השוואת שיטות

	מודל סדרות עתיות מבני (מודל מבני בסיסי)	מודל אוטורגרסיה וקטורית (VAR)
תחום	אקונומטריקה	אקונומטריקה
משפחה	Regression model	Regression model
שנת המקור	1990	2005
הוגה השיטה	Andrew C. Harvey	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
סוג	State-space (unobserved components) time series model	Multivariate time-series model
מקור מכונן	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
כינויים	BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM)	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
קשורות	4	4
תקציר	The 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).