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| Надежден динамичен панелен модел на данни× | Робастен анализ на панелни данни× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1991–2005 | 1987 |
| Създател≠ | Arellano & Bond (1991); robust extension via Windmeijer (2005) | Arellano (1987); White (1980) heteroscedasticity-consistent framework |
| Тип≠ | Dynamic panel estimator with robust inference | Robust estimation / inference correction |
| Основополагащ източник≠ | 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 ↗ | Arellano, M. (1987). Computing robust standard errors for within-groups estimators. Oxford Bulletin of Economics and Statistics, 49(4), 431–434. link ↗ |
| Други названия | robust dynamic panel, heteroscedasticity-robust dynamic panel, robust GMM dynamic panel, dynamic panel with robust standard errors | robust panel regression, cluster-robust panel estimation, panel regression with robust standard errors, HC/CR panel estimator |
| Свързани≠ | 5 | 6 |
| Резюме≠ | 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. | Robust panel data analysis applies standard panel estimators — fixed effects, random effects, or pooled OLS — while replacing conventional standard errors with cluster-robust or heteroscedasticity-consistent (HC) variants. The point estimates remain unchanged; what changes is the variance-covariance matrix used for inference, making t-tests and F-tests valid even when errors are heteroscedastic or correlated within cross-sectional units over time. |
| ScholarGateНабор от данни ↗ |
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