Peter C. Young is Director of the Centre for Research on Environmental Systems at the University of Lancaster. From 1975 to 1981 he was Professional Fellow at the Australian National University and, between 1970 and 1975, he was University Lecturer in Control and Systems, and Fellow of Clare Hall, University of Cambridge. His research interests include the recursive estimation, forecasting and control of non-stationary dynamic systems, with applications in areas ranging from ecology through engineering to economics.
Article first published online: 27 DEC 2006
Copyright © 1989 John Wiley & Sons, Ltd.
Journal of Forecasting
Volume 8, Issue 4, pages 399–416, October/December 1989
How to Cite
Young, P. and Ng, C. (1989), Variance intervention. J. Forecast., 8: 399–416. doi: 10.1002/for.3980080405
- Issue published online: 27 DEC 2006
- Article first published online: 27 DEC 2006
- Manuscript Accepted: APR 1989
- Manuscript Revised: AUG 1988
- Manuscript Received: MAY 1988
- State-space methods;
- Recursive estimation;
- Fixed interval smoothing;
- Structural models;
- Non-stationary time-series;
- Intervention methods;
- Spectral properties;
- Lag-free filtering;
- Adaptive seasonal adjustment
Variance intervention is a simple state-space approach to handling sharp discontinuities of level or slope in the states or parameters of models for non-stationary time-series. It derives from earlier procedures used in the 1960s for the design of self-adaptive, state variable feedback control systems. In the alternative state-space forecasting context considered in the present paper, it is particularly useful when applied to structural time series models. The paper compares the variance intervention procedure with the related ‘subjective intervention’ approach proposed by West and Harrison in a recent issue of the Journal of Forecasting, and demonstrates it efficacy by application to various time-series data, including those used by West and Harrison.