Salīdzināt metodes
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| Laika mainīgo parametru SARIMA modelis (TVP-SARIMA)× | Valsts telpas modelis (Kalmana filtrs)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1990s | 1990 |
| Autors≠ | Harvey, A. C.; Durbin, J. & Koopman, S. J. (state-space framework) | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| Tips≠ | Time-varying state-space model | State space time series model |
| Pirmavots | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 9780521321969 | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ |
| Citi nosaukumi | TVP-SARIMA, time-varying SARIMA, state-space SARIMA, adaptive SARIMA | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) |
| Saistītās | 4 | 4 |
| Kopsavilkums≠ | The Time-Varying Parameter SARIMA model extends the classical SARIMA framework by allowing autoregressive and moving-average coefficients to evolve over time. Cast as a state-space system and estimated with the Kalman filter, it captures both seasonal patterns and structural change within a single unified model. | 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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