# Guaranteed-Content Prediction Intervals for Non-linear Autoregressions

*Xavier de Luna*

*Journal of Forecasting*, 2001, vol. 20, issue 4, pages 265-72

**Abstract:**
In this paper we present guaranteed-content prediction intervals for time series data. These intervals are such that their content (or coverage) is guaranteed with a given high probability. They are thus more relevant for the observed time series at hand than classical prediction intervals, whose content is guaranteed merely on average over hypothetical repetitions of the prediction process. This type of prediction inference has, however, been ignored in the time series context because of a lack of results. This gap is filled by deriving asymptotic results for a general family of autoregressive models, thereby extending existing results in non-linear regression. The actual construction of guaranteed-content prediction intervals directly follows from this theory. Simulated and real data are used to illustrate the practical difference between classical and guaranteed-content prediction intervals for ARCH models. Copyright © 2001 by John Wiley & Sons, Ltd.

**Date:** 2001

**References:** Add references at CitEc

**Citations** Track citations by RSS feed

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

**Related works:**

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:** http://EconPapers.repec.org/RePEc:jof:jforec:v:20:y:2001:i:4:p:265-72

Access Statistics for this article

Journal of Forecasting is edited by *Derek W. Bunn*

More articles in Journal of Forecasting from John Wiley & Sons, Ltd.

Series data maintained by Wiley-Blackwell Digital Licensing ().