Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Arellano-Bond GMM novērtētājs× | Dinamiskais paneļa datu modelis× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1991 | 1988–1991 |
| Autors≠ | Manuel Arellano and Stephen Bond | Arellano & Bond (1991); Holtz-Eakin, Newey & Rosen (1988) |
| Tips≠ | GMM estimator for dynamic panel data | 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. 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 | AB-GMM, Difference GMM, first-difference GMM, Arellano-Bond estimator | dynamic panel model, panel data model with lagged dependent variable, DPD model, Arellano-Bond model |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. | 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. |
| ScholarGateDatu kopa ↗ |
|
|