Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель скользящего среднего с изменяющимися во времени параметрами× | Модель скользящего среднего (MA)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1990s | 1970 |
| Автор метода≠ | Harvey, A. C.; Durbin, J. & Koopman, S. J. | Box and Jenkins |
| Тип≠ | Time-varying state-space model | Linear 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., & Reinsel, G. C. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0130607744 |
| Другие названия | TVP-MA model, state-space MA, Kalman filter MA, time-varying MA | MA model, MA(q) process, moving-average process, Box-Jenkins MA |
| 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 Moving Average model of order q — written MA(q) — expresses the current value of a time series as a linear combination of the current and past random shocks (innovations). Unlike the AR model which uses lagged values of the series itself, the MA model uses lagged error terms, making it well-suited for capturing short-lived disturbances that dissipate over q periods. |
| ScholarGateНабор данных ↗ |
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