Optimal Forecasts in the Presence of Discrete Structural Breaks under Long Memory

Mwasi Mboya and Philipp Sibbertsen

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

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 in inflation rates that emphasizes the importance of our methods.

Keywords: Long memory; Forecasting; Structural break; Optimal weight; ARFIMA model (search for similar items in EconPapers)
JEL-codes: C12 C22 (search for similar items in EconPapers)
Pages: 24 pages
Date: 2022-12
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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https://diskussionspapiere.wiwi.uni-hannover.de/pdf_bib/dp-705.pdf (application/pdf)

Related works:
Journal Article: Optimal forecasts in the presence of discrete structural breaks under long memory (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:han:dpaper:dp-705

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