# filter

Filter disturbances through conditional variance model

## Description

## Examples

## Input Arguments

## Output Arguments

## Alternatives

`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.

## References

[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.

## See Also

### Objects

### Functions

**Introduced in R2012a**