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.
- Language
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Englisch
- Bibliographic citation
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Series: Hannover Economic Papers (HEP) ; No. 705
- Classification
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Wirtschaft
Hypothesis Testing: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- Subject
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long memory
forecasting
structural break
optimal weight
ARFIMA model
- Event
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Geistige Schöpfung
- (who)
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Mboya, Mwasi Paza
Sibbertsen, Philipp
- Event
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Veröffentlichung
- (who)
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Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät
- (where)
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Hannover
- (when)
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2022
- Handle
- Last update
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10.03.2025, 11:43 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Mboya, Mwasi Paza
- Sibbertsen, Philipp
- Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät
Time of origin
- 2022
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