Сравнение методов

	Динамическая панельная модель	Системный GMM для панельных данных (оценщик Бланделла-Бонда)
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1991–1998	1998
Автор метода	Arellano & Bond (1991); Blundell & Bond (1998)	Blundell & Bond (1998); Arellano & Bover (1995)
Тип	Dynamic panel regression	GMM estimator for dynamic panel data
Основополагающий источник	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.	Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143.
Другие названия	dynamic panel model, lagged dependent variable panel model, Arellano-Bond type dynamic panel, GMM dynamic panel	System GMM, Blundell-Bond estimator, SYS-GMM, two-step System GMM
Связанные	5	6
Сводка	The dynamic panel data model extends standard panel regression by including one or more lagged values of the outcome variable as regressors. Because past outcomes directly predict current outcomes, the model captures persistence and adjustment dynamics — but it also introduces a correlation between the lagged dependent variable and the individual fixed effect, rendering OLS and standard fixed-effects estimators inconsistent. GMM-based approaches developed by Arellano-Bond and Blundell-Bond resolve this problem.	Panel System GMM is a two-equation GMM estimator for dynamic panel data that stacks the differenced equation (using lagged levels as instruments) with the levels equation (using lagged differences as instruments). Developed by Blundell and Bond (1998) on the foundation of Arellano and Bover (1995), it is the preferred tool when the lagged dependent variable is highly persistent or individual effects are large.