विधियों की तुलना करें

	अरेखीय सामान्यीकृत न्यूनतम वर्ग (NGLS)	सामान्यीकृत क्षण विधि (GMM) आकलन	प्रतीत होने वाली असंबंधित रिग्रेशन (SUR)
क्षेत्र	अर्थमिति	अर्थमिति	अर्थमिति
परिवार	Regression model	Regression model	Regression model
उद्भव वर्ष	1975	1982	1962
प्रवर्तक	Gallant (1975); extended by Davidson & MacKinnon	Lars Peter Hansen; Arellano & Bond (dynamic panel)	Arnold Zellner
प्रकार	Nonlinear estimator	Moment-condition estimator	System regression (multi-equation)
मौलिक स्रोत	Gallant, A. R. (1987). Nonlinear Statistical Models. Wiley. ISBN: 978-0471802600	Hansen, L. P. (1982). Large Sample Properties of Generalized Method of Moments Estimators. Econometrica, 50(4), 1029-1054.	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.
उपनाम	NGLS, nonlinear generalized least squares, feasible nonlinear GLS, FNGLS	generalized method of moments, GMM, Arellano-Bond estimator, Genelleştirilmiş Momentler Yöntemi (GMM)	SUR, Zellner's SUR, seemingly unrelated regression equations, Görünürde İlişkisiz Regresyon (SUR)
संबंधित	2	5	5
सारांश	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.	The Generalized Method of Moments is a general-purpose econometric estimator that recovers parameters from population moment conditions, introduced by Lars Peter Hansen in 1982. It is widely used for instrumental-variable estimation, dynamic panel-data models (the Arellano-Bond estimator), and time-series applications.	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.