Kernel smoothed prediction intervals for ARMA models

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2002
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Abberger, Klaus
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Zusammenfassung

The procedures of estimating prediction intervals for ARMA processes can be divided into model based methods and empirical methods. Model based methods require knowledge of the model and the underlying innovation dis- tribution. Empirical methods are based on the sample forecast errors. In this paper we apply nonparametric quantile regression to the empirical fore- cast errors using lead time as regressor. With this method there is no need for a distribution assumption. But for the data pattern in this case a double kernel method which allows smoothing in two directions is required. An estimation algorithm is presented and applied to some simulation examples.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
510 Mathematik
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Forecasting, Prediction intervals, Non normal distributions, Nonparametric estimation, Quanrile regression
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ISO 690ABBERGER, Klaus, 2002. Kernel smoothed prediction intervals for ARMA models
BibTex
@techreport{Abberger2002Kerne-537,
  year={2002},
  series={CoFE-Diskussionspapiere / Zentrum für Finanzen und Ökonometrie},
  title={Kernel smoothed prediction intervals for ARMA models},
  number={2002/02},
  author={Abberger, Klaus}
}
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