方法对比
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| 时变参数自回归滑动平均模型 (TVP-ARMA)× | 状态空间模型（卡尔曼滤波器）× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1976 | 1990 |
| 提出者≠ | Cooley & Prescott (1976); further formalised by Harvey (1989) | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| 类型≠ | State-space time series model | State space time series model |
| 开创性文献≠ | Cooley, T. F., & Prescott, E. C. (1976). Estimation in the presence of stochastic parameter variation. Econometrica, 44(1), 167–184. DOI ↗ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ |
| 别名 | TVP-ARMA, time-varying ARMA, state-space ARMA, locally stationary ARMA | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) |
| 相关≠ | 3 | 4 |
| 摘要≠ | The time-varying parameter ARMA (TVP-ARMA) model extends the classical ARMA framework by allowing the autoregressive and moving-average coefficients to evolve over time. Embedded in a state-space representation and estimated via the Kalman filter, it captures structural change and parameter instability in time series without requiring an explicit breakpoint. | 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. |
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