Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель пространства состояний (фильтр Калмана)× | Взвешенный метод наименьших квадратов (ВМНК)× | |
|---|---|---|
| Область≠ | Эконометрика | Статистика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1990 | 1935 |
| Автор метода≠ | Harvey; Durbin & Koopman (state space treatment); Kalman filter | Alexander Craig Aitken |
| Тип≠ | State space time series model | Weighted linear estimator |
| Основополагающий источник≠ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ | Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48. DOI ↗ |
| Другие названия | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) | WLS, weighted regression, heteroscedasticity-corrected OLS, variance-weighted least squares |
| Связанные≠ | 4 | 3 |
| Сводка≠ | 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. | Weighted Least Squares is a generalization of Ordinary Least Squares (OLS) regression that assigns each observation a weight inversely proportional to its error variance, thereby down-weighting high-variance data points and up-weighting precise ones. Introduced in its general matrix form by Alexander Craig Aitken in 1935, WLS is the canonical remedy when heteroscedasticity is present and the error variance structure is known or can be reliably estimated. |
| ScholarGateНабор данных ↗ |
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