Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Нелинейни обобщени най-малки квадрати (NGLS)× | Привидно несвързани регресии (SUR)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1975 | 1962 |
| Създател≠ | Gallant (1975); extended by Davidson & MacKinnon | Arnold Zellner |
| Тип≠ | Nonlinear estimator | System regression (multi-equation) |
| Основополагащ източник≠ | Gallant, A. R. (1987). Nonlinear Statistical Models. Wiley. ISBN: 978-0471802600 | 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 ↗ |
| Други названия | NGLS, nonlinear generalized least squares, feasible nonlinear GLS, FNGLS | SUR, Zellner's SUR, seemingly unrelated regression equations, Görünürde İlişkisiz Regresyon (SUR) |
| Свързани≠ | 2 | 5 |
| Резюме≠ | 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. | 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. |
| ScholarGateНабор от данни ↗ |
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