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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.

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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
Journal or Publication Title: INTERNATIONAL JOURNAL OF FORECASTING
Publisher: ELSEVIER SCIENCE BV
ISSN: 0169-2070
Official Date: April 2001
Dates:
DateEvent
April 2001UNSPECIFIED
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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