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	Time-varying parameter WLS	Ważone Metody Najmniejszych Kwadratów (WLS)
Dziedzina	Ekonometria	Statystyka
Rodzina	Regression model	Regression model
Rok powstania	1976–1990	1935
Twórca	Cooley & Prescott (1976); Harvey (1990)	Alexander Craig Aitken
Typ	Time-varying coefficient regression with observation weights	Weighted linear estimator
Źródło pierwotne	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737	Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.
Inne nazwy	TVP-WLS, time-varying coefficient WLS, locally weighted time-varying regression, TVP weighted regression	WLS, weighted regression, heteroscedasticity-corrected OLS, variance-weighted least squares
Pokrewne	2	3
Podsumowanie	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.	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.