Bounds for the Least Squares Extrapolation in Non-linear AR(1) Processes

Jiri Andel

Journal of Forecasting, 2001, vol. 20, issue 1, 79-86

Abstract: It is proved that formula for least squares extrapolation in stationary non-linear AR(1) process is valid also for non-stationary non-linear AR(1) processes. This formula depends on the distribution of the corresponding white noise. If the non-linear function used in the model is non-decreasing and concave, upper and lower bounds are derived for least squares extrapolation such that the bounds depend only on the expectation of the white noise. It is shown in an example that the derived bounds in some cases give a good approximation to the least squares extrapolation. Copyright © 2001 by John Wiley & Sons, Ltd.

Date: 2001
References: Add references at CitEc
Citations:

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: https://EconPapers.repec.org/RePEc:jof:jforec:v:20:y:2001:i:1:p:79-86

Access Statistics for this article

Journal of Forecasting is currently edited by Derek W. Bunn

More articles in Journal of Forecasting from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley-Blackwell Digital Licensing () and Christopher F. Baum ().