Forecasting economic time series with unconditional time-varying variance

• Published in: International journal of forecasting Vol. 20; no. 4; pp. 611 - 627
• Main Authors: Van Bellegem, Sébastien, von Sachs, Rainer
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01-10-2004; Elsevier; Elsevier Sequoia S.A
• Series: International Journal of Forecasting
• Subjects: Covariance nonstationarity; Economic models; Forecasting; Nonparametric estimation; Rescaled time; Studies; Time series; Time-modulated process; USA; Rescaled time; Nonparametric estimation; Time-modulated process; Forecasting; Covariance nonstationarity
• ISSN: 0169-2070; 1872-8200
• DOI: 10.1016/j.ijforecast.2003.10.002

The classical forecasting theory of stationary time series exploits the second-order structure (variance, autocovariance, and spectral density) of an observed process in order to construct some prediction intervals. However, some economic time series show a time-varying
unconditional second-order structure. This article focuses on a simple and meaningful model allowing this nonstationary behaviour. We show that this model satisfactorily explains the nonstationary behaviour of several economic data sets, among which are the U.S. stock returns and exchange rates. The question of how to forecast these processes is addressed and evaluated on the data sets.