Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель скользящего среднего с изменяющимися во времени параметрами× | Авторегрессионная модель с изменяющимися во времени параметрами (TVP-AR)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1990s | 1976–2005 |
| Автор метода≠ | Harvey, A. C.; Durbin, J. & Koopman, S. J. | Cooley & Prescott (1976); further developed by Kim & Nelson (1999) and Cogley & Sargent (2001, 2005) |
| Тип≠ | Time-varying state-space model | Time-series model with drifting coefficients |
| Основополагающий источник≠ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 9780521321969 | Cogley, T., & Sargent, T. J. (2005). Drifts and volatilities: Monetary policies and outcomes in the post WWII US. Review of Economic Dynamics, 8(2), 262-302. DOI ↗ |
| Другие названия | TVP-MA model, state-space MA, Kalman filter MA, time-varying MA | TVP-AR, time-varying AR, state-space AR with drifting coefficients, random-walk coefficient AR |
| Связанные≠ | 6 | 4 |
| 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 Autoregressive (TVP-AR) model extends the classical AR model by allowing its autoregressive coefficients to drift over time, typically as a random walk. Cast as a state-space system, the model captures gradual structural change in the dynamics of a univariate time series without imposing a fixed break date. |
| ScholarGateНабор данных ↗ |
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