Salīdzināt metodes

	Laika mainīgo parametru VLS (TVP-WLS)	Valsts telpas modelis (Kalmana filtrs)	Svērto mazāko kvadrātu metode (WLS)
Nozare	Ekonometrija	Ekonometrija	Statistika
Saime	Regression model	Regression model	Regression model
Izcelsmes gads	1976–1990	1990	1935
Autors	Cooley & Prescott (1976); Harvey (1990)	Harvey; Durbin & Koopman (state space treatment); Kalman filter	Alexander Craig Aitken
Tips	Time-varying coefficient regression with observation weights	State space time series model	Weighted linear estimator
Pirmavots	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.	Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.
Citi nosaukumi	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)	WLS, weighted regression, heteroscedasticity-corrected OLS, variance-weighted least squares
Saistītās	2	4	3
Kopsavilkums	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.	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.