Arbeitspapier
Forecasting long memory time series under a break in persistence
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Diskussionsbeitrag ; No. 433
- Klassifikation
-
Wirtschaft
Statistical Simulation Methods: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Forecasting Models; Simulation Methods
- Thema
-
Zeitreihenanalyse
Strukturbruch
Simulation
Prognoseverfahren
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Heinen, Florian
Sibbertsen, Philipp
Kruse, Robinson
- Ereignis
-
Veröffentlichung
- (wer)
-
Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät
- (wo)
-
Hannover
- (wann)
-
2009
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Heinen, Florian
- Sibbertsen, Philipp
- Kruse, Robinson
- Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät
Entstanden
- 2009
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