Kernel smoothed prediction intervals for ARMA models

Arbeitspapier

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 distribution. Empirical methods are based on the sample forecast errors. In this paper we apply nonparametric quantile regression to the empirical forecast 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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: CoFE Discussion Paper ; No. 02/02

Classification and Topics

• Classification: Wirtschaft
• Subject: Forecasting ; Prediction intervals ; Non normal distributions ; Nonparametric estimation ; Quantile regression ; Theorie ; ARMA-Modell

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Abberger, Klaus
• Event: Veröffentlichung
• (who): University of Konstanz, Center of Finance and Econometrics (CoFE)
• (where): Konstanz
• (when): 2002

Further information

• Handle: http://hdl.handle.net/10419/85211
• URN: urn:nbn:de:bsz:352-opus-7817
• Last update: 2025-03-10T11:45:04+0100