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Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Градентно-базирани методи за оптимизация× | Структурна векторна авторегресия (SVAR)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1969 (Granger); TVP extension ~2005 | 1980 |
| Създател≠ | C.W.J. Granger (causality concept); TVP extension developed by Primiceri (2005) and subsequent literature | Sims (1980); identification schemes by Blanchard & Quah (1989) |
| Тип≠ | Causality test / time-varying model | Multivariate time series model |
| Основополагащ източник≠ | Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424-438. DOI ↗ | Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79(4), 655-673. link ↗ |
| Други названия | TVP Granger causality, rolling-window Granger causality, time-varying Granger test, dynamic Granger causality | SVAR, structural vector autoregression, identified VAR, structural VAR model |
| Свързани≠ | 4 | 5 |
| Резюме≠ | Time-varying parameter Granger causality extends the classical Granger causality framework by allowing the predictive relationships between time series to evolve across time. Instead of assuming fixed causal effects, the model estimates causal coefficients that can shift, capturing structural breaks, regime changes, or gradual evolution in economic or financial relationships. | 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. |
| ScholarGateНабор от данни ↗ |
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