手法を比較

	予測誤差分散分解 (FEVD)	構造的ベクトル自己回帰 (SVAR)	ベクトル自己回帰（VAR）モデル
分野	計量経済学	計量経済学	計量経済学
系統	Regression model	Regression model	Regression model
提唱年	2005	1980	2005
提唱者	Helmut Lütkepohl	Christopher Sims	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
種類	Multivariate time series analysis tool	Structural multivariate time-series model	Multivariate time-series model
原典	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.
別名	Variance Decomposition, Error Variance Decomposition, VD Analysis, Varyans Ayrıştırması	Structural VAR, Identified VAR, SVAR Model, Yapısal Vektör Otoregresyon	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
関連	3	2	4
概要	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.	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).