So sánh phương pháp

	Mô hình Vector Tự hồi quy Mạnh mẽ (Robust VAR)	Mô hình Tự hồi quy Vector Bảng (Panel VAR)	VAR Quantile	Mô hình Tự hồi quy Vector (VAR)
Lĩnh vực	Kinh tế lượng	Kinh tế lượng	Kinh tế lượng	Kinh tế lượng
Họ	Regression model	Regression model	Regression model	Regression model
Năm ra đời	1980s–2000s	1988	2006	2005
Người khởi xướng	Extensions by Lutkepohl and others building on Sims (1980) VAR framework	Holtz-Eakin, Newey & Rosen	Koenker and Xiao	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Loại	Multivariate time-series model with robust estimation	Panel vector autoregression	Distribution impulse response	Multivariate time-series model
Công trình gốc	Goncalves, S., & Kilian, L. (2004). Bootstrapping autoregressions with conditional heteroskedasticity of unknown form. Journal of Econometrics, 123(1), 89-120.	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.	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Tên gọi khác	robust VAR, outlier-robust VAR, heavy-tailed VAR, RVAR	PVAR, panel vector autoregression, Panel VAR (PVAR)	Quantile-based impulse response	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Liên quan	5	3	3	4
Tóm tắt	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.	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.	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).