Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Valsts telpas modelis (Kalmana filtrs)× | Strukturālais laika sēriju modelis (Pamata strukturālais modelis)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads | 1990 | 1990 |
| Autors≠ | Harvey; Durbin & Koopman (state space treatment); Kalman filter | Andrew C. Harvey |
| Tips≠ | State space time series model | State-space (unobserved components) time series model |
| Pirmavots | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737 |
| Citi nosaukumi | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) | BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM) |
| Saistītās | 4 | 4 |
| Kopsavilkums≠ | 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. | The Structural Time Series Model, in its Basic Structural Model (BSM) form, is Andrew Harvey's state-space approach that decomposes a series into separate stochastic trend, seasonal, cyclical, and irregular components. Developed in Harvey's 1990 treatment, it is prized for interpretability and component decomposition where ARIMA only delivers a black-box fit. |
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