Recursive estimation and forecasting of non‐stationary time series
- 1 March 1990
- journal article
- research article
- Published by Wiley in Journal of Forecasting
- Vol. 9 (2) , 173-204
- https://doi.org/10.1002/for.3980090208
Abstract
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.Keywords
This publication has 30 references indexed in Scilit:
- Variance interventionJournal of Forecasting, 1989
- A unified view of statistical forecasting proceduresJournal of Forecasting, 1984
- A Smoothness Priors-State Space Modeling of Time Series with Trend and SeasonalityJournal of the American Statistical Association, 1984
- A NONSTATIONARY TIME SERIES MODEL AND ITS FITTING BY A RECURSIVE FILTERJournal of Time Series Analysis, 1981
- Joint parameter/state estimationElectronics Letters, 1979
- Self-adaptive Kalman filterElectronics Letters, 1979
- Intervention Analysis with Applications to Economic and Environmental ProblemsJournal of the American Statistical Association, 1975
- A new look at the statistical model identificationIEEE Transactions on Automatic Control, 1974
- New Results in Linear Filtering and Prediction TheoryJournal of Basic Engineering, 1961
- TESTS OF FIT IN TIME SERIESBiometrika, 1952