Constructor for the generic nlar model class. On a fitted object you
can call some generic methods. For a list of them, see
nlar-methods.
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 model-specific
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. nlar-methods for a list of
available methods.
Author(s)
Antonio, Fabio Di Narzo
References
Non-linear time series models in empirical finance, Philip Hans
Franses and Dick van Dijk, Cambridge: Cambridge University Press (2000).
Non-Linear Time Series: A Dynamical Systems Approach, Tong, H., Oxford:
Oxford University Press (1990).
See Also
availableModels for currently available built-in
models. nlar-methods for available nlar methods.