Forecasting long memory time series under a break in persistence

Florian Heinen, Philipp Sibbertsen and Robinson Kruse

Hannover Economic Papers (HEP) from Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät

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.

Keywords: Long memory time series; Break in persistence; Structural change; Simulation; Forecasting competition (search for similar items in EconPapers)
JEL-codes: C15 C22 C53 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2009-11
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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http://diskussionspapiere.wiwi.uni-hannover.de/pdf_bib/dp-433.pdf (application/pdf)

Related works:
Working Paper: Forecasting long memory time series under a break in persistence (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:han:dpaper:dp-433

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