Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Arellano-Bond GMM novērtētājs× | Modelis ar fiksētajiem efektiem× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1991 | 1971–1978 |
| Autors≠ | Manuel Arellano and Stephen Bond | Mundlak (1978); Nerlove (1971); classical panel econometrics |
| Tips≠ | GMM estimator for dynamic panel data | Panel regression estimator |
| 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 ↗ | Baltagi, B. H. (2021). Econometric Analysis of Panel Data (6th ed.). Springer. ISBN: 978-3030538002 |
| Citi nosaukumi | AB-GMM, Difference GMM, first-difference GMM, Arellano-Bond estimator | FE model, within estimator, least squares dummy variable, LSDV regression |
| 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 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. |
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