Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| System GMM (Arellano-Bover / Blundell-Bond)× | Модель випадкових ефектів для панельних даних× | |
|---|---|---|
| Галузь | Економетрика | Економетрика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1998 | 2021 |
| Автор методу≠ | Arellano & Bover (1995); Blundell & Bond (1998) | Baltagi (textbook treatment); classical random-effects panel estimator |
| Тип≠ | Dynamic panel data estimator | Panel data regression |
| Основоположне джерело≠ | 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. DOI ↗ |
| Інші назви | Arellano-Bover estimator, Blundell-Bond estimator, dynamic panel GMM, Sistem GMM (Arellano-Bover / Blundell-Bond) | random effects panel model, RE estimator, GLS random effects, Panel Veri — Rassal Etkiler Modeli |
| Пов'язані≠ | 4 | 5 |
| Підсумок≠ | System GMM is a generalized method of moments estimator for dynamic panel models that contain a lagged dependent variable. Introduced by Blundell and Bond (1998), building on Arellano and Bover, it augments the differenced equation of the earlier difference GMM (Arellano-Bond) with the equation in levels to deliver consistent estimates when N is large and T is small. | 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). |
| ScholarGateНабір даних ↗ |
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