Comparer des méthodes

	Moindres carrés ordinaires non linéaires (MCO non linéaires)	Modèle ARDL non linéaire (NARDL)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1974–1987	2014
Auteur d'origine	Gallant (1987); Wooldridge (2010) for econometric treatment	Shin, Yu & Greenwood-Nimmo
Type	Nonlinear regression estimator	Nonlinear cointegration model
Source fondatrice	Gallant, A. R. (1987). Nonlinear Statistical Models. John Wiley & Sons. ISBN: 978-0471802600	Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In R. C. Sickles & W. C. Horrace (Eds.), Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications (pp. 281–314). Springer.
Alias	nonlinear least squares, NLS, NLLS, nonlinear regression	NARDL, nonlinear bounds test, asymmetric ARDL, asymmetric cointegration model
Apparentées	5	5
Résumé	Nonlinear Ordinary Least Squares (NLS) estimates regression models in which the conditional mean function is nonlinear in the parameters. Like standard OLS it minimises the sum of squared residuals, but because no closed-form solution exists the estimator is found by iterative numerical optimisation. Under standard regularity conditions NLS is consistent and asymptotically normal.	The Nonlinear ARDL (NARDL) model extends the linear ARDL bounds-testing framework to allow asymmetric long-run and short-run relationships. By decomposing the regressor into cumulative positive and negative partial sums, it tests whether increases and decreases in a variable exert different effects on the outcome — a feature especially relevant in financial and energy economics where positive and negative shocks rarely cancel out symmetrically.