• 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.