Applications of VAR Models
- Economic Forecasting: VAR models are widely used in economics to forecast the behavior of economic variables such as GDP, inflation, and interest rates.
- Causal Inference: By studying the impulse responses generated by VAR models, researchers can infer the causal impact of one variable on another. This is particularly valuable in policy evaluation.
- Financial Markets: VAR models can be used to predict financial indices, stocks and asset prices.
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