Salīdzināt metodes
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| 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≠ | Dynamic panel GMM estimator | 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 | Arellano-Bond GMM, AB-GMM, difference GMM estimator, dynamic panel GMM | dynamic panel model, panel data model with lagged dependent variable, DPD model, Arellano-Bond model |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | The Arellano-Bond GMM estimator addresses the two core problems of dynamic panel models — individual fixed effects correlated with the regressors, and the endogeneity introduced by a lagged dependent variable — by first-differencing to remove fixed effects and then using lagged levels of the dependent variable as internal instruments. | 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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