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Bootstrapping prediction intervals for autoregressive models

Clements, M. and Taylor, N. (2001) Bootstrapping prediction intervals for autoregressive models. International Journal of Forecasting., 17 (2). pp. 247-267. ISSN 0169-2070

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To link to this item DOI: 10.1016/S0169-2070(00)00079-0


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.

Item Type:Article
Divisions:Henley Business School > ICMA Centre
ID Code:35198
Uncontrolled Keywords:Prediction intervals; Bootstrapping; Bias-correction

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