Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Robust Difference GMM× | Dinamiskais paneļa datu modelis× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1991 / 2005 | 1988–1991 |
| Autors≠ | Arellano & Bond (1991); robust inference extension via Windmeijer (2005) | Arellano & Bond (1991); Holtz-Eakin, Newey & Rosen (1988) |
| Tips≠ | GMM estimator with robust standard errors | Dynamic regression / GMM estimation |
| Pirmavots≠ | Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The 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 Arellano-Bond estimator, difference GMM with robust SE, HAC difference GMM, AB-GMM robust | dynamic panel model, panel data model with lagged dependent variable, DPD model, Arellano-Bond model |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | Robust Difference GMM applies the Arellano-Bond first-difference GMM estimator with heteroscedasticity- and autocorrelation-consistent (HAC) or Windmeijer-corrected standard errors, delivering valid inference for dynamic panel models even when error variances are non-constant or residuals are cross-sectionally correlated. | 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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