UNSPECIFIED. (2001) Bootstrapping prediction intervals for autoregressive models. INTERNATIONAL JOURNAL OF FORECASTING, 17 (2). pp. 247-267. ISSN 0169-2070

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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
Date:	April 2001
Volume:	17
Number:	2
Number of Pages:	21
Page Range:	pp. 247-267
Publication Status:	Published
URI:	http://wrap.warwick.ac.uk/id/eprint/12144

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