Description Usage Arguments Details Value Author(s) References See Also
Generic nonlinear autogregressive model class constructor.
1 2  nlar(str, coefficients, fitted.values, residuals, k, model,
model.specific = NULL, ...)

str 
a 
coefficients, fitted.values, residuals, k, model, model.specific 
internal structure 
... 
further model specific fields 
Constructor for the generic nlar
model class. On a fitted object you
can call some generic methods. For a list of them, see
nlarmethods
.
An object of the nlar
class is a list of (at least) components:
nlar.struct
object, encapsulating
general infos such as time series length, embedding parameters, forecasting
steps, model design matrix
a named vector of model estimated/fixed coefficients
total number of estimated coefficients
model fitted values
model residuals
data frame containing the variables used
(optional) model specific additional infos
A nlar
object normally should also have a modelspecific
subclass (i.e., nlar
is a virtual class).
Each subclass should define at least a print
and, hopefully, a
oneStep
method, which is used by predict.nlar
to
iteratively extend ahead the time series.
An object of class nlar
. nlarmethods for a list of
available methods.
Antonio, Fabio Di Narzo
Nonlinear time series models in empirical finance, Philip Hans Franses and Dick van Dijk, Cambridge: Cambridge University Press (2000).
NonLinear Time Series: A Dynamical Systems Approach, Tong, H., Oxford: Oxford University Press (1990).
availableModels
for currently available builtin
models. nlarmethods for available nlar
methods.
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