Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Dinamiskais paneļdatu modelis× | Fiksēto efektu paneļa modelis (FE)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1991–1998 | 1978 |
| Autors≠ | Arellano & Bond (1991); Blundell & Bond (1998) | Mundlak (1978); classical treatment in Wooldridge (2010) and Baltagi (2021) |
| Tips≠ | Dynamic panel regression | 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 ↗ | Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586 |
| Citi nosaukumi | dynamic panel model, lagged dependent variable panel model, Arellano-Bond type dynamic panel, GMM dynamic panel | within estimator, FE model, within-group estimator, LSDV model |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | The dynamic panel data model extends standard panel regression by including one or more lagged values of the outcome variable as regressors. Because past outcomes directly predict current outcomes, the model captures persistence and adjustment dynamics — but it also introduces a correlation between the lagged dependent variable and the individual fixed effect, rendering OLS and standard fixed-effects estimators inconsistent. GMM-based approaches developed by Arellano-Bond and Blundell-Bond resolve this problem. | 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. |
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