Comparar métodos

	Cointegración de Engle-Granger con Parámetros Variables en el Tiempo	Modelo de espacio de estados (Filtro de Kalman)
Campo	Econometría	Econometría
Familia	Regression model	Regression model
Año de origen	1987/1999	1990
Autor original	Engle & Granger (1987) for cointegration; Park & Hahn (1999) for TVP extension	Harvey; Durbin & Koopman (state space treatment); Kalman filter
Tipo	Time-series cointegration model	State space time series model
Fuente seminal	Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
Alias	TVP Engle-Granger cointegration, time-varying cointegration, TVP-EG cointegration, varying-coefficient cointegration	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
Relacionados	3	4
Resumen	Time-varying parameter (TVP) Engle-Granger cointegration extends the classical two-step Engle-Granger framework by allowing the long-run relationship between integrated series to evolve over time. Instead of assuming a fixed cointegrating vector, the cointegrating coefficients are modelled as stochastic processes — typically via a random walk — and estimated with the Kalman filter or related state-space methods.	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.