Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель скользящего среднего с изменяющимися во времени параметрами× | Модель ARMA с изменяющимися во времени параметрами (TVP-ARMA)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1990s | 1976 |
| Автор метода≠ | Harvey, A. C.; Durbin, J. & Koopman, S. J. | Cooley & Prescott (1976); further formalised by Harvey (1989) |
| Тип≠ | Time-varying state-space model | State-space time series model |
| Основополагающий источник≠ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 9780521321969 | Cooley, T. F., & Prescott, E. C. (1976). Estimation in the presence of stochastic parameter variation. Econometrica, 44(1), 167–184. DOI ↗ |
| Другие названия | TVP-MA model, state-space MA, Kalman filter MA, time-varying MA | TVP-ARMA, time-varying ARMA, state-space ARMA, locally stationary ARMA |
| Связанные≠ | 6 | 3 |
| Сводка≠ | The time-varying parameter moving average (TVP-MA) model extends the standard MA model by allowing the moving-average coefficients to change over time. Cast as a state-space system, it is estimated via the Kalman filter and smoother, making it well suited for series where the shock-transmission dynamics evolve across the sample. | The time-varying parameter ARMA (TVP-ARMA) model extends the classical ARMA framework by allowing the autoregressive and moving-average coefficients to evolve over time. Embedded in a state-space representation and estimated via the Kalman filter, it captures structural change and parameter instability in time series without requiring an explicit breakpoint. |
| ScholarGateНабор данных ↗ |
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