方法对比
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| 时变参数 GARCH 模型 (TVP-GARCH)× | 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≠ | 1982–2013 | 1990 |
| 提出者≠ | Engle (1982) for ARCH/GARCH foundation; extended by Creal, Koopman & Lucas (2013) and others for time-varying parameter variants | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| 类型≠ | Volatility model with time-varying coefficients | State space time series model |
| 开创性文献≠ | Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007. DOI ↗ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ |
| 别名 | TVP-GARCH, time-varying GARCH, TV-GARCH, state-space GARCH | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) |
| 相关≠ | 5 | 4 |
| 摘要≠ | The Time-Varying Parameter GARCH model extends the standard GARCH framework by allowing the conditional variance parameters — including the ARCH and GARCH coefficients — to change over time rather than remaining fixed throughout the sample. This makes it well-suited to financial and macroeconomic series where volatility dynamics evolve across different market regimes or economic episodes. | 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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