Comparer des méthodes

	Modèle autorégressif non linéaire (NAR)	Modèle Vectoriel à Correction d'Erreur Non Linéaire (Nonlinear VECM)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1978-1990	1989–1998
Auteur d'origine	Tong, H. (threshold AR); Terasvirta, T. (STAR variant)	Granger & Lee (1989); Enders & Granger (1998)
Type	Nonlinear time series model	Nonlinear time-series model
Source fondatrice	Tong, H. (1990). Non-Linear Time Series: A Dynamical System Approach. Oxford University Press. ISBN: 9780198522201	Enders, W., & Granger, C. W. J. (1998). Unit-root tests and asymmetric adjustment with an example using the term structure of interest rates. Journal of Business & Economic Statistics, 16(3), 304–311.
Alias	NAR model, nonlinear autoregression, NLAR, threshold autoregressive model	nonlinear VECM, NVECM, threshold VECM, asymmetric VECM
Apparentées	6	2
Résumé	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.	The Nonlinear VECM extends the standard linear VECM by allowing the speed of adjustment toward long-run equilibrium to differ depending on the sign, magnitude, or regime of deviations from that equilibrium. It captures asymmetric or threshold-driven dynamics in cointegrated time-series systems that a standard VECM would miss.