手法を比較

	非線形一般化最小二乗法（NGLS）	一般化モーメント法 (GMM) 推定	見かけ上無関係な回帰 (SUR)
分野	計量経済学	計量経済学	計量経済学
系統	Regression model	Regression model	Regression model
提唱年	1975	1982	1962
提唱者	Gallant (1975); extended by Davidson & MacKinnon	Lars Peter Hansen; Arellano & Bond (dynamic panel)	Arnold Zellner
種類	Nonlinear estimator	Moment-condition estimator	System regression (multi-equation)
原典	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.
別名	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)
関連	2	5	5
概要	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.