Võrdle meetodeid

	Robust Vector Autoregression (Robust VAR) mudel	Kvantiiil-VAR	Struktuurne vektorautokorelatioonimudel (SVAR)
Valdkond	Ökonomeetria	Ökonomeetria	Ökonomeetria
Perekond	Regression model	Regression model	Regression model
Tekkeaasta	1980s–2000s	2006	1980
Looja	Extensions by Lutkepohl and others building on Sims (1980) VAR framework	Koenker and Xiao	Sims (1980); identification schemes by Blanchard & Quah (1989)
Tüüp	Multivariate time-series model with robust estimation	Distribution impulse response	Multivariate time series model
Algallikas	Goncalves, S., & Kilian, L. (2004). Bootstrapping autoregressions with conditional heteroskedasticity of unknown form. Journal of Econometrics, 123(1), 89-120.	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.
Rööpnimetused	robust VAR, outlier-robust VAR, heavy-tailed VAR, RVAR	Quantile-based impulse response	SVAR, structural vector autoregression, identified VAR, structural VAR model
Seotud	5	3	5
Kokkuvõte	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.	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.