# VAR: Estimates a Vector Autoregressive model of order $p$.

## Description

Estimation of a Vector Autoregressive model (VAR) by computing OLS per equation.## Usage

VAR(y, p = 1, exogen = NULL)

## Arguments

y

Endogenous variable for the VAR model.

p

lag-order for the autoregressive model.

exogen

Exogenous variable for the VAR model.

## Value

- A matrix of coefficients from fitting the VAR model.

## Details

Estimates a VAR by OLS per equation. The model is of the following
form
$$\bold{y}_t = A_1 \bold{y}_{t-1} + \ldots + A_p \bold{y}_{t-p} +
CD_t + \bold{u}_t$$
where $\bold{y}_t$ is a $K \times 1$ vector of endogenous
variables and $u_t$ assigns a spherical disturbance term of the
same dimension. The coefficient matrices $A_1, \ldots, A_p$ are of
dimension $K \times K$.
No seasonality or trend term can be included in the model.## References

Wei, William W.S. (2006). *Time Series Analysis - Univariate and
Multivariate Methods*
Brockwell, P.J. and Davis, R.A. (1996). *Introduction to Time
Series and Forecasting , Second Edition, Springer, New York*## Examples

library(LPTime)
data(EyeTrack.sample)
head( VAR(y = EyeTrack.sample, p = 2))