V. All rights reserved.
| Item Type: | Journal Article | ||||
|---|---|---|---|---|---|
| Subjects: | H Social Sciences > HC Economic History and Conditions H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management |
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| Journal or Publication Title: | INTERNATIONAL JOURNAL OF FORECASTING | ||||
| Publisher: | ELSEVIER SCIENCE BV | ||||
| ISSN: | 0169-2070 | ||||
| Official Date: | April 2001 | ||||
| Dates: |
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| Volume: | 17 | ||||
| Number: | 2 | ||||
| Number of Pages: | 21 | ||||
| Page Range: | pp. 247-267 | ||||
| Publication Status: | Published |
Data sourced from Thomson Reuters' Web of Knowledge
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