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Model robustne vektorske autoregresije (Robust VAR)×Kvantilni VAR (Quantile VAR)×
OblastEkonometrijaEkonometrija
PorodicaRegression modelRegression model
Godina nastanka1980s–2000s2006
TvoracExtensions by Lutkepohl and others building on Sims (1980) VAR frameworkKoenker and Xiao
TipMultivariate time-series model with robust estimationDistribution impulse response
Temeljni izvorGoncalves, S., & Kilian, L. (2004). Bootstrapping autoregressions with conditional heteroskedasticity of unknown form. Journal of Econometrics, 123(1), 89-120. DOI ↗Koenker, R., & Xiao, Z. (2006). Quantile autoregression. Journal of the American Statistical Association, 101(475), 980-990. DOI ↗
Drugi nazivirobust VAR, outlier-robust VAR, heavy-tailed VAR, RVARQuantile-based impulse response
Srodne53
SažetakThe 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.
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ScholarGateUporedite metode: Robust VAR model · Quantile VAR. Preuzeto 2026-06-17 sa https://scholargate.app/sr/compare