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Minimi Quadrati Generalizzati Non Lineari (NGLS)×Stima con il Metodo Generalizzato dei Momenti (GMM)×Regressioni Apparentemente Non Correlate (SUR)×
CampoEconometriaEconometriaEconometria
FamigliaRegression modelRegression modelRegression model
Anno di origine197519821962
IdeatoreGallant (1975); extended by Davidson & MacKinnonLars Peter Hansen; Arellano & Bond (dynamic panel)Arnold Zellner
TipoNonlinear estimatorMoment-condition estimatorSystem regression (multi-equation)
Fonte seminaleGallant, A. R. (1987). Nonlinear Statistical Models. Wiley. ISBN: 978-0471802600Hansen, L. P. (1982). Large Sample Properties of Generalized Method of Moments Estimators. Econometrica, 50(4), 1029-1054. DOI ↗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. DOI ↗
AliasNGLS, nonlinear generalized least squares, feasible nonlinear GLS, FNGLSgeneralized method of moments, GMM, Arellano-Bond estimator, Genelleştirilmiş Momentler Yöntemi (GMM)SUR, Zellner's SUR, seemingly unrelated regression equations, Görünürde İlişkisiz Regresyon (SUR)
Correlati255
SintesiNonlinear 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.
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ScholarGateConfronta i metodi: Nonlinear GLS · GMM Estimation · Seemingly Unrelated Regression. Consultato il 2026-06-18 da https://scholargate.app/it/compare