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Filter disturbances through conditional variance model

```
[V,Y] =
filter(Mdl,Z)
```

```
[V,Y] =
filter(Mdl,Z,Name,Value)
```

`filter`

generalizes `simulate`

. Both function filter a series of disturbances to produce
output responses and conditional variances. However, `simulate`

autogenerates a series of mean-zero, unit-variance, independent and identically
distributed (iid) disturbances according to the distribution in the conditional variance
model object, `Mdl`

. In contrast, `filter`

lets you
directly specify your own disturbances.

[1] Bollerslev, T. “Generalized Autoregressive Conditional
Heteroskedasticity.” *Journal of Econometrics.* Vol. 31,
1986, pp. 307–327.

[2] Bollerslev, T. “A Conditionally Heteroskedastic Time Series Model for
Speculative Prices and Rates of Return.” *The Review of Economics and
Statistics*. Vol. 69, 1987, pp. 542–547.

[3] Box, G. E. P., G. M. Jenkins, and G. C. Reinsel. *Time Series
Analysis: Forecasting and Control*. 3rd ed. Englewood Cliffs, NJ:
Prentice Hall, 1994.

[4] Enders, W. *Applied Econometric Time Series*. Hoboken, NJ:
John Wiley & Sons, 1995.

[5] Engle, R. F. “Autoregressive Conditional Heteroskedasticity with
Estimates of the Variance of United Kingdom Inflation.”
*Econometrica*. Vol. 50, 1982, pp. 987–1007.

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