Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Структурна векторна авторегресия (SVAR)× | Динамичен панелен модел× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1980 | 1988–1991 |
| Създател≠ | Sims (1980); identification schemes by Blanchard & Quah (1989) | Arellano & Bond (1991); Holtz-Eakin, Newey & Rosen (1988) |
| Тип≠ | Multivariate time series model | Dynamic regression / GMM estimation |
| Основополагащ източник≠ | Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79(4), 655-673. link ↗ | 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 ↗ |
| Други названия | SVAR, structural vector autoregression, identified VAR, structural VAR model | dynamic panel model, panel data model with lagged dependent variable, DPD model, Arellano-Bond model |
| Свързани | 5 | 5 |
| Резюме≠ | Structural VAR extends the reduced-form VAR by imposing economic theory-based restrictions that identify orthogonal structural shocks. This allows researchers to disentangle the causal effects of distinct economic disturbances — such as supply versus demand shocks — and trace their dynamic propagation through a system of variables via impulse response functions and forecast error variance decompositions. | 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. |
| ScholarGateНабор от данни ↗ |
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