Comparer des méthodes

	Moindres carrés ordinaires non linéaires (MCO non linéaires)	Moindres Carrés Généralisés Non Linéaires (MCGNL)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1974–1987	1975
Auteur d'origine	Gallant (1987); Wooldridge (2010) for econometric treatment	Gallant (1975); extended by Davidson & MacKinnon
Type	Nonlinear regression estimator	Nonlinear estimator
Source fondatrice	Gallant, A. R. (1987). Nonlinear Statistical Models. John Wiley & Sons. ISBN: 978-0471802600	Gallant, A. R. (1987). Nonlinear Statistical Models. Wiley. ISBN: 978-0471802600
Alias	nonlinear least squares, NLS, NLLS, nonlinear regression	NGLS, nonlinear generalized least squares, feasible nonlinear GLS, FNGLS
Apparentées	5	2
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.	Nonlinear Generalized Least Squares extends the classical GLS framework to regression models where the mean function is nonlinear in the parameters. It accounts for non-spherical errors — heteroscedasticity or autocorrelation — by pre-weighting the nonlinear objective with an estimated error covariance matrix, yielding consistent and asymptotically efficient estimates.