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Model ARMA s vremenski promjenjivim parametrima (TVP-ARMA)×Model prostora stanja (Kalmanov filtar)×
PodručjeEkonometrijaEkonometrija
ObiteljRegression modelRegression model
Godina nastanka19761990
TvoracCooley & Prescott (1976); further formalised by Harvey (1989)Harvey; Durbin & Koopman (state space treatment); Kalman filter
VrstaState-space time series modelState space time series model
Temeljni izvorCooley, T. F., & Prescott, E. C. (1976). Estimation in the presence of stochastic parameter variation. Econometrica, 44(1), 167–184. DOI ↗Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗
Drugi naziviTVP-ARMA, time-varying ARMA, state-space ARMA, locally stationary ARMAstate space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
Srodne34
SažetakThe time-varying parameter ARMA (TVP-ARMA) model extends the classical ARMA framework by allowing the autoregressive and moving-average coefficients to evolve over time. Embedded in a state-space representation and estimated via the Kalman filter, it captures structural change and parameter instability in time series without requiring an explicit breakpoint.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.
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ScholarGateUsporedite metode: Time-varying parameter ARMA model · State Space Model. Preuzeto 2026-06-17 s https://scholargate.app/hr/compare