Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| WLS cu Parametri Variabili în Timp (TVP-WLS)× | Modelul spațiului de stare (Filtrul Kalman)× | |
|---|---|---|
| Domeniu | Econometrie | Econometrie |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 1976–1990 | 1990 |
| Autorul original≠ | Cooley & Prescott (1976); Harvey (1990) | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| Tip≠ | Time-varying coefficient regression with observation weights | State space time series model |
| Sursa seminală | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737 | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ |
| Denumiri alternative | TVP-WLS, time-varying coefficient WLS, locally weighted time-varying regression, TVP weighted regression | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) |
| Înrudite≠ | 2 | 4 |
| Rezumat≠ | Time-Varying Parameter WLS is a regression technique for time-series data in which the slope and intercept coefficients are allowed to change over time while observations are weighted to account for heteroscedasticity or to discount distant data. It combines the flexibility of state-space coefficient evolution with the variance-correcting power of weighted least squares. | 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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