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| Stimatore Common Correlated Effects Mean Group (CCEMG)× | Stimatore Augmented Mean Group (AMG)× | Modello a Effetti Fissi per Dati Panel× | |
|---|---|---|---|
| Campo | Econometria | Econometria | Econometria |
| Famiglia | Regression model | Regression model | Regression model |
| Anno di origine≠ | 2006 | 2010 | 2014 |
| Ideatore≠ | M. Hashem Pesaran | Eberhardt & Teal; Bond & Eberhardt | Hsiao (textbook treatment); within transformation of panel data |
| Tipo≠ | Heterogeneous panel estimator | Heterogeneous panel data estimator | Panel data regression |
| Fonte seminale≠ | Pesaran, M. H. (2006). Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure. Econometrica, 74(4), 967-1012. DOI ↗ | Eberhardt, M. & Teal, F. (2010). Productivity Analysis in Global Manufacturing Production. Economics Series Working Papers, No. 515, University of Oxford. link ↗ | Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. DOI ↗ |
| Alias≠ | common correlated effects, CCE, CCEMG, Pesaran CCE estimator | AMG estimator, augmented mean group, Artırılmış Ortalama Grup Tahmincisi (AMG) | fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli |
| Correlati≠ | 4 | 4 | 5 |
| Sintesi≠ | The Common Correlated Effects Mean Group estimator, introduced by Pesaran in 2006, is a heterogeneous panel-data estimator that controls for cross-sectional dependence by approximating unobserved common factors with the cross-section averages of the variables. It remains consistent when the slope coefficients differ across units. | 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 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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