Nonlinear ARIMA model
The Nonlinear ARIMA model extends the classical Box-Jenkins ARIMA framework by allowing the conditional mean of a time series to depend on past values and past errors through a nonlinear function. It encompasses families such as Threshold AR (TAR/SETAR), Smooth Transition AR (STAR/LSTAR/ESTAR), and Markov-switching models, capturing asymmetric dynamics, regime changes, and business-cycle asymmetries that linear ARIMA cannot represent.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
- Tong, H. (1990). Non-Linear Time Series: A Dynamical System Approach. Oxford University Press. · ISBN 9780198522249
- Terasvirta, T. (1994). Specification, estimation, and evaluation of smooth transition autoregressive models. Journal of the American Statistical Association, 89(425), 208-218. · URL
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