Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Time-varying parameter OLS× | Модель простір-стан (фільтр Калмана)× | |
|---|---|---|
| Галузь | Економетрика | Економетрика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1976 | 1990 |
| Автор методу≠ | Cooley & Prescott (1976); further developed by Harvey (1990) | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| Тип≠ | Time-series regression with evolving coefficients | State space time series model |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви | 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) |
| Пов'язані | 4 | 4 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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