What is Vector Autoregression?
Vector Autoregression was first presented in the 1960s by economist Clive Granger. Granger’s significant discoveries laid the framework for understanding and modeling the dynamic interactions that exist among economic factors. VAR models acquired significant momentum in econometrics and macroeconomics during the 1970s and 1980s.
Vector Autoregression (VAR) is a multivariate extension of autoregression (AR) models. While traditional AR models analyze the relationship between a single variable and its lagged values, VAR models consider multiple variables simultaneously. In a VAR model, each variable is regressed on its own lagged values as well as lagged values of other variables in the system.
Vector Autoregression (VAR) for Multivariate Time Series
Vector Autoregression (VAR) is a statistical tool used to investigate the dynamic relationships between multiple time series variables. Unlike univariate autoregressive models, which only forecast a single variable based on its previous values, VAR models investigate the interconnectivity of many variables. They accomplish this by modeling each variable as a function of not only its previous values but also of the past values of other variables in the system. In this article, we are going to explore the fundamentals of Vector Autoregression.
Table of Content
- What is Vector Autoregression?
- Mathematical Intuition of VAR Equations
- Assumptions underlying the VAR model
- Steps to Implement VAR on Time Series Model
- Step 1: Importing necessary libraries
- Step 2: Generate Sample Data
- Step 3: Function to plot time series
- Step 4: Function to check stationarity
- Step 5: VAR analysis
- Output Explanation
- Applications of VAR Models