เปรียบเทียบวิธี

	ฟังก์ชันการตอบสนองต่อแรงกระตุ้น (Impulse Response Function - IRF)	การแยกส่วนความแปรปรวนของความคลาดเคลื่อนในการพยากรณ์ (FEVD)	แบบจำลองเวกเตอร์อัตถารวมเชิงโครงสร้าง (Structural Vector Autoregression: SVAR)
สาขาวิชา	เศรษฐมิติ	เศรษฐมิติ	เศรษฐมิติ
ตระกูล	Regression model	Regression model	Regression model
ปีกำเนิด	2005	2005	1980
ผู้ริเริ่ม	Helmut Lütkepohl	Helmut Lütkepohl	Christopher Sims
ประเภท	Post-estimation diagnostic	Multivariate time series analysis tool	Structural multivariate time-series model
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	IRF, Dynamic Multiplier, Shock Response Function, Etki Tepki Fonksiyonu	Variance Decomposition, Error Variance Decomposition, VD Analysis, Varyans Ayrıştırması	Structural VAR, Identified VAR, SVAR Model, Yapısal Vektör Otoregresyon
ที่เกี่ยวข้อง	3	3	2
สรุป	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.	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.