Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель векторной авторегрессии с изменяющимися во времени параметрами (TVP-VAR)× | Структурная векторная авторегрессия (SVAR)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2005 | 1980 |
| Автор метода≠ | Primiceri (2005); Cogley & Sargent (2001, 2005) | Sims (1980); identification schemes by Blanchard & Quah (1989) |
| Тип≠ | Multivariate time-series model with drifting coefficients | Multivariate time series model |
| Основополагающий источник≠ | Primiceri, G. E. (2005). Time varying structural vector autoregressions and monetary policy. Review of Economic Studies, 72(3), 821-852. DOI ↗ | Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79(4), 655-673. link ↗ |
| Другие названия | TVP-VAR, time-varying VAR, TV-VAR, drifting-coefficient VAR | SVAR, structural vector autoregression, identified VAR, structural VAR model |
| Связанные≠ | 6 | 5 |
| Сводка≠ | The Time-Varying Parameter VAR (TVP-VAR) model extends the standard vector autoregression by allowing the coefficients and error covariances to evolve gradually over time. Estimated via Bayesian methods and MCMC simulation, it captures how dynamic relationships between macroeconomic or financial variables shift across different economic regimes without requiring pre-specified break points. | 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 данных ↗ |
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