Salīdzināt metodes

	Laika mainīgo parametru MA modelis	ARMA modelis (Autoregresīvs vidējais aritmētiskais)
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1990s	1970
Autors	Harvey, A. C.; Durbin, J. & Koopman, S. J.	George E. P. Box and Gwilym M. Jenkins
Tips	Time-varying state-space model	Time series model
Pirmavots	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.
Citi nosaukumi	TVP-MA model, state-space MA, Kalman filter MA, time-varying MA	ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q)
Saistītās	6	5
Kopsavilkums	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.