方法对比
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| 非线性广义最小二乘 (NGLS)× | 广义矩估计法 (GMM)× | 看似无关的回归 (SUR)× | |
|---|---|---|---|
| 领域 | 计量经济学 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model | Regression model |
| 起源年份≠ | 1975 | 1982 | 1962 |
| 提出者≠ | Gallant (1975); extended by Davidson & MacKinnon | Lars Peter Hansen; Arellano & Bond (dynamic panel) | Arnold Zellner |
| 类型≠ | Nonlinear estimator | Moment-condition estimator | System regression (multi-equation) |
| 开创性文献≠ | 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. 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 ↗ |
| 别名 | 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) |
| 相关≠ | 2 | 5 | 5 |
| 摘要≠ | 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. |
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