Сравнение методов

	Time-varying parameter WLS	Модель пространства состояний (фильтр Калмана)
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1976–1990	1990
Автор метода	Cooley & Prescott (1976); Harvey (1990)	Harvey; Durbin & Koopman (state space treatment); Kalman filter
Тип	Time-varying coefficient regression with observation weights	State space time series model
Основополагающий источник	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.
Другие названия	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)
Связанные	2	4
Сводка	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.