Regression model
Vector Autoregression (VAR) Model
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).
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Sources
- Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. DOI: 10.1007/978-3-540-27752-1 ↗
Related methods
Referenced by
ARIMABayesian VARBEKK-GARCHCGE ModelCointegration TestDSGE ModelDynamic Factor ModelFAVARFEVDFourier SVAR ModelFourier Toda-Yamamoto CausalityGranger CausalityImpulse Response FunctionJohansen Cointegration TestMIDAS RegressionNonlinear ARIMA modelNonlinear NARDLNonlinear Toda-Yamamoto CausalityPanel VARRobust Granger CausalityRobust VAR modelStructural Time Series ModelSVARThreshold and Smooth-Transition VARTime-varying parameter Toda-Yamamoto causalityToda-Yamamoto CausalityTVP-VARVECM