Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Model cu Efecte Fixe× | Estimatorul GMM Arellano-Bond× | |
|---|---|---|
| Domeniu | Econometrie | Econometrie |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 1971–1978 | 1991 |
| Autorul original≠ | Mundlak (1978); Nerlove (1971); classical panel econometrics | Manuel Arellano and Stephen Bond |
| Tip≠ | Panel regression estimator | GMM estimator for dynamic panel data |
| Sursa seminală≠ | Baltagi, B. H. (2021). Econometric Analysis of Panel Data (6th ed.). Springer. ISBN: 978-3030538002 | 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 ↗ |
| Denumiri alternative | FE model, within estimator, least squares dummy variable, LSDV regression | AB-GMM, Difference GMM, first-difference GMM, Arellano-Bond estimator |
| Înrudite | 5 | 5 |
| Rezumat≠ | The fixed effects (FE) model is the workhorse estimator for panel data when unobserved unit-specific characteristics are suspected to correlate with the regressors. By absorbing each entity's time-invariant heterogeneity into a separate intercept, FE isolates the causal effect of within-unit variation and eliminates omitted-variable bias from time-constant confounders. | 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. |
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