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.

See Also

Objects

Functions

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