Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Robust Difference GMM× | Sistēmas GMM panelim (Blundell-Bonda novērtētājs)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1991 / 2005 | 1998 |
| Autors≠ | Arellano & Bond (1991); robust inference extension via Windmeijer (2005) | Blundell & Bond (1998); Arellano & Bover (1995) |
| Tips≠ | GMM estimator with robust standard errors | 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. The 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 ↗ |
| Citi nosaukumi | robust Arellano-Bond estimator, difference GMM with robust SE, HAC difference GMM, AB-GMM robust | System GMM, Blundell-Bond estimator, SYS-GMM, two-step System GMM |
| Saistītās | 6 | 6 |
| 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. | 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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