Compară metode

	Autoregresia vectorială pe panel (Panel VAR)	VAR cu Cuantile	Modelul Vectorial de Autoregresie (VAR)
Domeniu	Econometrie	Econometrie	Econometrie
Familie	Regression model	Regression model	Regression model
Anul apariției	1988	2006	2005
Autorul original	Holtz-Eakin, Newey & Rosen	Koenker and Xiao	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Tip	Panel vector autoregression	Distribution impulse response	Multivariate time-series model
Sursa seminală	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.
Denumiri alternative	PVAR, panel vector autoregression, Panel VAR (PVAR)	Quantile-based impulse response	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Înrudite	3	3	4
Rezumat	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).