Forecast conditional variances from conditional variance models
generates forecasts with additional options specified by one or more name-value pair
arguments. For example, you can initialize the model by specifying presample
conditional variances.V
= forecast(Mdl
,numperiods
,Y0
,Name,Value
)
If the conditional variance model Mdl
has an offset
(Mdl.Offset
), forecast
subtracts
it from the specified presample responses Y0
to obtain
presample innovations E0
. Subsequently,
forecast
uses E0
to initialize
the conditional variance model for forecasting.
forecast
sets the number of sample paths to forecast
numpaths
to the maximum number of columns among the
presample data sets Y0
and V0
. All
presample data sets must have either numpaths
> 1 columns or
one column. Otherwise, forecast
issues an error. For
example, if Y0
has five columns, representing five paths,
then V0
can either have five columns or one column. If
V0
has one column, then forecast
applies V0
to each path.
NaN
values in presample data sets indicate missing data.
forecast
removes missing data from the presample
data sets following this procedure:
forecast
horizontally concatenates the
specified presample data sets Y0
and
V0
such that the latest observations occur
simultaneously. The result can be a jagged array because the
presample data sets can have a different number of rows. In this
case, forecast
prepads variables with an
appropriate amount of zeros to form a matrix.
forecast
applies list-wise deletion to the
combined presample matrix by removing all rows containing at least
one NaN
.
forecast
extracts the processed presample
data sets from the result of step 2, and removes all prepadded
zeros.
List-wise deletion reduces the sample size and can create irregular time series.
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