Comparar métodos

	Time-varying parameter WLS	Modelo de espacio de estados (Filtro de Kalman)
Campo	Econometría	Econometría
Familia	Regression model	Regression model
Año de origen	1976–1990	1990
Autor original	Cooley & Prescott (1976); Harvey (1990)	Harvey; Durbin & Koopman (state space treatment); Kalman filter
Tipo	Time-varying coefficient regression with observation weights	State space time series model
Fuente seminal	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.
Alias	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)
Relacionados	2	4
Resumen	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.