Presample Data
As discussed in MMSE Forecasting, garchpred computes
MMSE forecasts. It does this by applying iterated conditional expectations
to the conditional mean and variance models one forecast period at
a time. Since these models are generally recursive in nature, they
often require presample data to initiate the iterative forecasting
process. This initial data plays the identical role that the presample
time series inputs PreInnovations, PreSigmas,
and PreSeries play in simulation and estimation.
For more information, see garchsim, garchfit, and garchinfer.
The time series array of asset returns, Series,
is a required input. The garchpred function takes
the initial returns needed to initiate forecasting of the conditional
mean directly from the last (most recent) rows of Series.
For example, consider a conditional mean model with an AR(R)
autoregressive component. In this case, garchpred takes
the R observations required to initiate the forecast
of each realization of Series directly from the
lastR rows of Series.
However, garchpred obtains initial innovations
and conditional standard deviations needed to initiate forecasting
of the conditional variance model from the input array Series via
the inverse filtering inference engine garchinfer.
For more information, see:
 | MMSE Forecasting | | Asymptotic Behavior |  |
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