RECURSIVE ESTIMATION AND FORECASTING OF NONSTATIONARY TIME-SERIES

The paper presents a unified, fully recursive approach to the modelling and forecasting of non‐stationary time‐series. The basic time‐series model, which is based on the well‐known ‘component’ or ‘structuraL’ form, is formulated in state‐space terms. A novel spectral decomposition procedure, based on the exploitation of recursive smoothing algorithms, is then utilized to simplify the procedures of model identification and estimation. Finally, the fully recursive formulation allows for conventional or self‐adaptive implementation of state‐space forecasting and seasonal adjustment. Although the paper is restricted to the consideration of univariate time series, the basic approach can be extended to handle explanatory variables or full multivariable (vector) series. Copyright © 1990 John Wiley & Sons, Ltd.