Arbeitspapier
Optimal forecasts in the presence of discrete structural breaks under long memory
We develop methods to obtain optimal forecast under long memory in the presence of a discrete structural break based on different weighting schemes for the observations. We observe significant changes in the forecasts when long-range dependence is taken into account. Using Monte Carlo simulations, we confirm that our methods substantially improve the forecasting performance under long memory. We further present an empirical application to in inflation rates that emphasizes the importance of our methods.
- Sprache
-
Englisch
- Erschienen in
-
Series: Hannover Economic Papers (HEP) ; No. 705
- Klassifikation
-
Wirtschaft
Hypothesis Testing: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- Thema
-
long memory
forecasting
structural break
optimal weight
ARFIMA model
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Mboya, Mwasi Paza
Sibbertsen, Philipp
- Ereignis
-
Veröffentlichung
- (wer)
-
Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät
- (wo)
-
Hannover
- (wann)
-
2022
- 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
- Mboya, Mwasi Paza
- Sibbertsen, Philipp
- Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät
Entstanden
- 2022
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