Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Robuust dynamisch paneldatamodel× | Panel System GMM (Blundell-Bond Schatter)× | |
|---|---|---|
| Vakgebied | Econometrie | Econometrie |
| Familie | Regression model | Regression model |
| Jaar van ontstaan≠ | 1991–2005 | 1998 |
| Grondlegger≠ | Arellano & Bond (1991); robust extension via Windmeijer (2005) | Blundell & Bond (1998); Arellano & Bover (1995) |
| Type≠ | Dynamic panel estimator with robust inference | GMM estimator for dynamic panel data |
| Oorspronkelijke bron≠ | Arellano, M., & Bond, S. (1991). Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. Review of Economic Studies, 58(2), 277–297. DOI ↗ | Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143. DOI ↗ |
| Aliassen | robust dynamic panel, heteroscedasticity-robust dynamic panel, robust GMM dynamic panel, dynamic panel with robust standard errors | System GMM, Blundell-Bond estimator, SYS-GMM, two-step System GMM |
| Verwant≠ | 5 | 6 |
| Samenvatting≠ | The robust dynamic panel data model combines the dynamic panel GMM framework — which handles endogeneity from lagged dependent variables and unobserved heterogeneity — with robust covariance estimation that remains valid under heteroscedasticity and serial correlation. The Windmeijer finite-sample correction is the standard robust adjustment applied to two-step GMM estimators in this setting. | Panel System GMM is a two-equation GMM estimator for dynamic panel data that stacks the differenced equation (using lagged levels as instruments) with the levels equation (using lagged differences as instruments). Developed by Blundell and Bond (1998) on the foundation of Arellano and Bover (1995), it is the preferred tool when the lagged dependent variable is highly persistent or individual effects are large. |
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