Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Robusts dinamiskā paneļa datu modelis× | Arellano-Bond GMM novērtētājs× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1991–2005 | 1991 |
| Autors≠ | Arellano & Bond (1991); robust extension via Windmeijer (2005) | Manuel Arellano and Stephen Bond |
| Tips≠ | Dynamic panel estimator with robust inference | GMM estimator for dynamic panel data |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi | robust dynamic panel, heteroscedasticity-robust dynamic panel, robust GMM dynamic panel, dynamic panel with robust standard errors | AB-GMM, Difference GMM, first-difference GMM, Arellano-Bond estimator |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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 Arellano-Bond GMM estimator is the standard approach for dynamic panel data models in which the lagged dependent variable appears as a regressor. By first-differencing to remove fixed effects and using deeper lags as instruments, it yields consistent estimates even when the error is serially correlated and regressors are endogenous. |
| ScholarGateDatu kopa ↗ |
|
|