Сравнение на методи

	Разлагане на дисперсията на прогнозната грешка (FEVD)	Модел на векторна авторегресия (VAR)
Област	Иконометрия	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	2005	2005
Създател	Helmut Lütkepohl	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Тип	Multivariate time series analysis tool	Multivariate time-series model
Основополагащ източник	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.
Други названия	Variance Decomposition, Error Variance Decomposition, VD Analysis, Varyans Ayrıştırması	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Свързани	3	4
Резюме	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.	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).