Optimal forecasts in the presence of discrete structural breaks under long memory - Mwasi Paza Mboya; Philipp Sibbertsen (Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät, 2022)
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: C12; C22; 330; long memory; forecasting; structural break; optimal weight; ARFIMA model