Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Байєсівська модель динамічних панельних даних× | Оцінювач GMM за Ареллано-Бондом× | |
|---|---|---|
| Галузь | Економетрика | Економетрика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 2002–2007 | 1991 |
| Автор методу≠ | Hsiao, Pesaran, Tahmiscioglu; Arellano & Bonhomme | Manuel Arellano and Stephen Bond |
| Тип≠ | Bayesian panel model | GMM estimator for dynamic panel data |
| Основоположне джерело≠ | Hsiao, C., Pesaran, M. H., & Tahmiscioglu, A. K. (2002). Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods. Journal of Econometrics, 109(1), 107–150. 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 ↗ |
| Інші назви | Bayesian DPD model, Bayesian lagged dependent variable panel model, Bayesian autoregressive panel model, B-DPD | AB-GMM, Difference GMM, first-difference GMM, Arellano-Bond estimator |
| Пов'язані≠ | 6 | 5 |
| Підсумок≠ | The Bayesian dynamic panel data model extends standard dynamic panel models — which include a lagged dependent variable to capture state dependence — by estimating all parameters within a Bayesian framework. Prior distributions are combined with the likelihood to yield a full posterior distribution over model parameters, enabling probabilistic inference and coherent uncertainty quantification even in short panels. | The Arellano-Bond GMM estimator is the standard approach for dynamic panel data models in which the lagged dependent variable appears as a regressor. By first-differencing to remove fixed effects and using deeper lags as instruments, it yields consistent estimates even when the error is serially correlated and regressors are endogenous. |
| ScholarGateНабір даних ↗ |
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