Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/37129 
Year of Publication: 
2009
Series/Report no.: 
Diskussionsbeitrag No. 433
Publisher: 
Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät, Hannover
Abstract: 
We consider the problem of forecasting time series with long memory when the memory parameter is subject to a structural break. By means of a large-scale Monte Carlo study we show that ignoring such a change in persistence leads to substantially reduced forecasting precision. The strength of this effect depends on whether the memory parameter is increasing or decreasing over time. A comparison of six forecasting strategies allows us to conclude that pre-testing for a change in persistence is highly recommendable in our setting. In addition we provide an empirical example which underlines the importance of our findings.
JEL: 
C15
C22
C53
Document Type: 
Working Paper

Files in This Item:
File
Size
130.78 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.