The familiar Box and Jenkins method used to build prediction intervals for AR processes neglects the variability due to the estimation of model order and parameters. The purpose of the present paper is to assess the robustness of an approach that takes into account this additional uncertainty when the assumption that the underlying process is AR is not satisfied.
Bootstrap prediction intervals for autoregressive models fitted to non-autoregressive processes
GRIGOLETTO, MATTEO
2001
Abstract
The familiar Box and Jenkins method used to build prediction intervals for AR processes neglects the variability due to the estimation of model order and parameters. The purpose of the present paper is to assess the robustness of an approach that takes into account this additional uncertainty when the assumption that the underlying process is AR is not satisfied.
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