# Simulate Responses Using filter

Illustrate the relationship between `simulate` and `filter` by estimating a 4-dimensional VAR(2) model of the four response series in Johansen's Danish data set. Simulate a single path of responses using the fitted model and the historical data as initial values, and then filter a random set of Gaussian disturbances through the estimated model using the same presample responses.

Load Johansen's Danish economic data.

`load Data_JDanish`

For details on the variables, enter `Description`.

Create a default 4-D VAR(2) model.

`Mdl = varm(4,2);`

Estimate the VAR(2) model using the entire data set.

`EstMdl = estimate(Mdl,Data);`

When reproducing the results of `simulate` and `filter`, it is important to take these actions.

• Set the same random number seed using `rng`.

• Specify the same presample response data using the `'Y0'` name-value pair argument.

Set the default random seed. Simulate 100 observations by passing the estimated model to `simulate`. Specify the entire data set as the presample.

```rng default YSim = simulate(EstMdl,100,'Y0',Data);```

`YSim` is a 100-by-4 matrix of simulated responses. Columns correspond to the columns of the variables in `Data`.

Set the default random seed. Simulate 4 series of 100 observations from the standard Gaussian distribution.

```rng default Z = randn(100,4);```

Filter the Gaussian values through the estimated model. Specify the entire data set as the presample.

`YFilter = filter(EstMdl,Z,'Y0',Data);`

`YFilter` is a 100-by-4 matrix of simulated responses. Columns correspond to the columns of the variables in the data `Data`. Before filtering the disturbances, `filter` scales `Z` by the lower triangular Cholesky factor of the model covariance in `EstMdl.Covariance`.

Compare the resulting responses between `filter` and `simulate`.

`(YSim - YFilter)'*(YSim - YFilter)`
```ans = 4×4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ```

The results are identical.