Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Dinamiskās instrumentālās mainīgās (Dinamiskais panelis IV / Arellano-Bond)× | Fiksēto efektu paneļa datu modelis× | |
|---|---|---|
| Nozare≠ | Cēloņsakarību secināšana | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1991 | 2014 |
| Autors≠ | Arellano & Bond (1991); extended by Blundell & Bond (1998) | Hsiao (textbook treatment); within transformation of panel data |
| Tips≠ | Dynamic panel causal estimation | Panel data regression |
| 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 ↗ | Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. DOI ↗ |
| Citi nosaukumi | Dynamic IV, Dynamic Panel IV, Arellano-Bond GMM, System GMM | fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | Dynamic Instrumental Variables estimation addresses endogeneity in panel models where the outcome depends on its own past values. By first-differencing to remove unit fixed effects and then using lagged levels as instruments for the differenced lagged outcome, it produces consistent causal estimates even when standard OLS or fixed-effects are biased by dynamic feedback. | The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014). |
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