方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 状态空间模型（卡尔曼滤波器）× | 结构向量自回归 (SVAR)× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1990 | 1980 |
| 提出者≠ | Harvey; Durbin & Koopman (state space treatment); Kalman filter | Sims (1980); identification schemes by Blanchard & Quah (1989) |
| 类型≠ | State space time series model | Multivariate time series model |
| 开创性文献≠ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ | Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79(4), 655-673. link ↗ |
| 别名 | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) | SVAR, structural vector autoregression, identified VAR, structural VAR model |
| 相关≠ | 4 | 5 |
| 摘要≠ | A state space model is a general time series framework that describes a series through unobserved (latent) state variables linked by a measurement equation and a transition equation, with the states estimated in real time by the Kalman filter. Developed in the state space tradition of Harvey (1990) and Durbin & Koopman (2012), it nests ARIMA and exponential smoothing as special cases. | 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. |
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