Сравнение методов

	Модель скользящего среднего с изменяющимися во времени параметрами	Модель ARIMA с изменяющимися во времени параметрами (TVP-ARIMA)
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1990s	1976–1989
Автор метода	Harvey, A. C.; Durbin, J. & Koopman, S. J.	Cooley & Prescott (1976); Harvey (1989) state-space formulation
Тип	Time-varying state-space model	Time series model with evolving coefficients
Основополагающий источник	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 9780521321969	Harvey, A. C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 9780521405737
Другие названия	TVP-MA model, state-space MA, Kalman filter MA, time-varying MA	TVP-ARIMA, time-varying ARIMA, adaptive ARIMA, state-space ARIMA
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 ARIMA model extends the classical ARIMA framework by allowing its autoregressive and moving-average coefficients to evolve over time rather than remaining fixed. Cast in state-space form and estimated via the Kalman filter, it is designed for economic and financial time series whose dynamic structure shifts in response to structural breaks, policy changes, or regime transitions.