Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Bayesian Difference GMM× | Динамическая панельная модель× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1991/2003 | 1991–1998 |
| Автор метода≠ | Arellano & Bond (1991) for Difference GMM; Chernozhukov & Hong (2003) for Bayesian GMM framework | Arellano & Bond (1991); Blundell & Bond (1998) |
| Тип≠ | Dynamic panel estimator (Bayesian) | Dynamic panel regression |
| Основополагающий источник≠ | Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277-297. DOI ↗ | 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 ↗ |
| Другие названия | Bayesian Arellano-Bond estimator, Bayesian difference GMM, quasi-Bayesian difference GMM, Bayesian first-difference GMM | dynamic panel model, lagged dependent variable panel model, Arellano-Bond type dynamic panel, GMM dynamic panel |
| Связанные | 5 | 5 |
| Сводка≠ | Bayesian Difference GMM combines the Arellano-Bond first-differencing strategy for dynamic panel data with a Bayesian inference framework. By treating the GMM moment conditions as a quasi-likelihood and placing priors on parameters, the approach produces a full posterior distribution over coefficients rather than a single point estimate with asymptotic standard errors. | 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. |
| ScholarGateНабор данных ↗ |
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