방법 비교

	비선형 일반화 최소제곱법 (Nonlinear Generalized Least Squares, NGLS)	일반화 적률법 (GMM) 추정	겉보기에는 관련 없어 보이는 회귀(SUR)
분야	계량경제학	계량경제학	계량경제학
계열	Regression model	Regression model	Regression model
기원 연도	1975	1982	1962
창시자	Gallant (1975); extended by Davidson & MacKinnon	Lars Peter Hansen; Arellano & Bond (dynamic panel)	Arnold Zellner
유형	Nonlinear estimator	Moment-condition estimator	System regression (multi-equation)
원전	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.
별칭	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)
관련	2	5	5
요약	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.