Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Time-varying parameter GLS× | Модель пространства состояний (фильтр Калмана)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1976 | 1990 |
| Автор метода≠ | Cooley & Prescott | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| Тип≠ | Time-series regression with drifting coefficients | State space time series model |
| Основополагающий источник≠ | Cooley, T. F., & Prescott, E. C. (1976). Estimation in the presence of stochastic parameter variation. Econometrica, 44(1), 167–184. DOI ↗ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ |
| Другие названия | TVP-GLS, time-varying coefficient GLS, adaptive GLS, state-space GLS | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) |
| Связанные≠ | 2 | 4 |
| Сводка≠ | Time-varying parameter GLS extends generalized least squares to settings where regression coefficients are not fixed constants but evolve over time according to a stochastic process. By embedding the model in a state-space framework and applying GLS corrections for non-spherical errors, it captures structural change, regime shifts, and gradually drifting relationships in time-series data. | 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. |
| ScholarGateНабор данных ↗ |
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