Comparer des méthodes

	Moindres Carrés Généralisés Non Linéaires (MCGNL)	Régression apparemment non liée (SUR)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1975	1962
Auteur d'origine	Gallant (1975); extended by Davidson & MacKinnon	Arnold Zellner
Type	Nonlinear estimator	System regression (multi-equation)
Source fondatrice	Gallant, A. R. (1987). Nonlinear Statistical Models. Wiley. ISBN: 978-0471802600	Zellner, A. (1962). An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias. Journal of the American Statistical Association, 57(298), 348-368.
Alias	NGLS, nonlinear generalized least squares, feasible nonlinear GLS, FNGLS	SUR, Zellner's SUR, seemingly unrelated regression equations, Görünürde İlişkisiz Regresyon (SUR)
Apparentées	2	5
Résumé	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.	Seemingly Unrelated Regressions, introduced by Arnold Zellner in 1962, is a system regression method that estimates several linear equations jointly when their error terms are correlated across equations. By exploiting that cross-equation correlation through generalized least squares, it is more efficient than estimating each equation separately by OLS.