השוואת שיטות

	מודל רגרסיה וקטורית אוטורגרסיבית חסין (Robust VAR)	אוטורגרסיה וקטורית של פאנל (Panel VAR)	אוטורגרסיה וקטורית מבנית (SVAR)
תחום	אקונומטריקה	אקונומטריקה	אקונומטריקה
משפחה	Regression model	Regression model	Regression model
שנת המקור	1980s–2000s	1988	1980
הוגה השיטה	Extensions by Lutkepohl and others building on Sims (1980) VAR framework	Holtz-Eakin, Newey & Rosen	Sims (1980); identification schemes by Blanchard & Quah (1989)
סוג	Multivariate time-series model with robust estimation	Panel vector autoregression	Multivariate time series model
מקור מכונן	Goncalves, S., & Kilian, L. (2004). Bootstrapping autoregressions with conditional heteroskedasticity of unknown form. Journal of Econometrics, 123(1), 89-120.	Holtz-Eakin, D., Newey, W. & Rosen, H. S. (1988). Estimating Vector Autoregressions with Panel Data. Econometrica, 56(6), 1371-1395.	Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79(4), 655-673.
כינויים	robust VAR, outlier-robust VAR, heavy-tailed VAR, RVAR	PVAR, panel vector autoregression, Panel VAR (PVAR)	SVAR, structural vector autoregression, identified VAR, structural VAR model
קשורות	5	3	5
תקציר	The Robust VAR model extends the classical Vector Autoregression framework by replacing ordinary least squares estimation with robust estimators — such as M-estimators or median-based methods — to reduce the influence of outliers, structural breaks, and heavy-tailed shocks common in financial and macroeconomic time series.	Panel VAR extends the vector autoregression model to panel data, modelling the dynamic interactions among several variables while controlling for cross-unit heterogeneity through fixed effects. It was introduced by Holtz-Eakin, Newey and Rosen in 1988 and produces impulse-response functions and variance decompositions at the panel level.	Structural VAR extends the reduced-form VAR by imposing economic theory-based restrictions that identify orthogonal structural shocks. This allows researchers to disentangle the causal effects of distinct economic disturbances — such as supply versus demand shocks — and trace their dynamic propagation through a system of variables via impulse response functions and forecast error variance decompositions.