Filter disturbances through vector autoregression (VAR) model

`Y = filter(Mdl,Z)`

`Y = filter(Mdl,Z,Name,Value)`

```
[Y,E] =
filter(___)
```

uses additional
options specified by one or more name-value pair arguments. For example, you can specify exogenous predictor data or whether to
scale the disturbances by the lower triangular Cholesky factor of the model
innovations covariance matrix.`Y`

= filter(`Mdl`

,`Z`

,`Name,Value`

)

`filter`

computes`Y`

and`E`

using this process for each pagein`j`

`Z`

.If

`Scale`

is`true`

, then`E(:,:,`

=)`j`

`L*Z(:,:,`

, where)`j`

`L`

=`chol(Mdl.Covariance,'lower')`

. Otherwise,`E(:,:,`

=)`j`

`Z(:,:,`

. Set)`j`

*e*=_{t}`E(:,:,`

.)`j`

`Y(:,:,`

is)`j`

*y*in this system of equations._{t}$${y}_{t}={\widehat{\Phi}}^{-1}(L)\left(\widehat{c}+\widehat{\delta}t+{e}_{t}\right).$$

For variable definitions, see Definitions.

`filter`

generalizes`simulate`

. Both functions filter a disturbance series through a model to produce responses and innovations. However, whereas`simulate`

generates a series of mean-zero, unit-variance, independent Gaussian disturbances`Z`

to form innovations`E`

=`L*Z`

,`filter`

enables you to supply disturbances from any distribution.`filter`

uses this process to determine the time origin*t*_{0}of models that include linear time trends.If you do not specify

`Y0`

, then*t*_{0}= 0.Otherwise,

`filter`

sets*t*_{0}to`size(Y0,1)`

–`Mdl.P`

. Therefore, the times in the trend component are*t*=*t*_{0}+ 1,*t*_{0}+ 2,...,*t*_{0}+`numobs`

, where`numobs`

is the effective sample size (`size(Y,1)`

after`filter`

removes missing values). This convention is consistent with the default behavior of model estimation in which`estimate`

removes the first`Mdl.P`

responses, reducing the effective sample size. Although`filter`

explicitly uses the first`Mdl.P`

presample responses in`Y0`

to initialize the model, the total number of observations in`Y0`

and`Y`

(excluding missing values) determines*t*_{0}.

[1]
Hamilton, J. D. *Time Series Analysis*. Princeton, NJ: Princeton University Press, 1994.

[2]
Johansen, S. *Likelihood-Based Inference in Cointegrated Vector Autoregressive Models*. Oxford: Oxford University Press, 1995.

[3]
Juselius, K. *The Cointegrated VAR Model*. Oxford: Oxford University Press, 2006.

[4] Lütkepohl, H. *New Introduction to Multiple
Time Series Analysis*. Berlin: Springer, 2005.