# Create and Adjust VAR Model Using Shorthand Syntax

This example shows how to create a three-dimensional VAR(4) model with unknown parameters using `varm` and the shorthand syntax. Then, this example shows how to adjust parameters of the created model using dot notation.

Create a VAR(4) model for a three-dimensional response series using the shorthand syntax.

```numseries = 3; p = 4; Mdl = varm(3,4)```
```Mdl = varm with properties: Description: "3-Dimensional VAR(4) Model" SeriesNames: "Y1" "Y2" "Y3" NumSeries: 3 P: 4 Constant: [3×1 vector of NaNs] AR: {3×3 matrices of NaNs} at lags [1 2 3 ... and 1 more] Trend: [3×1 vector of zeros] Beta: [3×0 matrix] Covariance: [3×3 matrix of NaNs] ```

`Mdl` is a `varm` model object. The properties of the model display at the command line. Observe that:

• The default value of some of the parameters are `NaN` values, which indicates their presence in the model. In particular, each lag from 1 through 4 has an unknown, nonzero autoregressive coefficient matrix.

• You created the model without using response data. That is, `Mdl` is agnostic about data.

Suppose that you want lags 1 and 4 in the model to be unknown and nonzero, but all other lags are zero. Using dot notation, remove the other lags from the model object by placing 3-by-3 matrices of zeros the corresponding cells.

```Mdl.AR{2} = zeros(3); Mdl.AR{3} = zeros(3)```
```Mdl = varm with properties: Description: "3-Dimensional VAR(4) Model" SeriesNames: "Y1" "Y2" "Y3" NumSeries: 3 P: 4 Constant: [3×1 vector of NaNs] AR: {3×3 matrices} at lags [1 4] Trend: [3×1 vector of zeros] Beta: [3×0 matrix] Covariance: [3×3 matrix of NaNs] ```

Observe that the model degree `p` is still `4`, but there are unknown, nonzero coefficients at lags 1 and 4 only.