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Model vektorske autoregresije sa vremenski promenljivim parametrima (TVP-VAR)×Model stanja prostora (Kalmanov filter)×
OblastEkonometrijaEkonometrija
PorodicaRegression modelRegression model
Godina nastanka20051990
TvoracPrimiceri (2005); Cogley & Sargent (2001, 2005)Harvey; Durbin & Koopman (state space treatment); Kalman filter
TipMultivariate time-series model with drifting coefficientsState space time series model
Temeljni izvorPrimiceri, G. E. (2005). Time varying structural vector autoregressions and monetary policy. Review of Economic Studies, 72(3), 821-852. DOI ↗Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗
Drugi naziviTVP-VAR, time-varying VAR, TV-VAR, drifting-coefficient VARstate space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
Srodne64
SažetakThe Time-Varying Parameter VAR (TVP-VAR) model extends the standard vector autoregression by allowing the coefficients and error covariances to evolve gradually over time. Estimated via Bayesian methods and MCMC simulation, it captures how dynamic relationships between macroeconomic or financial variables shift across different economic regimes without requiring pre-specified break points.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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ScholarGateUporedite metode: Time-varying parameter VAR model · State Space Model. Preuzeto 2026-06-17 sa https://scholargate.app/sr/compare