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| Modello Robusto a Dati Panel Dinamici× | Modello Dinamico a Dati Panel× | |
|---|---|---|
| Campo | Econometria | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1991–2005 | 1988–1991 |
| Ideatore≠ | Arellano & Bond (1991); robust extension via Windmeijer (2005) | Arellano & Bond (1991); Holtz-Eakin, Newey & Rosen (1988) |
| Tipo≠ | Dynamic panel estimator with robust inference | Dynamic regression / GMM estimation |
| Fonte seminale | 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., & 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 ↗ |
| Alias | robust dynamic panel, heteroscedasticity-robust dynamic panel, robust GMM dynamic panel, dynamic panel with robust standard errors | dynamic panel model, panel data model with lagged dependent variable, DPD model, Arellano-Bond model |
| Correlati | 5 | 5 |
| Sintesi≠ | 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. | The dynamic panel data model extends standard panel regression by including a lagged value of the outcome variable as a regressor, capturing persistence and adjustment dynamics. Because the lagged dependent variable is correlated with the unit-specific fixed effect, ordinary OLS or within estimators are biased; GMM-based methods using internal instruments are the standard remedy. |
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