Nonlinear time series model, base class definition
Description
Generic nonlinear autogregressive model class constructor.
Usage
1 2  nlar(str, coefficients, fitted.values, residuals, k, model,
model.specific = NULL, ...)

Arguments
str 
a 
coefficients,fitted.values,residuals,k,model,model.specific 
internal structure 
... 
further model specific fields 
Details
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:
 str

nlar.struct
object, encapsulating general infos such as time series length, embedding parameters, forecasting steps, model design matrix  coefficients
a named vector of model estimated/fixed coefficients
 k
total number of estimated coefficients
 fitted.values
model fitted values
 residuals

model residuals
 model
data frame containing the variables used
 model.specific
(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.
Value
An object of class nlar
. nlarmethods for a list of
available methods.
Author(s)
Antonio, Fabio Di Narzo
References
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).
See Also
availableModels
for currently available builtin
models. nlarmethods for available nlar
methods.