Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| GMM atšķirību metode (Arellano–Bonda 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 panel estimator | 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 | Arellano-Bond estimator, AB-GMM, first-difference GMM, difference GMM estimator | FE model, within estimator, least squares dummy variable, LSDV regression |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. | 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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