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| Modèle à effets fixes sur données de panel× | Estimateur GMM par différences (Estimateur d'Arellano-Bond)× | |
|---|---|---|
| Domaine | Économétrie | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1978 | 1991 |
| Auteur d'origine≠ | Mundlak (1978); classical treatment in Wooldridge (2010) and Baltagi (2021) | Manuel Arellano and Stephen Bond |
| Type≠ | Panel regression estimator | GMM panel estimator |
| Source fondatrice≠ | Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586 | 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 ↗ |
| Alias | within estimator, FE model, within-group estimator, LSDV model | Arellano-Bond estimator, AB-GMM, first-difference GMM, difference GMM estimator |
| Apparentées | 5 | 5 |
| Résumé≠ | The panel fixed effects (FE) model controls for all time-invariant, unit-specific unobserved heterogeneity by absorbing it into individual intercepts. By sweeping out unit means through the within transformation, FE yields unbiased estimates of the effect of time-varying regressors even when omitted unit-level confounders are correlated with those regressors. | Difference GMM, introduced by Arellano and Bond (1991), estimates dynamic panel data models by first-differencing the equation to remove fixed effects, then using lagged levels of the endogenous variables as GMM instruments. It is the standard approach when a lagged dependent variable or other endogenous regressors are present in a panel with many units and few time periods. |
| ScholarGateJeu de données ↗ |
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