Porovnat metody

	Strukturální vektorová autoregrese (SVAR)	Impulse Response Function (IRF)	Model vektorové autoregrese (VAR)
Obor	Ekonometrie	Ekonometrie	Ekonometrie
Rodina	Regression model	Regression model	Regression model
Rok vzniku	1980	2005	2005
Tvůrce	Christopher Sims	Helmut Lütkepohl	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Typ	Structural multivariate time-series model	Post-estimation diagnostic	Multivariate time-series model
Původní zdroj	Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1), 1–48.	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. ISBN: 978-3-540-40172-8	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Další názvy	Structural VAR, Identified VAR, SVAR Model, Yapısal Vektör Otoregresyon	IRF, Dynamic Multiplier, Shock Response Function, Etki Tepki Fonksiyonu	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Příbuzné	2	3	4
Shrnutí	Structural Vector Autoregression (SVAR) is a multivariate time-series model, developed by Christopher Sims (1980), that extends the reduced-form VAR by imposing economically motivated identifying restrictions on contemporaneous relationships among variables. SVAR enables researchers to isolate orthogonal structural shocks and trace their causal dynamic effects through impulse response functions and forecast error variance decompositions, making it a cornerstone of modern empirical macroeconomics.	The Impulse Response Function (IRF) traces the dynamic response of each variable in a Vector Autoregression (VAR) system to a one-unit shock in one of its error terms over a user-specified forecast horizon. It is the primary tool for structural analysis following VAR estimation and is widely used in macroeconomics, monetary economics, and finance to quantify how shocks propagate through interconnected time series systems.	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).