Forecasting Long Memory Time Series Under a Break in Persistence

CREATES Research Paper 2009-53

31 Pages Posted: 19 Nov 2009

Date Written: November 18, 2009

Abstract

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: Long memory time series, Break in persistence, Structural change, Simulation, Forecasting competition

JEL Classification: C15, C22, C53

Suggested Citation

Sibbertsen, Philipp and Kruse, Robinson, Forecasting Long Memory Time Series Under a Break in Persistence (November 18, 2009). CREATES Research Paper 2009-53. Available at SSRN: https://ssrn.com/abstract=1508404 or http://dx.doi.org/10.2139/ssrn.1508404

Philipp Sibbertsen (Contact Author)

University of Hannover ( email )

Welfengarten 1
D-30167 Hannover, 30167
Germany

Robinson Kruse

Aarhus University ( email )

Nordre Ringgade 1
DK-8000 Aarhus C, 8000
Denmark

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