Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/283156 
Year of Publication: 
2022
Series/Report no.: 
Hannover Economic Papers (HEP) No. 705
Publisher: 
Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät, Hannover
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.
Subjects: 
long memory
forecasting
structural break
optimal weight
ARFIMA model
JEL: 
C12
C22
Document Type: 
Working Paper

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