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| Augmented Mean Group (AMG) 推定量の概要× | 最小二乗法 (OLS) 回帰× | |
|---|---|---|
| 分野 | 計量経済学 | 計量経済学 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 2010 | 2019 |
| 提唱者≠ | Eberhardt & Teal; Bond & Eberhardt | Wooldridge (textbook treatment); classical least squares |
| 種類≠ | Heterogeneous panel data estimator | Linear regression |
| 原典≠ | Eberhardt, M. & Teal, F. (2010). Productivity Analysis in Global Manufacturing Production. Economics Series Working Papers, No. 515, University of Oxford. link ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 別名≠ | AMG estimator, augmented mean group, Artırılmış Ortalama Grup Tahmincisi (AMG) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 関連≠ | 4 | 5 |
| 概要≠ | 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. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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