Сравнение на методи

	Time-varying parameter ARIMA model	Модел в състояние пространство (Калманов филтър)
Област	Иконометрия	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	1976–1989	1990
Създател	Cooley & Prescott (1976); Harvey (1989) state-space formulation	Harvey; Durbin & Koopman (state space treatment); Kalman filter
Тип	Time series model with evolving coefficients	State space time series model
Основополагащ източник	Harvey, A. C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 9780521405737	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
Други названия	TVP-ARIMA, time-varying ARIMA, adaptive ARIMA, state-space ARIMA	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
Свързани	3	4
Резюме	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.	A state space model is a general time series framework that describes a series through unobserved (latent) state variables linked by a measurement equation and a transition equation, with the states estimated in real time by the Kalman filter. Developed in the state space tradition of Harvey (1990) and Durbin & Koopman (2012), it nests ARIMA and exponential smoothing as special cases.