FORECASTING EXPONENTIAL AUTOREGRESSIVE MODELS OF ORDER 1
- 1 March 1989
- journal article
- Published by Wiley in Journal of Time Series Analysis
- Vol. 10 (2) , 95-113
- https://doi.org/10.1111/j.1467-9892.1989.tb00018.x
Abstract
Exact forecasting of the non‐linear EXPAR(1) model for several steps ahead involves a sequence of numerical integrations, thus motivating the search for reasonable approximations. A method based on the assumption of approximately normal forecast errors is shown to give forecasts which perform well in both qualitative and numerical comparisons with two alternative approximations based on naive extrapolation and linearization of the autoregression function.Keywords
This publication has 11 references indexed in Scilit:
- On Multi-Step Non-Linear Least Squares PredictionJournal of the Royal Statistical Society: Series D (The Statistician), 1988
- EXACT LEAST SQUARES MULTI‐STEP PREDICTION FROM NONLINEAR AUTOREGRESSIVE MODELSJournal of Time Series Analysis, 1987
- The importance of non‐linearities in large forecasting models with stochastic error processesJournal of Forecasting, 1986
- ON ESTIMATING THRESHOLDS IN AUTOREGRESSIVE MODELSJournal of Time Series Analysis, 1986
- THE STATISTICAL ANALYSIS OF PERTURBED LIMIT CYCLE PROCESSES USING NONLINEAR TIME SERIES MODELSJournal of Time Series Analysis, 1982
- Modelling nonlinear random vibrations using an amplitude-dependent autoregressive time series modelBiometrika, 1981
- A NOTE ON THE DISTRIBUTIONS OF NON‐LINEAR AUTOREGRESSIVE STOCHASTIC MODELSJournal of Time Series Analysis, 1981
- Non-linear time series models for non-linear random vibrationsJournal of Applied Probability, 1980
- Nonlinear autoregressive processesProceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, 1978
- Non-Linear Time Series Model Identification by Akaike's Information CriterionIFAC Proceedings Volumes, 1977