Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель скользящего среднего с изменяющимися во времени параметрами× | Модель ARMA (авторегрессионная скользящая средняя)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1990s | 1970 |
| Автор метода≠ | Harvey, A. C.; Durbin, J. & Koopman, S. J. | George E. P. Box and Gwilym M. Jenkins |
| Тип≠ | Time-varying state-space model | Time series model |
| Основополагающий источник≠ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 9780521321969 | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Другие названия | TVP-MA model, state-space MA, Kalman filter MA, time-varying MA | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) |
| Связанные≠ | 6 | 5 |
| 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 ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a moving average part that accounts for past q error terms. It is the foundational framework of the Box-Jenkins methodology for univariate time series modelling and short-run forecasting. |
| ScholarGateНабор данных ↗ |
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