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Strukturni model vremenskih serija (Osnovni strukturni model)×Model vektorske autoregresije (VAR)×
OblastEkonometrijaEkonometrija
PorodicaRegression modelRegression model
Godina nastanka19902005
TvoracAndrew C. HarveyLütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
TipState-space (unobserved components) time series modelMultivariate time-series model
Temeljni izvorHarvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. DOI ↗
Drugi naziviBSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM)vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Srodne44
SažetakThe Structural Time Series Model, in its Basic Structural Model (BSM) form, is Andrew Harvey's state-space approach that decomposes a series into separate stochastic trend, seasonal, cyclical, and irregular components. Developed in Harvey's 1990 treatment, it is prized for interpretability and component decomposition where ARIMA only delivers a black-box fit.Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005).
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ScholarGateUporedite metode: Structural Time Series Model · VAR Model. Preuzeto 2026-06-17 sa https://scholargate.app/sr/compare