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	Robust Vektor Autoregression (Robust VAR) Modell	Panel VAR (Panel Vector Autoregression)	Vektorautoregressionsmodell (VAR)
Ämnesområde	Ekonometri	Ekonometri	Ekonometri
Familj	Regression model	Regression model	Regression model
Ursprungsår	1980s–2000s	1988	2005
Upphovsperson	Extensions by Lutkepohl and others building on Sims (1980) VAR framework	Holtz-Eakin, Newey & Rosen	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Typ	Multivariate time-series model with robust estimation	Panel vector autoregression	Multivariate time-series model
Ursprungskälla	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.	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Alias	robust VAR, outlier-robust VAR, heavy-tailed VAR, RVAR	PVAR, panel vector autoregression, Panel VAR (PVAR)	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Närliggande	5	3	4
Sammanfattning	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.	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).