predict,fEGarch_fit-method {fEGarch} | R Documentation |
Multistep and Rolling Point Forecasts
Description
Given a fitted model object from this package, conduct either multistep point forecasts of the conditional means and the conditional standard deviations into the future or rolling point forecasts of arbitrary step size of these quantities for a future test set.
Usage
## S4 method for signature 'fEGarch_fit'
predict(object, n.ahead = 10, trunc = NULL, ...)
## S4 method for signature 'fEGarch_fit'
predict_roll(object, step_size = 1, trunc = NULL, ...)
Arguments
object |
an object of class |
n.ahead |
a single numeric value indicating how far into the future the multistep point forecasts should be produced. |
trunc |
the truncation setting for the infinite-order
polynomial of long-memory model parts; the default uses
the setting from the fitted input object |
... |
currently without use and included for compatibility with generics. |
step_size |
the step size of the rolling point
forecasts; by default, |
Details
Use predict
to compute multistep point forecasts
(of the conditional mean and of the conditional standard deviation)
into the future. Let n
be the number of observations
of the data, to which a model was fitted. Then multistep
point forecasts are produced for all future time points
from n + 1
to n + n.ahead
.
Otherwise, if data was reserved for testing when creating
object
, e.g. through the use of the argument
n_test
in the corresponding functions, compute
rolling point forecasts over the test set using predict_roll
.
step_size
then determines the forecasting horizon for
the rolling point forecasts. For example, step_size = 1
, i.e.
the default, computes one-step rolling point forecasts, whereas for
example step_size = 10
computes ten-step rolling point
forecasts (starting at the tenth test time point).
Refitting of models during the rolling point forecast procedure is currently not yet available.
Value
Returns an object of class "fEGarch_forecast"
that has the
two slots @sigt
and @cmeans
representing the
forecasted conditional standard deviations and conditional
means, respectively. If the training series saved in object
has a special time series formatting like "zoo"
or "ts"
,
the formatting is adopted accordingly to these numeric
output series. A third slot @model
is the fitted model
input object object
.
Examples
window.zoo <- get("window.zoo", envir = asNamespace("zoo"))
rt <- window.zoo(SP500, end = "2002-12-31")
# Multistep forecasting (EGARCH with cond. normal distr.)
model1 <- fEGarch(spec = egarch_spec(), rt)
fcast1 <- predict(model1, n.ahead = 10)
fcast1
# Rolling one-step forecasts (EGARCH with cond. normal distr.)
model2 <- fEGarch(spec = egarch_spec(), rt, n_test = 250)
fcast2 <- predict_roll(model2, step_size = 1)
fcast2