Bootstrapping prediction intervals for autoregressive models

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.