Porovnať metódy

	Kvantilový VAR	Štrukturálna vektorová autoregresia (SVAR)	Model vektorovej autoregresie (VAR)
Odbor	Ekonometria	Ekonometria	Ekonometria
Rodina	Regression model	Regression model	Regression model
Rok vzniku	2006	1980	2005
Tvorca	Koenker and Xiao	Sims (1980); identification schemes by Blanchard & Quah (1989)	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Typ	Distribution impulse response	Multivariate time series model	Multivariate time-series model
Pôvodný zdroj	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.	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Ďalšie názvy	Quantile-based impulse response	SVAR, structural vector autoregression, identified VAR, structural VAR model	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Príbuzné	3	5	4
Zhrnutie	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.	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).