Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Model stavového prostoru (Kalmanův filtr)× | Model vektorové autoregrese (VAR)× | |
|---|---|---|
| Obor | Ekonometrie | Ekonometrie |
| Rodina | Regression model | Regression model |
| Rok vzniku≠ | 1990 | 2005 |
| Tvůrce≠ | Harvey; Durbin & Koopman (state space treatment); Kalman filter | Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition |
| Typ≠ | State space time series model | Multivariate time-series model |
| Původní zdroj≠ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ | Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. DOI ↗ |
| Další názvy | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) | vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon |
| Příbuzné | 4 | 4 |
| Shrnutí≠ | 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. | Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005). |
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