Comparer des méthodes

	Modèle de Moyenne Mobile Non Linéaire (NMA)	Modèle autorégressif non linéaire (NAR)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1978	1978-1990
Auteur d'origine	Granger & Andersen (bilinear/NMA framework); Tong (nonlinear time series theory)	Tong, H. (threshold AR); Terasvirta, T. (STAR variant)
Type	Nonlinear time series model	Nonlinear time series model
Source fondatrice	Granger, C. W. J., & Andersen, A. P. (1978). An Introduction to Bilinear Time Series Models. Vandenhoeck and Ruprecht, Gottingen.	Tong, H. (1990). Non-Linear Time Series: A Dynamical System Approach. Oxford University Press. ISBN: 9780198522201
Alias	NMA model, nonlinear moving average, NLMA model, nonlinear MA	NAR model, nonlinear autoregression, NLAR, threshold autoregressive model
Apparentées	4	6
Résumé	The Nonlinear Moving Average (NMA) model extends the classical linear MA model by allowing the current observation to depend on past innovations through a nonlinear function rather than a simple weighted sum. It is used in time series analysis when error shocks transmit to outcomes in an asymmetric or state-dependent fashion.	The Nonlinear AR model extends the classical autoregressive framework by allowing the mapping from past values to the current value to follow an arbitrary or regime-switching nonlinear function. Major families include the Self-Exciting Threshold AR (SETAR), Smooth Transition AR (STAR), and neural network AR, each capturing different forms of asymmetry, regime shifts, or smooth nonlinear dynamics in univariate time series.