Forecasting long memory time series under a break in persistence - Florian Heinen; Philipp Sibbertsen; Robinson Kruse (Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät, 2009)
We consider the problem of forecasting time series with long memory when the memory parameter is subject to a structural break. By means of a large-scale Monte Carlo study we show that ignoring such a change in persistence leads to substantially reduced forecasting precision. The strength of this effect depends on whether the memory parameter is increasing or decreasing over time. A comparison of six forecasting strategies allows us to conclude that pre-testing for a change in persistence is highly recommendable in our setting. In addition we provide an empirical example which underlines the importance of our findings.
Keywords: C15; C22; C53; 330; Zeitreihenanalyse; Strukturbruch; Simulation; Prognoseverfahren