Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Augmented Mean Group (AMG) novērtētājs× | Modelis ar nejaušiem efektiem panelī× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2010 | 2021 |
| Autors≠ | Eberhardt & Teal; Bond & Eberhardt | Baltagi (textbook treatment); classical random-effects panel estimator |
| Tips≠ | Heterogeneous panel data estimator | Panel data regression |
| Pirmavots≠ | Eberhardt, M. & Teal, F. (2010). Productivity Analysis in Global Manufacturing Production. Economics Series Working Papers, No. 515, University of Oxford. link ↗ | Baltagi, B. H. (2021). Econometric Analysis of Panel Data (6th ed.). Springer. DOI ↗ |
| Citi nosaukumi≠ | AMG estimator, augmented mean group, Artırılmış Ortalama Grup Tahmincisi (AMG) | random effects panel model, RE estimator, GLS random effects, Panel Veri — Rassal Etkiler Modeli |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | The Augmented Mean Group estimator, developed by Eberhardt and Teal (2010), is a panel data method for estimating heterogeneous slope coefficients in the presence of cross-sectional dependence. It approximates the unobserved common dynamic process driving all units and folds it into unit-by-unit regressions, then averages the results. | The Random Effects model is a panel-data regression that treats unobserved individual heterogeneity as a random component drawn from a common distribution, rather than a separate parameter for each unit. It is a standard estimator in panel econometrics, developed in textbook treatments such as Baltagi's Econometric Analysis of Panel Data (2021). |
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