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Forecasting long memory time series under a break in persistence

Autor: Florian Heinen, Philipp Sibbertsen and Robinson Kruse
Nummer: 433, Nov 2009, pp. 29
JEL-Class: C15, C22, C53

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.

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