Kernel smoothed prediction intervals for ARMA models

Klaus Abberger

No 02/02, CoFE Discussion Papers from University of Konstanz, Center of Finance and Econometrics (CoFE)

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 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.

Keywords: Forecasting; Prediction intervals; Non normal distributions; Nonparametric estimation; Quantile regression (search for similar items in EconPapers)
Date: 2002
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/85211/1/dp02-02.pdf (application/pdf)

Related works:
Journal Article: Kernel smoothed prediction intervals for ARMA models (2006)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:cofedp:0202

Access Statistics for this paper

More papers in CoFE Discussion Papers from University of Konstanz, Center of Finance and Econometrics (CoFE) Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().