方法对比
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| 时变参数自回归积分滑动平均模型 (TVP-ARIMA)× | 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≠ | 1976–1989 | 1990 |
| 提出者≠ | Cooley & Prescott (1976); Harvey (1989) state-space formulation | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| 类型≠ | Time series model with evolving coefficients | State space time series model |
| 开创性文献≠ | Harvey, A. C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 9780521405737 | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ |
| 别名 | TVP-ARIMA, time-varying ARIMA, adaptive ARIMA, state-space ARIMA | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) |
| 相关≠ | 3 | 4 |
| 摘要≠ | The time-varying parameter ARIMA model extends the classical ARIMA framework by allowing its autoregressive and moving-average coefficients to evolve over time rather than remaining fixed. Cast in state-space form and estimated via the Kalman filter, it is designed for economic and financial time series whose dynamic structure shifts in response to structural breaks, policy changes, or regime transitions. | 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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