ScholarGate
Asistent

Uporedite metode

Pregledajte izabrane metode jednu pored druge; redovi koji se razlikuju su istaknuti.

Funkcija impulsnog odziva (IRF)×Декомпозиција варијансе грешке прогнозе (FEVD)×Strukturna vektorska autoregresija (SVAR)×
OblastEkonometrijaEkonometrijaEkonometrija
PorodicaRegression modelRegression modelRegression model
Godina nastanka200520051980
TvoracHelmut LütkepohlHelmut LütkepohlChristopher Sims
TipPost-estimation diagnosticMultivariate time series analysis toolStructural multivariate time-series model
Temeljni izvorLütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. ISBN: 978-3-540-40172-8Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. ISBN: 978-3-540-40172-8Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1), 1–48. DOI ↗
Drugi naziviIRF, Dynamic Multiplier, Shock Response Function, Etki Tepki FonksiyonuVariance Decomposition, Error Variance Decomposition, VD Analysis, Varyans AyrıştırmasıStructural VAR, Identified VAR, SVAR Model, Yapısal Vektör Otoregresyon
Srodne332
SažetakThe 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.
ScholarGateSkup podataka
  1. v1
  2. 1 Izvori
  3. PUBLISHED
  1. v1
  2. 1 Izvori
  3. PUBLISHED
  1. v1
  2. 1 Izvori
  3. PUBLISHED

Idi na pretragu Preuzmi slajdove

ScholarGateUporedite metode: Impulse Response Function · FEVD · SVAR. Preuzeto 2026-06-16 sa https://scholargate.app/sr/compare