[Y,E,V]
= filter(Mdl,Z)
[Y,E,V]
= filter(Mdl,Z,Name,Value)
[
filters
disturbances, Y
,E
,V
]
= filter(Mdl
,Z
)Z
, to produce responses, innovations,
and conditional variances of a univariate ARIMA(p,D,q)
model.
[
filters
disturbances using additional options specified by one or more Y
,E
,V
]
= filter(Mdl
,Z
,Name,Value
)Name,Value
pair
arguments.

ARIMA model, as created by  

$${\epsilon}_{t}={\sigma}_{t}{z}_{t}.$$ As a column vector,

Specify optional commaseparated pairs of Name,Value
arguments.
Name
is the argument
name and Value
is the corresponding
value. Name
must appear
inside single quotes (' '
).
You can specify several name and value pair
arguments in any order as Name1,Value1,...,NameN,ValueN
.

Positive presample conditional variances that provide initial
values for the model. If
Default: 

Matrix of predictor data corresponding to a regression component
in the conditional mean model. The columns of Default: 

Presample response data, providing initial values for the model.
If
Default: 

Presample disturbances, providing initial values for the input
disturbance series,
Default: 
Notes







filter
generalizes simulate
.
That is, both filter a series of disturbances to produce output responses,
innovations, and conditional variances. However, simulate
autogenerates
a series of mean zero, unit variance, independent and identically
distributed (iid) disturbances according to the distribution in Mdl
.
In contrast, filter
lets you directly specify your
own disturbances.
[1] 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.
[2] Enders, W. Applied Econometric Time Series. Hoboken, NJ: John Wiley & Sons, 1995.
[3] Hamilton, J. D. Time Series Analysis. Princeton, NJ: Princeton University Press, 1994.