Salīdzināt metodes

	Variances sadalījums prognozes kļūdai (FEVD)	Impulse Response Function (IRF) (impulsa reakcijas funkcija)
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	2005	2005
Autors	Helmut Lütkepohl	Helmut Lütkepohl
Tips	Multivariate time series analysis tool	Post-estimation diagnostic
Pirmavots	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. ISBN: 978-3-540-40172-8
Citi nosaukumi	Variance Decomposition, Error Variance Decomposition, VD Analysis, Varyans Ayrıştırması	IRF, Dynamic Multiplier, Shock Response Function, Etki Tepki Fonksiyonu
Saistītās	3	3
Kopsavilkums	Forecast Error Variance Decomposition (FEVD) is a multivariate time series technique used within Vector Autoregression (VAR) frameworks to quantify what proportion of the forecast error variance of each variable is attributable to shocks from every other variable in the system. It is widely used by econometricians, macroeconomists, and financial researchers to assess the relative importance of different structural disturbances in driving short-run and long-run fluctuations across interconnected economic series.	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.