Note: This page has been translated by MathWorks. Please click here

To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

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.

Was this topic helpful?