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| Kointegracja Engle’a-Grangera ze zmiennymi w czasie parametrami× | Model przestrzeni stanów (filtr Kalmana)× | |
|---|---|---|
| Dziedzina | Ekonometria | Ekonometria |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 1987/1999 | 1990 |
| Twórca≠ | Engle & Granger (1987) for cointegration; Park & Hahn (1999) for TVP extension | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| Typ≠ | Time-series cointegration model | State space time series model |
| Źródło pierwotne≠ | Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. DOI ↗ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ |
| Inne nazwy | TVP Engle-Granger cointegration, time-varying cointegration, TVP-EG cointegration, varying-coefficient cointegration | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) |
| Pokrewne≠ | 3 | 4 |
| Podsumowanie≠ | Time-varying parameter (TVP) Engle-Granger cointegration extends the classical two-step Engle-Granger framework by allowing the long-run relationship between integrated series to evolve over time. Instead of assuming a fixed cointegrating vector, the cointegrating coefficients are modelled as stochastic processes — typically via a random walk — and estimated with the Kalman filter or related state-space methods. | 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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