Kernel smoothed prediction intervals for ARMA models

Abberger, Klaus

Kernel smoothed prediction intervals for ARMA models

Files

dp0202.pdf(222.66 KB)

Date

2002

Authors

Abberger, Klaus

Series

CoFE-Diskussionspapiere / Zentrum für Finanzen und Ökonometrie; 2002/02

Publication type

Working Paper/Technical Report

Abstract

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.

Subject (DDC)

510 Mathematics

Keywords

Forecasting,Prediction intervals,Non normal distributions,Nonparametric estimation,Quanrile regression

Cite This

ISO 690

ABBERGER, 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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