قارن الطرق

	نموذج الانحدار الذاتي المتجه للبيانات المقطعية (Panel VAR)	الانحدار الذاتي الهيكلي المتجه (SVAR)	نموذج الانحدار الذاتي المتجهي (VAR)
المجال	الاقتصاد القياسي	الاقتصاد القياسي	الاقتصاد القياسي
العائلة	Regression model	Regression model	Regression model
سنة النشأة	1988	1980	2005
صاحب الطريقة	Holtz-Eakin, Newey & Rosen	Sims (1980); identification schemes by Blanchard & Quah (1989)	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
النوع	Panel vector autoregression	Multivariate time series model	Multivariate time-series model
المصدر التأسيسي	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.	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
الأسماء البديلة	PVAR, panel vector autoregression, Panel VAR (PVAR)	SVAR, structural vector autoregression, identified VAR, structural VAR model	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
ذات صلة	3	5	4
الملخص	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.	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).