Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Модель структурного розриву Difference GMM (GMM з розривами у відмінностях)× | Модель динамічних панельних даних× | |
|---|---|---|
| Галузь | Економетрика | Економетрика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1991 / 1998 | 1988–1991 |
| Автор методу≠ | Arellano & Bond (Difference GMM); Bai & Perron (structural break testing) | Arellano & Bond (1991); Holtz-Eakin, Newey & Rosen (1988) |
| Тип≠ | Dynamic panel estimator with structural breaks | Dynamic regression / GMM estimation |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви | Difference GMM with structural breaks, break-augmented Arellano-Bond GMM, dynamic panel GMM with regime shifts, structural change Difference GMM | dynamic panel model, panel data model with lagged dependent variable, DPD model, Arellano-Bond model |
| Пов'язані≠ | 6 | 5 |
| Підсумок≠ | Structural Break Difference GMM extends the Arellano-Bond first-difference GMM estimator to dynamic panel settings where the data-generating process shifts at one or more unknown breakpoints. By explicitly incorporating break indicators or allowing regime-specific parameters, the estimator avoids the biased coefficient and invalid moment conditions that arise when a structural change is ignored in a standard Difference GMM fit. | The dynamic panel data model extends standard panel regression by including a lagged value of the outcome variable as a regressor, capturing persistence and adjustment dynamics. Because the lagged dependent variable is correlated with the unit-specific fixed effect, ordinary OLS or within estimators are biased; GMM-based methods using internal instruments are the standard remedy. |
| ScholarGateНабір даних ↗ |
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