How to estimate VAR models in EViews

Vector Autoregressive (VAR) models in Stata

Vector autoregression (VAR) is a powerful statistical model used to measure and analyze the relationships between multiple variables over time. It is a stochastic process model, meaning it models randomness in the form of data points that can be observed at different moments in time. VAR has been widely used in economics, finance, marketing, and other disciplines where there are multiple variables to consider when making decisions or predictions.

Formal representation of VAR models

Assumptions

1. Variables Yt and Xt are assumed Stationary. (Logs and Differences can be applied if required.)

2. ut and vt are white noise disturbances. Commonly called innovations or shock terms.

3. The coefficients in the main matrix (b11, b12, b21, b22) are estimated by OLS.

Engle and Granger Test

The engle and granger test will show the relationship between variables. If one variable causes the other one, it means that it helps to predict future values of the other variable. For example, the inflation rate can help us predict the future values of the interest rate (if inflation goes up, interest rates will eventually go up).

Lag Length criteria

Once you have estimated your VAR model, you should check for the lag length criteria. The lag length defines the number of lags that will be included as explanatory variables in the model, and has a substantial impact on its performance. The choice of an appropriate lag length is not always straightforward and requires careful consideration by the user.

Impulse Response Functions

An impulse response function will show how each endogenous variable evolves over time after it has been hit by an exogenous shock. For example, if there is a sudden surge in oil prices, this would be considered as an exogenous shock. The impulse response function would allow economists to observe the effect of this shock on various endogenous variables like inflation, interest rates and GDP over time. This information can then be used to make informed economic policy decisions that address short-term fluctuations while also considering long-term effects.

Variance Decomposition

Variance decomposition is an important tool for data analysis. It helps to measure the proportion of variation in a dependent variable explained by each of the independent variables. The variance decomposition is obtained after fitting the VAR model.

The VAR model estimates parameters in an equation system which describes the variation in the dependent variable as a function of its own lagged values and independent variables. By estimating and comparing these parameters, one can determine how much variation in the dependent variable is explained by each of its covariates. This technique is useful for identifying influential factors when conducting forecasting or regression analysis.

Consequently, variance decomposition provides an efficient way to measure the relative importance of different independent variables on dependent variables. Furthermore, it helps to identify relationships between two or more time series that may not be immediately apparent from visual inspection alone.

Variance Decomposition in Stata

We can see the effect of an unemployment shock in Unemployment and the Fed. Rate.

Also, we can see the effect of the fed rate on unemployment and the fed rate.

Learn how to estimate VAR models in EViews

Are you interested in learning how to estimate vector auto-regressive models in EViews? If so, this is the perfect package for you! With this package, you will get access to the same slides used in my comprehensive video tutorial, as well as the relevant dataset and EViews file. Not only is this a great learning tool, but it also allows you to replicate the results yourself and gain confidence in your own work.