Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| MCOV con Parámetros Variables en el Tiempo (MCOV-PVT)× | Modelo de espacio de estados (Filtro de Kalman)× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1976 | 1990 |
| Autor original≠ | Cooley & Prescott (1976); further developed by Harvey (1990) | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| Tipo≠ | Time-series regression with evolving coefficients | State space time series model |
| Fuente seminal≠ | Cooley, T. F., & Prescott, E. C. (1976). Estimation in the Presence of Stochastic Parameter Variation. Econometrica, 44(1), 167–184. DOI ↗ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ |
| Alias | TVP-OLS, time-varying coefficient regression, rolling OLS, locally weighted OLS | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) |
| Relacionados | 4 | 4 |
| Resumen≠ | Time-Varying Parameter OLS extends classical ordinary least squares to allow regression coefficients to change over time. Instead of assuming fixed slopes throughout the sample, the model treats each coefficient as a stochastic process, tracking how economic relationships evolve — making it well-suited for analysing structural change in time-series data. | 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. |
| ScholarGateConjunto de datos ↗ |
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