Forecasting long memory time series under a break in persistence

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Diskussionsbeitrag ; No. 433

Classification and Topics

• Classification: Wirtschaft ; Statistical Simulation Methods: General ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes ; Forecasting Models; Simulation Methods
• Subject: Zeitreihenanalyse ; Strukturbruch ; Simulation ; Prognoseverfahren

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Heinen, Florian ; Sibbertsen, Philipp ; Kruse, Robinson
• Event: Veröffentlichung
• (who): Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät
• (where): Hannover
• (when): 2009

Further information

• Handle: http://hdl.handle.net/10419/37129
• Last update: 10.03.2025, 11:43 AM CET