Details
| Original language | English |
|---|---|
| Pages (from-to) | 1889-1908 |
| Number of pages | 20 |
| Journal | Journal of forecasting |
| Volume | 42 |
| Issue number | 7 |
| Early online date | 2 May 2023 |
| Publication status | Published - 1 Oct 2023 |
Abstract
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 inflation rates that emphasizes the importance of our methods.
Keywords
- ARFIMA model, forecasting, long memory, optimal weight, structural break
ASJC Scopus subject areas
- Mathematics(all)
- Modelling and Simulation
- Computer Science(all)
- Computer Science Applications
- Business, Management and Accounting(all)
- Strategy and Management
- Decision Sciences(all)
- Statistics, Probability and Uncertainty
- Decision Sciences(all)
- Management Science and Operations Research
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In: Journal of forecasting, Vol. 42, No. 7, 01.10.2023, p. 1889-1908.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Optimal forecasts in the presence of discrete structural breaks under long memory
AU - Paza Mboya, Mwasi
AU - Sibbertsen, Philipp
N1 - Funding Information: We are indepted to Simon Wingert, the participants of the conference of Deutsche Arbeitsgemeinschaft Statistik 2022 in Hamburg, an anonymous referee, and Siem Jan Koopman for helpful comments and discussion. The financial support of Deutsche Forschungsgemeinschaft is gratefully acknowledged. Open Access funding enabled and organized by Projekt DEAL.
PY - 2023/10/1
Y1 - 2023/10/1
N2 - 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 inflation rates that emphasizes the importance of our methods.
AB - 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 inflation rates that emphasizes the importance of our methods.
KW - ARFIMA model
KW - forecasting
KW - long memory
KW - optimal weight
KW - structural break
UR - http://www.scopus.com/inward/record.url?scp=85158063942&partnerID=8YFLogxK
U2 - 10.1002/for.2988
DO - 10.1002/for.2988
M3 - Article
AN - SCOPUS:85158063942
VL - 42
SP - 1889
EP - 1908
JO - Journal of forecasting
JF - Journal of forecasting
SN - 0277-6693
IS - 7
ER -