مقایسهٔ روش‌ها

	مدل خودرگرسیونی برداری پانل (Panel VAR)	مدل خودرگرسیون برداری (VAR) ناویژه (Quantile VAR)	مدل خودرگرسیون برداری ساختاری (SVAR)
حوزه	اقتصادسنجی	اقتصادسنجی	اقتصادسنجی
خانواده	Regression model	Regression model	Regression model
سال پیدایش	1988	2006	1980
پدیدآور	Holtz-Eakin, Newey & Rosen	Koenker and Xiao	Sims (1980); identification schemes by Blanchard & Quah (1989)
نوع	Panel vector autoregression	Distribution impulse response	Multivariate time series model
منبع بنیادین	Holtz-Eakin, D., Newey, W. & Rosen, H. S. (1988). Estimating Vector Autoregressions with Panel Data. Econometrica, 56(6), 1371-1395.	Koenker, R., & Xiao, Z. (2006). Quantile autoregression. Journal of the American Statistical Association, 101(475), 980-990.	Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79(4), 655-673.
نام‌های دیگر	PVAR, panel vector autoregression, Panel VAR (PVAR)	Quantile-based impulse response	SVAR, structural vector autoregression, identified VAR, structural VAR model
مرتبط	3	3	5
خلاصه	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.	Quantile VAR estimates impulse responses of multivariate systems conditional on different quantiles of the distribution, revealing how shocks propagate heterogeneously across the conditional distribution. Introduced by Koenker and Xiao (2006) and applied to risk measurement by White et al. (2015), it reveals tail behavior and contagion effects invisible to mean-based VAR analysis. This is essential for risk management and understanding how crises propagate differently than normal times.	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.