Bandingkan kaedah

	Pecahan Varians Ralat Ramalan (FEVD)	Fungsi Gerak Balas Impuls (IRF)	Structural Vector Autoregression (SVAR)	Model Regresi Autoruang (VAR)
Bidang	Ekonometrik	Ekonometrik	Ekonometrik	Ekonometrik
Keluarga	Regression model	Regression model	Regression model	Regression model
Tahun asal	2005	2005	1980	2005
Pengasas	Helmut Lütkepohl	Helmut Lütkepohl	Christopher Sims	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Jenis	Multivariate time series analysis tool	Post-estimation diagnostic	Structural multivariate time-series model	Multivariate time-series model
Sumber perintis	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	Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1), 1–48.	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Alias	Variance Decomposition, Error Variance Decomposition, VD Analysis, Varyans Ayrıştırması	IRF, Dynamic Multiplier, Shock Response Function, Etki Tepki Fonksiyonu	Structural VAR, Identified VAR, SVAR Model, Yapısal Vektör Otoregresyon	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Berkaitan	3	3	2	4
Ringkasan	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.	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.	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).