手法を比較
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| システムGMM（アレラーノ・ボバー / ブランドル・ボンド）× | 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≠ | 1998 | 2019 |
| 提唱者≠ | Arellano & Bover (1995); Blundell & Bond (1998) | Wooldridge (textbook treatment); classical least squares |
| 種類≠ | Dynamic panel data estimator | Linear regression |
| 原典≠ | 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 ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 別名 | Arellano-Bover estimator, Blundell-Bond estimator, dynamic panel GMM, Sistem GMM (Arellano-Bover / Blundell-Bond) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 関連≠ | 4 | 5 |
| 概要≠ | 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. | 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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