قارن الطرق

	تحليل تباين خطأ التنبؤ (FEVD)	دالة الاستجابة للصدمة (IRF)	نموذج الانحدار الذاتي المتجهي (VAR)
المجال	الاقتصاد القياسي	الاقتصاد القياسي	الاقتصاد القياسي
العائلة	Regression model	Regression model	Regression model
سنة النشأة	2005	2005	2005
صاحب الطريقة	Helmut Lütkepohl	Helmut Lütkepohl	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
النوع	Multivariate time series analysis tool	Post-estimation diagnostic	Multivariate time-series model
المصدر التأسيسي	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	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
الأسماء البديلة	Variance Decomposition, Error Variance Decomposition, VD Analysis, Varyans Ayrıştırması	IRF, Dynamic Multiplier, Shock Response Function, Etki Tepki Fonksiyonu	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
ذات صلة	3	3	4
الملخص	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.	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).